CN113128896B - Intelligent workshop management system and method based on Internet of things - Google Patents

Intelligent workshop management system and method based on Internet of things Download PDF

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CN113128896B
CN113128896B CN202110476196.1A CN202110476196A CN113128896B CN 113128896 B CN113128896 B CN 113128896B CN 202110476196 A CN202110476196 A CN 202110476196A CN 113128896 B CN113128896 B CN 113128896B
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罗昱文
李杨
陈绪林
欧汉文
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Abstract

The invention relates to the technical field of workshop management, in particular to an intelligent workshop management system and method based on the Internet of things. The option generation module generates options according to daily reports of the user, and the options are displayed on a screen; the identity verification module is used for acquiring the real-time motion trail of the pupil of the user when the user selects the answer, and verifying the identity of the user according to the real-time motion trail of the pupil of the user and the answer selected by the user; the mental state detection module is used for acquiring pupil information of the user and detecting the mental state of the user according to the pupil information of the user; the entrance guard opening and closing control module controls the opening and closing of the entrance guard system. The system can strengthen the identity authentication of the user who needs to enter the workshop and detect the mental state of the user, thereby ensuring the safe operation of the workshop.

Description

基于物联网的智慧车间管理系统及方法Intelligent workshop management system and method based on Internet of things

技术领域technical field

本发明涉及车间管理技术领域，特别涉及基于物联网的智慧车间管理系统及方法。The invention relates to the technical field of workshop management, in particular to an intelligent workshop management system and method based on the Internet of Things.

背景技术Background technique

现有的生产中，如何保证车间的安全运行，尤其是防止无关人员进入车间，是生产过程中至关重要的一点。现有技术中对此采取的技术手段通常是通过划线标识，提示无关人员不要进入车间，但还是会有很多人闯入车间，为此，现有技术采用了门禁系统，通过设置密码、人脸识别等方式对希望进入车间的人的身份信息进行验证，但密码容易泄露，人脸识别系统漏洞大，单纯的通过这些管控方式仍然难以确认进入车间的人员的身份，无关人员仍然比较容易想到办法闯入车间。In the existing production, how to ensure the safe operation of the workshop, especially to prevent irrelevant personnel from entering the workshop, is a crucial point in the production process. The technical means for this in the prior art is usually to remind irrelevant personnel not to enter the workshop through the marking of lines, but there will still be many people breaking into the workshop. For this reason, the prior art adopts an access control system. Face recognition and other methods verify the identity information of people who want to enter the workshop, but passwords are easy to leak, and the face recognition system has large loopholes. It is still difficult to confirm the identity of people entering the workshop through these control methods alone, and it is still relatively easy for irrelevant personnel to think Way to break into the workshop.

特别是化工类车间，车间内物品的危险性高，对进入车间的人员更加需要严格的管控。不仅如此，在此类车间工作的人员，需要保证操作的谨慎，否则很小的一个失误可能导致巨大的危险。所以，对于进入车间的人员，不仅需要对其身份进行更加严格的认证，还需要对其精神状态进行检测，从而保证车间的安全运行。Especially in chemical workshops, the items in the workshop are highly dangerous, and people entering the workshop need to be strictly controlled. Not only that, but the personnel working in such workshops need to ensure that they operate cautiously, otherwise a small mistake may lead to great danger. Therefore, for those who enter the workshop, not only their identity needs to be more strictly authenticated, but also their mental state needs to be tested to ensure the safe operation of the workshop.

发明内容Contents of the invention

本发明提供了基于物联网的智慧车间管理系统，能够加强对需要进入车间的用户的身份认证，并对用户的精神状态进行检测，从而保证车间的安全运行。The invention provides a smart workshop management system based on the Internet of Things, which can strengthen the identity authentication of users who need to enter the workshop, and detect the mental state of the users, thereby ensuring the safe operation of the workshop.

本发明提供的基础方案：Basic scheme provided by the present invention:

基于物联网的智慧车间管理系统及方法，包括日报采集模块、存储模块、身份识别模块、日报获取模块、选项生成模块、身份验证模块、精神状态检测模块和门禁开闭控制模块：A smart workshop management system and method based on the Internet of Things, including a daily report collection module, a storage module, an identity recognition module, a daily report acquisition module, an option generation module, an identity verification module, a mental state detection module, and an access control module:

所述日报采集模块：用于采集车间中各用户的日报；The daily daily collection module: used to collect the daily daily reports of each user in the workshop;

所述存储模块：用于存储车间中各用户的身份信息；The storage module: used to store the identity information of each user in the workshop;

所述身份识别模块：用于根据存储模块中各用户的身份信息，识别用户的身份；The identity identification module: used to identify the identity of the user according to the identity information of each user in the storage module;

所述日报获取模块：用于根据用户的身份，获取用户的日报；The daily daily acquisition module: used to acquire the daily daily of the user according to the identity of the user;

所述选项生成模块：用于根据用户的日报生成选项，所述选项显示在屏幕上；The option generation module: used to generate options according to the user's daily report, and the options are displayed on the screen;

所述身份验证模块：用于用户选择答案时，获取用户的瞳孔的实时运动轨迹，并根据用户的瞳孔的实时运动轨迹和用户选择的答案，验证用户的身份；The identity verification module: when the user selects an answer, obtains the real-time movement track of the user's pupil, and verifies the identity of the user according to the real-time movement track of the user's pupil and the answer selected by the user;

所述精神状态检测模块：用于获取用户的瞳孔信息，并根据用户的瞳孔信息检测用户的精神状态；The mental state detection module: used to acquire the user's pupil information, and detect the user's mental state according to the user's pupil information;

所述门禁开闭控制模块：用于身份验证模块验证用户为本人，且用户的精神状态达到中性精神状态阈值时，控制门禁系统打开；身份验证模块验证用户不为本人，或用户的精神状态未达到中性精神状态阈值时，控制门禁系统关闭。The access control opening and closing control module: for the identity verification module to verify that the user is himself, and when the user's mental state reaches a neutral mental state threshold, control the access control system to open; the identity verification module verifies that the user is not himself, or the user's mental state When the neutral mental state threshold is not reached, the control access control system is turned off.

本发明的原理及优点在于：车间内每一个用户的日报因其每天的工作内容不同而不同，且用户每天的日报不一样，所以无关人员了解用户每天的日报内容的概率低，相对于现有技术中在门禁系统设置密码的方式，本方案中每个用户的日报不一样，且每个用户每天的日报不一样，而用户本人对于自己的日报内容、工作内容熟知，不清楚时也能够通过日报提交记录进行查询，故本方案根据不同用户的日报生成选项供相应用户选择，其安全性更高，对用户的身份认证更加严谨可靠，且如果是用户本人，但无法回忆起自己的日报，也说明其今天的状态不好。除此之外，本方案对于用户选择答案时的瞳孔进行实时运动轨迹的获取，防止无关人员使用用户的静态照片冒充车间的工作人员。除此之外，车间中的工作人员，尤其是化工类车间中的工作人员，需要保证良好的精神状态，否则容易在工作过程中出现失误，从而导致严重的后果，故本方案在加强对用户的身份认证的基础上，对用户的精神状态进行检测，确保用户为车间中的员工且精神状态可以保证工作的安全进行。The principle and advantages of the present invention are: the daily report of each user in the workshop is different because of the different daily work content, and the daily report of the user is different, so the probability of unrelated personnel knowing the content of the daily report of the user is low, compared with the existing In the way of setting passwords in the access control system in the technology, in this solution, each user's daily report is different, and each user's daily report is different, and the user himself is familiar with his daily report content and work content, and can pass through if he is not sure. Daily reports are submitted for query. Therefore, this solution provides corresponding users with options to generate daily reports according to different users. It is more secure and more rigorous and reliable for user identity authentication. If the user is himself, but cannot recall his own daily reports, It also shows that its condition today is not good. In addition, this solution acquires the real-time movement trajectory of the pupils when the user chooses the answer, so as to prevent unrelated personnel from using the user's static photos to pretend to be the staff of the workshop. In addition, the staff in the workshop, especially the staff in the chemical workshop, need to ensure a good mental state, otherwise it is easy to make mistakes in the work process, which will lead to serious consequences. On the basis of identity authentication, the user's mental state is detected to ensure that the user is an employee in the workshop and the mental state can ensure the safety of the work.

进一步，所述身份信息包括指纹，所述身份识别模块包括指纹采集模块、指纹比对模块和身份确认模块：Further, the identity information includes fingerprints, and the identity recognition module includes a fingerprint collection module, a fingerprint comparison module and an identity confirmation module:

所述指纹采集模块：用于采集用户的指纹；The fingerprint collection module: used to collect the user's fingerprint;

所述指纹比对模块：用于将采集的用户的指纹分别和存储模块中各用户的指纹进行比对，生成重合度；The fingerprint comparison module: used to compare the fingerprints of the collected users with the fingerprints of each user in the storage module to generate a coincidence degree;

所述身份确认模块：用于重合度高于重合度阈值时，确认用户的身份。The identity confirmation module: used to confirm the identity of the user when the coincidence degree is higher than the coincidence degree threshold.

有益效果：指纹是每个人特有的身份标识，具有唯一性和永久性，通过指纹进行用户身份的识别具有安全性，且指纹识别较密码锁更加方便，触摸即可。Beneficial effect: Fingerprint is the unique identity mark of each person, which is unique and permanent, and the identification of user identity through fingerprint is safe, and fingerprint identification is more convenient than combination lock, just touch it.

进一步，所述身份验证模块包括答案判定模块、选项位置生成模块、瞳孔追踪模块、轨迹比对模块和验证结果生成模块：Further, the identity verification module includes an answer determination module, an option position generation module, a pupil tracking module, a trajectory comparison module and a verification result generation module:

所述答案判定模块：用于判定用户选择的答案的准确率；The answer judging module: used to judge the accuracy of the answer selected by the user;

所述选项位置生成模块：用于生成选项在屏幕上的位置；The option position generation module: used to generate the position of the option on the screen;

所述瞳孔追踪模块：用于追踪用户选择答案时瞳孔的实时运动轨迹；The pupil tracking module: used to track the real-time trajectory of the pupil when the user selects an answer;

所述轨迹比对模块：用于将用户的瞳孔的实时运动轨迹与选项在屏幕上的位置进行比对，生成用户的瞳孔在选项所在位置的停留时间；The trajectory comparison module: for comparing the real-time movement trajectory of the user's pupil with the position of the option on the screen, and generating the dwell time of the user's pupil at the position of the option;

所述验证结果生成模块：用于用户选择的答案达到正确率阈值，且所述停留时间达到时间阈值时，生成用户为本人的验证结果；用户选择的答案未达到正确率阈值或所述停留时间未达到时间阈值时，生成用户不为本人的验证结果。The verification result generating module: used for generating a verification result that the user is himself when the answer selected by the user reaches the correct rate threshold and the dwell time reaches the time threshold; the answer selected by the user does not reach the correct rate threshold or the dwell time If the time threshold has not been reached, a verification result that the user is not the user is generated.

有益效果：用户选择答案时需要看选项，本方案通过选项位置生成模块生成选项在屏幕上的位置，并通过瞳孔追踪模块追踪用户选择答案时瞳孔的实时运动轨迹，轨迹比对模块将用户的瞳孔的实时运动轨迹与选项在屏幕上的位置进行比对，从而可以判断用户是否有看选项答题，从而防止无关人员利用车间的员工的动态图像冒充员工，以保证是员工本人在进行答题，采用本方案可以保证对用户的身份认证更加严谨可靠。Beneficial effect: the user needs to look at the options when choosing an answer. This solution uses the option position generation module to generate the position of the option on the screen, and uses the pupil tracking module to track the real-time movement trajectory of the pupil when the user chooses the answer. The trajectory comparison module compares the user's pupil Compare the real-time trajectory of the real-time motion track with the position of the option on the screen, so that it can be judged whether the user has read the option to answer the question, so as to prevent irrelevant personnel from using the dynamic image of the employee in the workshop to pretend to be an employee, so as to ensure that the employee himself is answering the question. The scheme can ensure that the user's identity authentication is more rigorous and reliable.

进一步，所述身份信息还包括用户睁眼时的面部特征和用户闭眼时的面部特征。Further, the identity information also includes facial features when the user's eyes are open and facial features when the user's eyes are closed.

有益效果：通过多种身份信息对用户的身份进行识别，从而提高身份识别的准确性。Beneficial effect: the identity of the user is identified through multiple identity information, thereby improving the accuracy of identity identification.

进一步，基于物联网的智慧车间管理方法，其特征在于：包括以下步骤：Further, the intelligent workshop management method based on the Internet of Things is characterized in that: comprising the following steps:

S1：采集车间中各用户的日报；S1: Collect the daily reports of each user in the workshop;

S2：存储车间中各用户的身份信息；S2: store the identity information of each user in the workshop;

S3：根据存储的各用户的身份信息，识别用户的身份；S3: Identify the identity of the user according to the stored identity information of each user;

S4：根据用户的身份，获取用户的日报；S4: Obtain the user's daily report according to the user's identity;

S5：根据用户的日报生成选项，所述选项显示在屏幕上；S5: Generate options according to the user's daily report, and display the options on the screen;

S6：用户选择答案时，获取用户的瞳孔的实时运动轨迹，并根据用户的瞳孔的实时运动轨迹和用户选择的答案，验证用户的身份；S6: When the user selects an answer, obtain the real-time movement track of the user's pupil, and verify the identity of the user according to the real-time movement track of the user's pupil and the answer selected by the user;

S7：获取用户的瞳孔信息，并根据用户的瞳孔信息检测用户的精神状态；S7: Obtain the user's pupil information, and detect the user's mental state according to the user's pupil information;

S8：验证用户为本人，且用户的精神状态达到中性精神状态阈值时，控制门禁系统打开；验证用户不为本人，或用户的精神状态未达到中性精神状态阈值时，控制门禁系统关闭。S8: When it is verified that the user is the person and the user's mental state reaches the neutral mental state threshold, control the access control system to open; when it is verified that the user is not the user, or the user's mental state does not reach the neutral mental state threshold, control the access control system to close.

有益效果：车间内每一个用户的日报因其每天的工作内容不同而不同，且用户每天的日报不一样，所以无关人员了解用户每天的日报内容的概率低，相对于现有技术中在门禁系统设置密码的方式，本方案中每个用户的日报不一样，且每个用户每天的日报不一样，而用户本人对于自己的日报内容、工作内容熟知，不清楚时也能够通过日报提交记录进行查询，故本方案根据不同用户的日报生成选项供相应用户选择，其安全性更高，对用户的身份认证更加严谨可靠，且如果是用户本人，但无法回忆起自己的日报，也说明其今天的状态不好。除此之外，本方案对于用户选择答案时的瞳孔进行实时运动轨迹的获取，防止无关人员使用用户的静态照片冒充车间的工作人员。除此之外，车间中的工作人员，尤其是化工类车间中的工作人员，需要保证良好的精神状态，否则容易在工作过程中出现失误，从而导致严重的后果，故本方案在加强对用户的身份认证的基础上，对用户的精神状态进行检测，确保用户为车间中的员工且精神状态可以保证工作的安全进行。Beneficial effects: the daily report of each user in the workshop is different because of the different daily work content, and the daily report of the user is different, so the probability of unrelated personnel knowing the content of the daily report of the user is low. Compared with the access control system in the prior art The way to set the password, in this solution, each user’s daily report is different, and each user’s daily report is different, and the user himself is familiar with his daily report content and work content, and can query through the daily report submission record if he is not sure , so this scheme is based on different user's daily report generation options for corresponding users to choose, which has higher security and more rigorous and reliable user identity authentication, and if the user himself cannot recall his daily report, it also shows that his bad mood. In addition, this solution acquires the real-time movement trajectory of the pupils when the user chooses the answer, so as to prevent unrelated personnel from using the user's static photos to pretend to be the staff of the workshop. In addition, the staff in the workshop, especially the staff in the chemical workshop, need to ensure a good mental state, otherwise it is easy to make mistakes in the work process, which will lead to serious consequences. On the basis of identity authentication, the user's mental state is detected to ensure that the user is an employee in the workshop and the mental state can ensure the safety of the work.

进一步，所述身份信息包括指纹，所述S3包括：Further, the identity information includes fingerprints, and the S3 includes:

S301：采集用户的指纹；S301: Collect the user's fingerprint;

S302：将采集的用户的指纹分别和存储的各用户的指纹进行比对，生成重合度；S302: Compare the fingerprints of the collected users with the stored fingerprints of each user to generate a coincidence degree;

S303：重合度高于重合度阈值时，确认用户的身份。S303: When the coincidence degree is higher than the coincidence degree threshold, confirm the identity of the user.

有益效果：指纹是每个人特有的身份标识，具有唯一性和永久性，通过指纹进行用户身份的识别具有安全性，且指纹识别较密码锁更加方便，触摸即可。Beneficial effect: Fingerprint is the unique identity mark of each person, which is unique and permanent, and the identification of user identity through fingerprint is safe, and fingerprint identification is more convenient than combination lock, just touch it.

进一步，所述S6包括：Further, the S6 includes:

S601：判定用户选择的答案的准确率；S601: Determine the accuracy rate of the answer selected by the user;

S602：生成选项在屏幕上的位置；S602: Generate the position of the option on the screen;

S603：追踪用户选择答案时瞳孔的实时运动轨迹；S603: Track the real-time movement trajectory of the pupil when the user selects the answer;

S604：将用户的瞳孔的实时运动轨迹与选项在屏幕上的位置进行比对，生成用户的瞳孔在选项所在位置的停留时间；S604: Comparing the real-time movement track of the user's pupil with the position of the option on the screen, generating the dwell time of the user's pupil at the position of the option;

S605：用于用户选择的答案达到正确率阈值，且所述停留时间达到时间阈值时，生成用户为本人的验证结果；用户选择的答案未达到正确率阈值或所述停留时间未达到时间阈值时，生成用户不为本人的验证结果。S605: When the answer selected by the user reaches the correct rate threshold and the stay time reaches the time threshold, generate a verification result that the user is himself; when the answer selected by the user does not reach the correct rate threshold or the stay time does not reach the time threshold , to generate a verification result that the user is not the user.

有益效果：用户选择答案时需要看选项，将用户的瞳孔的实时运动轨迹与选项在屏幕上的位置进行比对，从而可以判断用户是否有看选项答题，从而防止无关人员利用车间的员工的动态图像冒充员工，以保证是员工本人在进行答题，采用本方案可以保证对用户的身份认证更加严谨可靠。Beneficial effects: users need to look at the options when choosing an answer, and compare the real-time movement track of the user's pupils with the position of the options on the screen, so that it can be judged whether the user has looked at the options to answer the question, thereby preventing irrelevant personnel from using the dynamics of the employees in the workshop The image pretends to be an employee to ensure that the employee himself is answering the questions. Using this solution can ensure that the user's identity authentication is more rigorous and reliable.

进一步，所述身份信息还包括用户睁眼时的面部特征和用户闭眼时的面部特征。Further, the identity information also includes facial features when the user's eyes are open and facial features when the user's eyes are closed.

有益效果：通过多种身份信息对用户的身份进行识别，提高身份识别的准确性。Beneficial effect: the identity of the user is identified through multiple identity information, and the accuracy of identity identification is improved.

附图说明Description of drawings

图1为本发明实施例基于物联网的智慧车间管理系统的逻辑框图。Fig. 1 is a logical block diagram of an intelligent workshop management system based on the Internet of Things according to an embodiment of the present invention.

图2为本发明实施例基于物联网的智慧车间管理方法的流程图。FIG. 2 is a flow chart of an Internet of Things-based smart workshop management method according to an embodiment of the present invention.

具体实施方式Detailed ways

下面通过具体实施方式进一步详细说明：The following is further described in detail through specific implementation methods:

实施例1基本如附图1所示：Embodiment 1 is basically as shown in accompanying drawing 1:

基于物联网的智慧车间管理系统，包括日报采集模块、存储模块、身份识别模块、日报获取模块、选项生成模块、身份验证模块、精神状态检测模块和门禁开闭控制模块。日报采集模块用于采集车间中各用户的日报；存储模块用于存储车间中各用户的身份信息，本实施例中，所述身份信息为指纹，在本申请的其他实施例中，还可以为用户睁眼时的面部特征和用户闭眼时的面部特征。The intelligent workshop management system based on the Internet of Things includes a daily report collection module, a storage module, an identification module, a daily report acquisition module, an option generation module, an identity verification module, a mental state detection module and an access control module. The daily report acquisition module is used to collect the daily reports of each user in the workshop; the storage module is used to store the identity information of each user in the workshop. In this embodiment, the identity information is a fingerprint. In other embodiments of the application, it can also be The facial features of the user with their eyes open and the facial features of the user with their eyes closed.

身份识别模块用于根据存储模块中各用户的身份信息，识别用户的身份。身份识别模块包括指纹采集模块、指纹比对模块和身份确认模块。指纹采集模块用于采集用户的指纹；指纹比对模块将采集的用户的指纹分别和存储模块中各用户的指纹进行比对，生成重合度；身份确认模块用于重合度高于重合度阈值时，确认用户的身份。本实施例中，重合度包括0％-100％，重合度阈值为90％。The identity identification module is used to identify the identity of the user according to the identity information of each user in the storage module. The identification module includes a fingerprint collection module, a fingerprint comparison module and an identity confirmation module. The fingerprint collection module is used to collect the user's fingerprint; the fingerprint comparison module compares the collected user's fingerprint with the fingerprints of each user in the storage module to generate a coincidence degree; the identity confirmation module is used when the coincidence degree is higher than the coincidence degree threshold , confirming the user's identity. In this embodiment, the coincidence degree includes 0%-100%, and the coincidence degree threshold is 90%.

日报获取模块根据身份识别模块识别出的用户的身份，获取用户的日报。本实施例中，获取用户上一工作日的日报。选项生成模块根据用户上一工作日的日报生成选项，所述选项显示在屏幕上，所述日报的内容包括工作内容、工作计划和工作总结。The daily report obtaining module obtains the user's daily report according to the identity of the user identified by the identity recognition module. In this embodiment, the user's daily report of the previous working day is obtained. The option generation module generates options according to the user's daily report of the previous working day, and the options are displayed on the screen. The content of the daily report includes work content, work plan and work summary.

本实施例中，选项生成模块共生成三个题目，分别为有关用户上一工作日的日报中工作内容、工作计划和工作总结的题目。具体生成方式如下：提取工作计划中的一个关键词，再随机生成三个关键词，分别作为选择题的四个选项，供用户选择上一个工作日的工作计划，在进行用户的身份确认的同时，可以提醒用户今日需要进行的工作。生成有关工作内容、工作总结的题目的方式与生成有关工作计划的题目的方式相同，在此不再赘述。In this embodiment, the option generation module generates three topics in total, which are the topics related to the work content, work plan and work summary in the user's daily daily report of the previous working day. The specific generation method is as follows: extract a keyword in the work plan, and then randomly generate three keywords, which are respectively used as four options for the multiple-choice question, for the user to choose the work plan of the previous working day, while confirming the user's identity , which can remind the user of the work that needs to be done today. The method of generating the questions related to the work content and work summary is the same as the method of generating the questions related to the work plan, and will not be repeated here.

身份验证模块用于用户选择答案时，获取用户的瞳孔的实时运动轨迹，并根据用户的瞳孔的实时运动轨迹和用户选择的答案，验证用户的身份。身份验证模块包括答案判定模块、选项位置生成模块、瞳孔追踪模块、轨迹比对模块和验证结果生成模块。The identity verification module is used to obtain the real-time movement track of the user's pupil when the user selects an answer, and verify the identity of the user according to the real-time movement track of the user's pupil and the answer selected by the user. The identity verification module includes an answer determination module, an option location generation module, a pupil tracking module, a trajectory comparison module and a verification result generation module.

答案判定模块用于判定用户选择的答案的准确率；选项位置生成模块用于生成选项在屏幕上的位置，本实施例中，选项共占据屏幕四分之一的面积，选项在屏幕上的位置分别为左上角、左下角、右上角和右下角；瞳孔追踪模块用于追踪用户选择答案时瞳孔的实时运动轨迹；轨迹比对模块用于将用户的瞳孔的实时运动轨迹与选项在屏幕上的位置进行比对，生成用户的瞳孔在选项所在位置的停留时间；验证结果生成模块用于用户选择的答案达到正确率阈值，且所述停留时间达到时间阈值时，生成用户为本人的验证结果；用户选择的答案未达到正确率阈值或所述停留时间未达到时间阈值时，生成用户不为本人的验证结果。本实施例中，正确率阈值2/3，时间阈值为5秒。The answer determination module is used to determine the accuracy of the answer selected by the user; the option position generation module is used to generate the position of the option on the screen. In this embodiment, the options occupy a quarter of the screen area, and the position of the option on the screen They are the upper left corner, the lower left corner, the upper right corner and the lower right corner; the pupil tracking module is used to track the real-time movement trajectory of the pupil when the user chooses an answer; the trajectory comparison module is used to compare the real-time movement trajectory of the user's pupil with the options on the screen. The positions are compared to generate the residence time of the user's pupils at the location of the option; the verification result generation module is used for the answer selected by the user to reach the correct rate threshold, and when the residence time reaches the time threshold, the verification result that the user is himself is generated; When the answer selected by the user does not reach the correct rate threshold or the stay time does not reach the time threshold, a verification result that the user is not the user is generated. In this embodiment, the accuracy threshold is 2/3, and the time threshold is 5 seconds.

精神状态检测模块用于获取用户的瞳孔信息，并根据用户的瞳孔信息检测用户的精神状态。本实施例中，瞳孔信息为眼睑盖住瞳孔的面积和眨眼频率，精神状态检测模块通过人工智能的方式，将用户的眼睑盖住瞳孔的面积和眨眼频率作为输入层的输入，用户的精神状态作为输出层的输出。The mental state detection module is used to obtain the user's pupil information, and detect the user's mental state according to the user's pupil information. In this embodiment, the pupil information is the area of the pupil covered by the eyelid and the blink frequency. The mental state detection module uses artificial intelligence to use the area of the user's eyelid covering the pupil and the blink frequency as the input of the input layer. The user's mental state as the output of the output layer.

具体的，首先构建一个三层的BP神经网络模型，包括输入层、隐层和输出层，本实施例中，输入层有2个节点，输出层的输出有1个节点，本实施例中，输出的用户的精神状态由差至好包括0-10，用户的中性精神状态阈值为6；针对于隐层，本实施例使用了以下公式来确定隐层节点的数量：其中l为隐层的节点数，n为输入层的节点数，m为输出层的节点数，a为1至10之间的一个数，本实施例中取为6，因此隐层共有8个节点。BP神经网络通常采用Sigmoid可微函数和线性函数作为网络的激励函数。本实施例选择S型正切函数tansig作为隐层神经元的激励函数。预测模型选取S型对数函数tansig作为输出层神经元的激励函数。Specifically, a three-layer BP neural network model is first constructed, including an input layer, a hidden layer and an output layer. In this embodiment, the input layer has 2 nodes, and the output of the output layer has 1 node. In this embodiment, The output user's mental state includes 0-10 from poor to good, and the user's neutral mental state threshold is 6; for the hidden layer, the present embodiment uses the following formula to determine the number of hidden layer nodes: Among them, l is the number of nodes in the hidden layer, n is the number of nodes in the input layer, m is the number of nodes in the output layer, and a is a number between 1 and 10, which is 6 in this embodiment, so there are 8 hidden layers node. BP neural network usually adopts Sigmoid differentiable function and linear function as the activation function of the network. In this embodiment, the sigmoid tangent function tansig is selected as the activation function of the neurons in the hidden layer. The prediction model selects the S-type logarithmic function tansig as the excitation function of the neurons in the output layer.

门禁开闭控制模块用于身份验证模块验证用户为本人，且用户的精神状态达到中性精神状态阈值时，控制门禁系统打开；身份验证模块验证用户不为本人，或用户的精神状态未达到中性精神状态阈值时，控制门禁系统关闭。The access control opening and closing control module is used for the identity verification module to verify that the user is himself, and when the user's mental state reaches the neutral mental state threshold, control the opening of the access control system; the identity verification module verifies that the user is not himself, or the user's mental state has not reached the neutral state When the sexual mental state threshold is exceeded, the control access control system is closed.

实施例2基本如附图2所示：Embodiment 2 is basically as shown in accompanying drawing 2:

基于物联网的智慧车间管理方法，包括以下步骤：The intelligent workshop management method based on the Internet of Things includes the following steps:

S1：采集车间中各用户的日报；S1: Collect the daily reports of each user in the workshop;

S2：存储车间中各用户的身份信息；本实施例中，所述身份信息为指纹，在本申请的其他实施例中，还可以为用户睁眼时的面部特征和用户闭眼时的面部特征。S2: Store the identity information of each user in the workshop; in this embodiment, the identity information is a fingerprint, and in other embodiments of the application, it can also be the facial features of the user when the eyes are open and the facial features of the user when the eyes are closed .

S3：根据存储的各用户的身份信息，识别用户的身份；S3: Identify the identity of the user according to the stored identity information of each user;

S4：根据用户的身份，获取用户的日报；S4: Obtain the user's daily report according to the user's identity;

S5：根据用户的日报生成选项，所述选项显示在屏幕上；S5: Generate options according to the user's daily report, and display the options on the screen;

S6：用户选择答案时，获取用户的瞳孔的实时运动轨迹，并根据用户的瞳孔的实时运动轨迹和用户选择的答案，验证用户的身份；S6: When the user selects an answer, obtain the real-time movement track of the user's pupil, and verify the identity of the user according to the real-time movement track of the user's pupil and the answer selected by the user;

S7：获取用户的瞳孔信息，并根据用户的瞳孔信息检测用户的精神状态；本实施例中，瞳孔信息为眼睑盖住瞳孔的面积和眨眼频率；S7: Obtain the user's pupil information, and detect the user's mental state according to the user's pupil information; in this embodiment, the pupil information is the area of the pupil covered by the eyelid and the blink frequency;

S8：验证用户为本人，且用户的精神状态达到中性精神状态阈值时，控制门禁系统打开；验证用户不为本人，或用户的精神状态未达到中性精神状态阈值时，控制门禁系统关闭。S8: When it is verified that the user is the person and the user's mental state reaches the neutral mental state threshold, control the access control system to open; when it is verified that the user is not the user, or the user's mental state does not reach the neutral mental state threshold, control the access control system to close.

其中，S3包括S301：采集用户的指纹；Wherein, S3 includes S301: collecting the user's fingerprint;

S302：将采集的用户的指纹分别和存储的各用户的指纹进行比对，生成重合度；本实施例中，重合度包括0％-100％；S302: Compare the collected fingerprints of the users with the stored fingerprints of each user to generate a coincidence degree; in this embodiment, the coincidence degree includes 0%-100%;

S303：重合度高于重合度阈值时，确认用户的身份；本实施例中，重合度阈值为90％。S303: When the coincidence degree is higher than the coincidence degree threshold, confirm the identity of the user; in this embodiment, the coincidence degree threshold is 90%.

具体的，S4根据识别出的用户的身份，获取用户的日报。本实施例中，获取用户上一工作日的日报，根据用户上一工作日的日报生成选项，所述选项显示在屏幕上，所述日报的内容包括工作内容、工作计划和工作总结。Specifically, S4 obtains the user's daily report according to the identified identity of the user. In this embodiment, the daily report of the user's last working day is obtained, and options are generated according to the daily report of the user's last working day, and the options are displayed on the screen. The content of the daily report includes work content, work plan and work summary.

本实施例中，共生成三个题目，分别为有关用户上一工作日的日报中工作内容、工作计划和工作总结的题目。具体生成方式如下：提取工作计划中的一个关键词，再随机生成三个关键词，分别作为选择题的四个选项，供用户选择上一个工作日的工作计划，在进行用户的身份确认的同时，可以提醒用户今日需要进行的工作。生成有关工作内容、工作总结的题目的方式与生成有关工作计划的题目的方式相同，在此不再赘述。In this embodiment, a total of three topics are generated, which are topics related to the work content, work plan and work summary in the user's daily daily report of the previous working day. The specific generation method is as follows: extract a keyword in the work plan, and then randomly generate three keywords, which are respectively used as four options for the multiple-choice question, for the user to choose the work plan of the previous working day, while confirming the user's identity , which can remind the user of the work that needs to be done today. The method of generating the questions related to the work content and work summary is the same as the method of generating the questions related to the work plan, and will not be repeated here.

其中，S6包括：Among them, S6 includes:

S601：判定用户选择的答案的准确率；S601: Determine the accuracy rate of the answer selected by the user;

S602：生成选项在屏幕上的位置；本实施例中，选项共占据屏幕四分之一的面积，选项在屏幕上的位置分别为左上角、左下角、右上角和右下角；S602: Generate the positions of the options on the screen; in this embodiment, the options occupy a quarter of the screen, and the positions of the options on the screen are the upper left corner, the lower left corner, the upper right corner, and the lower right corner;

S603：追踪用户选择答案时瞳孔的实时运动轨迹；S603: Track the real-time movement trajectory of the pupil when the user selects the answer;

S604：将用户的瞳孔的实时运动轨迹与选项在屏幕上的位置进行比对，生成用户的瞳孔在选项所在位置的停留时间；S604: Comparing the real-time movement track of the user's pupil with the position of the option on the screen, generating the dwell time of the user's pupil at the position of the option;

S605：用于用户选择的答案达到正确率阈值，且所述停留时间达到时间阈值时，生成用户为本人的验证结果；用户选择的答案未达到正确率阈值或所述停留时间未达到时间阈值时，生成用户不为本人的验证结果。本实施例中，正确率阈值2/3，时间阈值为5秒。S605: When the answer selected by the user reaches the correct rate threshold and the stay time reaches the time threshold, generate a verification result that the user is himself; when the answer selected by the user does not reach the correct rate threshold or the stay time does not reach the time threshold , to generate a verification result that the user is not the user. In this embodiment, the accuracy threshold is 2/3, and the time threshold is 5 seconds.

本实施例中，S7通过人工智能的方式，将用户的眼睑盖住瞳孔的面积和眨眼频率作为输入层的输入，用户的精神状态作为输出层的输出。具体的，首先构建一个三层的BP神经网络模型，包括输入层、隐层和输出层，本实施例中，输入层有2个节点，输出层的输出有1个节点，本实施例中，输出的用户的精神状态由差至好包括0-10，用户的中性精神状态阈值为6；针对于隐层，本实施例使用了以下公式来确定隐层节点的数量：其中l为隐层的节点数，n为输入层的节点数，m为输出层的节点数，a为1至10之间的一个数，本实施例中取为6，因此隐层共有8个节点。BP神经网络通常采用Sigmoid可微函数和线性函数作为网络的激励函数。本实施例选择S型正切函数tansig作为隐层神经元的激励函数。预测模型选取S型对数函数tansig作为输出层神经元的激励函数。In this embodiment, S7 uses artificial intelligence to take the area of the user's eyelids covering the pupil and the blink frequency as the input of the input layer, and the user's mental state as the output of the output layer. Specifically, a three-layer BP neural network model is first constructed, including an input layer, a hidden layer and an output layer. In this embodiment, the input layer has 2 nodes, and the output of the output layer has 1 node. In this embodiment, The output user's mental state includes 0-10 from poor to good, and the user's neutral mental state threshold is 6; for the hidden layer, the present embodiment uses the following formula to determine the number of hidden layer nodes: Among them, l is the number of nodes in the hidden layer, n is the number of nodes in the input layer, m is the number of nodes in the output layer, and a is a number between 1 and 10, which is 6 in this embodiment, so there are 8 hidden layers node. BP neural network usually adopts Sigmoid differentiable function and linear function as the activation function of the network. In this embodiment, the sigmoid tangent function tansig is selected as the activation function of the neurons in the hidden layer. The prediction model selects the S-type logarithmic function tansig as the excitation function of the neurons in the output layer.

以上的仅是本发明的实施例，方案中公知的具体结构及特性等常识在此未作过多描述，所属领域普通技术人员知晓申请日或者优先权日之前发明所属技术领域所有的普通技术知识，能够获知该领域中所有的现有技术，并且具有应用该日期之前常规实验手段的能力，所属领域普通技术人员可以在本申请给出的启示下，结合自身能力完善并实施本方案，一些典型的公知结构或者公知方法不应当成为所属领域普通技术人员实施本申请的障碍。应当指出，对于本领域的技术人员来说，在不脱离本发明结构的前提下，还可以作出若干变形和改进，这些也应该视为本发明的保护范围，这些都不会影响本发明实施的效果和专利的实用性。本申请要求的保护范围应当以其权利要求的内容为准，说明书中的具体实施方式等记载可以用于解释权利要求的内容。The above is only an embodiment of the present invention, and the common knowledge such as the specific structure and characteristics known in the scheme is not described here too much. Those of ordinary skill in the art know all the common technical knowledge in the technical field to which the invention belongs before the filing date or the priority date , can know all the existing technologies in this field, and have the ability to apply conventional experimental methods before this date. Under the inspiration given by this application, those skilled in the art can improve and implement this plan in combination with their own abilities. Some typical The known structures or known methods should not be an obstacle for those of ordinary skill in the art to implement the present application. It should be pointed out that for those skilled in the art, under the premise of not departing from the structure of the present invention, some modifications and improvements can also be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation of the present invention. Effects and utility of patents. The scope of protection required by this application shall be based on the content of the claims, and the specific implementation methods and other records in the specification may be used to interpret the content of the claims.

Claims (8)

1. Intelligent workshop management system based on Internet of things, and is characterized in that: the intelligent daily newspaper acquisition system comprises a daily newspaper acquisition module, a storage module, an identity recognition module, a daily newspaper acquisition module, an option generation module, an identity verification module, a mental state detection module and an access control module:
the daily report acquisition module is used for: the daily newspaper collecting device is used for collecting daily newspaper of each user in the workshop, wherein the daily newspaper of each user in the workshop is different due to different daily working contents, and the daily newspaper of the users is different;
the storage module: the system is used for storing the identity information of each user in the workshop;
the identity recognition module is as follows: the system is used for identifying the identity of the user according to the identity information of each user in the storage module;
the daily report acquisition module is used for: the method comprises the steps of acquiring daily reports of a user according to the identity of the user;
the option generation module: the method comprises the steps of generating options according to daily reports of a user, wherein the options are displayed on a screen;
the identity verification module: when the user selects the answer, acquiring a real-time motion trail of the pupil of the user, and verifying the identity of the user according to the real-time motion trail of the pupil of the user and the answer selected by the user;
the mental state detection module: the pupil information detection device is used for acquiring pupil information of a user and detecting the mental state of the user according to the pupil information of the user;
the entrance guard opening and closing control module is used for: the authentication module is used for authenticating that the user is himself, and controlling the access control system to be opened when the mental state of the user reaches a neutral mental state threshold value; and the identity verification module verifies that the user is not the person, or the access control system is controlled to be closed when the mental state of the user does not reach the threshold value of the neutral mental state.
2. The intelligent workshop management system based on the internet of things according to claim 1, wherein: the identity information comprises fingerprints, and the identity recognition module comprises a fingerprint acquisition module, a fingerprint comparison module and an identity confirmation module:
the fingerprint acquisition module is as follows: the fingerprint acquisition device is used for acquiring fingerprints of a user;
the fingerprint comparison module: the fingerprint acquisition module is used for comparing the acquired fingerprints of the users with the fingerprints of the users in the storage module respectively to generate the coincidence degree;
the identity confirmation module: and when the contact ratio is higher than the contact ratio threshold value, confirming the identity of the user.
3. The intelligent workshop management system based on the internet of things according to claim 1, wherein: the identity verification module comprises an answer judgment module, an option position generation module, a pupil tracking module, a track comparison module and a verification result generation module:
the answer determination module: the method comprises the steps of determining the accuracy of answers selected by a user;
the option position generation module: for generating a position of the option on the screen;
the pupil tracking module: the real-time motion trail of the pupil is used for tracking when the user selects an answer;
the track comparison module: the real-time motion trail of the pupil of the user is compared with the position of the option on the screen, and the residence time of the pupil of the user at the position of the option is generated;
the verification result generation module: when the answer selected by the user reaches the accuracy threshold and the residence time reaches the time threshold, generating a verification result of the user as the user; and when the answer selected by the user does not reach the correct rate threshold or the residence time does not reach the time threshold, generating a verification result that the user is not the user.
4. The intelligent workshop management system based on the internet of things according to claim 1, wherein: the identity information also includes facial features of the user when the eyes are open and facial features of the user when the eyes are closed.
5. The intelligent workshop management method based on the Internet of things is characterized by comprising the following steps of: the method comprises the following steps:
s1: collecting daily reports of all users in a workshop;
s2: storing identity information of each user in a workshop;
s3: identifying the identity of the user according to the stored identity information of each user;
s4: acquiring daily reports of the user according to the identity of the user;
s5: generating options according to daily reports of the user, wherein the options are displayed on a screen;
s6: when the user selects an answer, acquiring a real-time motion trail of the pupil of the user, and verifying the identity of the user according to the real-time motion trail of the pupil of the user and the answer selected by the user;
s7: acquiring pupil information of a user, and detecting the mental state of the user according to the pupil information of the user;
s8: verifying that the user is himself, and controlling the access control system to be opened when the mental state of the user reaches a neutral mental state threshold value; and (3) when the user is verified to be not the person or the mental state of the user does not reach the threshold value of the neutral mental state, controlling the access control system to be closed.
6. The intelligent workshop management method based on the internet of things according to claim 5, wherein: the identity information includes a fingerprint, and the S3 includes:
s301: collecting fingerprints of users;
s302: comparing the collected fingerprints of the users with stored fingerprints of the users respectively to generate the coincidence degree;
s303: and when the overlap ratio is higher than the overlap ratio threshold value, confirming the identity of the user.
7. The intelligent workshop management method based on the internet of things according to claim 5, wherein: the step S6 comprises the following steps:
s601: judging the accuracy of the answer selected by the user;
s602: generating the position of the option on the screen;
s603: tracking the real-time motion trail of the pupil when the user selects the answer;
s604: comparing the real-time motion trail of the pupil of the user with the position of the option on the screen to generate the residence time of the pupil of the user at the position of the option;
s605: when the answer selected by the user reaches the accuracy threshold and the residence time reaches the time threshold, generating a verification result of the user as the user; and when the answer selected by the user does not reach the correct rate threshold or the residence time does not reach the time threshold, generating a verification result that the user is not the user.
8. The intelligent workshop management method based on the internet of things according to claim 5, wherein: the identity information also includes facial features of the user when the eyes are open and facial features of the user when the eyes are closed.
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Citations (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004010365A2 (en) * 2002-07-19 2004-01-29 Dicut Inc. Face recognition system and method therefor
WO2008157342A1 (en) * 2007-06-13 2008-12-24 Videntity Systems, Inc. Identity verification and information management
WO2009043047A1 (en) * 2007-09-28 2009-04-02 Eye Controls, Llc Systems and methods for biometric identification
CN102279422A (en) * 2010-06-11 2011-12-14 秦和平 Detection method based on intelligent anti-terrorism safety inspection door device
CN103902046A (en) * 2014-04-10 2014-07-02 深圳市中兴移动通信有限公司 Intelligent prompting method and terminal
JP2014211766A (en) * 2013-04-18 2014-11-13 三菱化学エンジニアリング株式会社 Personnel management system and personnel management method
CN106562793A (en) * 2015-10-08 2017-04-19 松下电器（美国）知识产权公司 Method for controlling information display apparatus, and information display apparatus
CN107393096A (en) * 2017-08-11 2017-11-24 阜阳华润电力有限公司 The control method and access control device of a kind of gate inhibition
CN107464325A (en) * 2017-09-13 2017-12-12 南京信息职业技术学院 Multi-level access control system and implementation method thereof
JP2018106435A (en) * 2016-12-27 2018-07-05 清水建設株式会社 Labor Management System
WO2018151758A1 (en) * 2017-02-17 2018-08-23 Whatsapp Inc. Methods and systems for processing an ephemeral content message
CN108830975A (en) * 2018-04-27 2018-11-16 安徽继远软件有限公司 A kind of Intelligent human-face identification gate control system and control method
CN109272627A (en) * 2018-11-01 2019-01-25 云南昆钢电子信息科技有限公司 A kind of mine down-hole gate inhibition automatic control device
CN208444022U (en) * 2018-07-25 2019-01-29 中控智慧科技股份有限公司 Station is stared in a kind of safety check
CN110197108A (en) * 2018-08-17 2019-09-03 平安科技（深圳）有限公司 Auth method, device, computer equipment and storage medium
CN110390741A (en) * 2018-04-23 2019-10-29 宁波欧依安盾安全科技有限公司 An intelligent access control system
CN110827457A (en) * 2018-07-23 2020-02-21 宁波欧依安盾安全科技有限公司 Virtual access control system based on safety competence identification
CN111832344A (en) * 2019-04-17 2020-10-27 深圳熙卓科技有限公司 Dynamic pupil detection method and device
CN112507315A (en) * 2021-02-05 2021-03-16 红石阳光（北京）科技股份有限公司 Personnel passing detection system based on intelligent brain

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020097145A1 (en) * 1997-11-06 2002-07-25 David M. Tumey Integrated vehicle security system utilizing facial image verification
IL165586A0 (en) * 2004-12-06 2006-01-15 Daphna Palti Wasserman Multivariate dynamic biometrics system
US20130189661A1 (en) * 2010-06-07 2013-07-25 Affectiva, Inc. Scoring humor reactions to digital media
US10839020B2 (en) * 2014-04-14 2020-11-17 Netspective Communications Llc Multi-source user generated electronic data integration in a blockchain-based transactional system
CN107066847A (en) * 2015-12-07 2017-08-18 由田新技股份有限公司 Identity verification method, device and system
JP2018142258A (en) * 2017-02-28 2018-09-13 オムロン株式会社 Manufacturing management device, method, and program

Patent Citations (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004010365A2 (en) * 2002-07-19 2004-01-29 Dicut Inc. Face recognition system and method therefor
WO2008157342A1 (en) * 2007-06-13 2008-12-24 Videntity Systems, Inc. Identity verification and information management
WO2009043047A1 (en) * 2007-09-28 2009-04-02 Eye Controls, Llc Systems and methods for biometric identification
CN102279422A (en) * 2010-06-11 2011-12-14 秦和平 Detection method based on intelligent anti-terrorism safety inspection door device
JP2014211766A (en) * 2013-04-18 2014-11-13 三菱化学エンジニアリング株式会社 Personnel management system and personnel management method
CN103902046A (en) * 2014-04-10 2014-07-02 深圳市中兴移动通信有限公司 Intelligent prompting method and terminal
CN106562793A (en) * 2015-10-08 2017-04-19 松下电器（美国）知识产权公司 Method for controlling information display apparatus, and information display apparatus
JP2018106435A (en) * 2016-12-27 2018-07-05 清水建設株式会社 Labor Management System
WO2018151758A1 (en) * 2017-02-17 2018-08-23 Whatsapp Inc. Methods and systems for processing an ephemeral content message
CN107393096A (en) * 2017-08-11 2017-11-24 阜阳华润电力有限公司 The control method and access control device of a kind of gate inhibition
CN107464325A (en) * 2017-09-13 2017-12-12 南京信息职业技术学院 Multi-level access control system and implementation method thereof
CN110390741A (en) * 2018-04-23 2019-10-29 宁波欧依安盾安全科技有限公司 An intelligent access control system
CN108830975A (en) * 2018-04-27 2018-11-16 安徽继远软件有限公司 A kind of Intelligent human-face identification gate control system and control method
CN110827457A (en) * 2018-07-23 2020-02-21 宁波欧依安盾安全科技有限公司 Virtual access control system based on safety competence identification
CN208444022U (en) * 2018-07-25 2019-01-29 中控智慧科技股份有限公司 Station is stared in a kind of safety check
CN110197108A (en) * 2018-08-17 2019-09-03 平安科技（深圳）有限公司 Auth method, device, computer equipment and storage medium
CN109272627A (en) * 2018-11-01 2019-01-25 云南昆钢电子信息科技有限公司 A kind of mine down-hole gate inhibition automatic control device
CN111832344A (en) * 2019-04-17 2020-10-27 深圳熙卓科技有限公司 Dynamic pupil detection method and device
CN112507315A (en) * 2021-02-05 2021-03-16 红石阳光（北京）科技股份有限公司 Personnel passing detection system based on intelligent brain

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
人工智能视域下图书馆智慧服务探析;于兴尚;高晶;;图书馆研究(第02期);第107-116页 *

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