CN109686447B - Staff state monitoring system based on artificial intelligence - Google Patents
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Abstract
The invention relates to an artificial intelligence-based staff state monitoring system, belongs to the technical field of state monitoring, and solves the problems that the subjectivity is strong, the real-time performance is poor, the accuracy of a judgment result is low, and the safety production requirement cannot be met in the prior art. The system comprises: the multifunctional sensor is arranged on the helmet and the wrist strap, and the information processor is arranged on the safety work clothes worn by the staff. The multifunctional sensor is used for acquiring the audio and video information and the personal physiological data information of the field working environment of the employee and transmitting the information to the information processor; and the information processor is used for identifying the working state and the real-time physiological health condition of the employee according to the audio and video information of the field working environment of the employee and the personal physiological data information, further judging whether the pre-post physiological state meets the post requirement or not, or whether the physiological state can be qualified for the current work in the working process, and displaying the judgment result. The system can automatically and accurately analyze the physiological and psychological conditions of the staff in real time.
Description
Technical Field
The invention relates to the technical field of state monitoring, in particular to an artificial intelligence-based employee state monitoring system.
Background
Production activities of modern enterprises are continuous and repeatedly in a production line, and enterprise production is often carried out in high-temperature, high-humidity, high-dust and high-noise environments, such as buildings, electric power, production sites of the production line and the like. The modern enterprise production has higher requirements on the operating proficiency of the staff, the fine operation, the production activity has higher requirements on the pre-post state and the working state of the staff, and the psychological and physiological states of the staff directly influence the working quality.
Currently, most enterprises still monitor employees manually and passively, but some new semi-active monitoring methods are available, that is, administrators monitor the working states of employees by monitoring methods, but the following problems generally exist in the management methods:
1) a monitoring room administrator is required to judge the working state of the staff, the judgment subjectivity is strong, and misjudgment and missed judgment are easy to occur;
2) the real-time performance is poor, the labor cost is high, and a manager needs to continuously watch a monitoring picture;
3) for safety production, active prevention in advance, in-process monitoring and after-event feedback cannot be achieved.
Disclosure of Invention
In view of the foregoing analysis, the embodiments of the present invention provide an employee status monitoring system based on artificial intelligence, so as to solve the problems of the prior art, such as strong subjectivity, poor real-time performance, low accuracy of the determination result, and incapability of meeting the requirements of safe production.
In one aspect, an embodiment of the invention provides an employee state monitoring system based on artificial intelligence, which comprises a multifunctional sensor arranged on a helmet and a wrist strap, and an information processor arranged on a safety work clothes worn by an employee.
The multifunctional sensor is used for acquiring the audio and video information and the personal physiological data information of the field working environment of the employee and transmitting the information to the information processor;
the information processor is used for identifying the working state and the real-time physiological health condition of the employee according to the audio and video information of the field working environment of the employee and the personal physiological data information, further judging whether the pre-post physiological state meets the post requirement or not, or whether the physiological state can be qualified for the current work in the working process, and displaying the judgment result.
The beneficial effects of the above technical scheme are as follows: the employee state monitoring system is manufactured on a helmet, a wrist strap and a safety work garment, so that each employee can conveniently wear before work, use during work and change the garment after work. Different multifunctional sensors can be customized according to the physiological and psychological states of a single employee, the working state and the specific real-time physiological health condition of a specific employee can be obtained, and the function expandability is realized. The real-time working state (psychological) and real-time physiological health condition of the staff are automatically judged through the collected real-time audio and video information and personal physiological data information of the working site environment of the staff, whether the pre-post physiological state meets the post requirement or not is further judged, or whether the working physiological state is qualified for the current work or not is further judged, the purpose of active prevention in advance, monitoring in the process and feedback after the process is achieved, the staff which do not meet the post requirement is timely reminded and replaced, and the safe production is ensured. A large number of tests prove that the system has high accuracy of judgment results and can meet the requirements of most production, manufacture and use.
In another embodiment based on the above method, the multifunctional sensor comprises:
the miniature audio and video acquisition equipment is arranged on the helmet and used for acquiring the audio and video information of the field working environment of the employee and transmitting the audio and video information to the information processor;
and the physiological data sensor is arranged on the wrist strap and used for acquiring real-time physiological data information of the employee and transmitting the real-time physiological data information to the information processor.
The beneficial effects of the above technical scheme are: the multifunctional sensor is limited in assembly and installation position, real-time field work environment audio and video information of the staff can be collected through the miniature audio and video collecting device arranged on the helmet, the real-time field work environment audio and video information comprises operation information of relevant field devices, and real-time physiological data of the staff can be collected through the physiological data sensor arranged on the wrist strap, and the real-time physiological data comprises body temperature, respiration, heartbeat frequency, alcohol content and the like.
Furthermore, the miniature audio and video acquisition equipment comprises a high-definition camera, a camera base and a reinforcing device; wherein,
the back of the high-definition camera adopts a clamping groove protrusion design; the front of the camera base adopts a groove design matched with the protruding design of the clamping groove, and the back of the camera base is fixed at a preset quick-connection base or an additional temporary base of the helmet through a reinforcing device.
The beneficial effects of the further scheme are as follows: the miniature audio and video acquisition equipment is limited in composition, and the high-definition camera is firmly fixed at the preset position of the helmet through the matching of the clamping groove and the reinforcement design, so that the audio and video information of the field working environment is stably and effectively acquired.
Further, the reinforcing device comprises a double-sided adhesive tape and a hook and loop fastener elastic band; wherein,
the camera base is adhered to the helmet through the double-sided adhesive, and the high-definition camera is bound to the helmet of the worker through the magic tape elastic band.
The beneficial effects of the further scheme are as follows: the reinforcing device is limited in composition, the camera base can be stably fixed at the preset position of the helmet of the worker through the double-sided adhesive tape and the hook-and-loop fastener elastic band, the method is simple, and the practicability is high.
Further, the real-time physiological data information of the staff comprises at least one of body temperature, respiration, heartbeat frequency and alcohol content data.
The beneficial effects of the further scheme are as follows: in medicine, physiological data of human body temperature, respiration (rhythm), heartbeat frequency, alcohol content and the like have definite normal value ranges, and whether the physiological state of the staff meets the requirement of a work post or not can be conveniently and effectively judged by collecting the data. And the physiological data of the staff can be acquired in real time through the arranged multifunctional sensor, so that a large amount of time is not needed to be spent for carrying out additional physical examination, and the time and the cost are saved.
Further, the information processor includes:
the data processing module is used for identifying the working state of corresponding equipment in the working environment, the working state of the employee and real-time physiological health according to the audio and video information and the personal physiological data information of the field working environment of the employee, further judging whether the post-job physiological state of the employee meets the post requirement or not, judging whether the physiological state can be qualified for the current work or not in the working process, and displaying a judgment result and a related alarm;
the data storage module is used for storing the audio and video information of the field working environment, the personal physiological data information, and corresponding judgment results and related alarms for judging whether the pre-post physiological state meets the post requirement or not, whether the physiological state can be qualified for the current work or not in the work process; the data end of the data processing module is connected with the data end of the data processing module;
the power supply module is used for supplying power to the data processing module, the data storage module and the multifunctional sensor; the output end of the multifunctional sensor is respectively connected with the data processing module, the data storage module and the power supply end of the multifunctional sensor.
The beneficial effects of the further scheme are as follows: the information processor component is modularized, the interface is standardized, different multifunctional sensors can be customized according to different requirements of different posts on physiological and psychological states of the staff, the working state and the specific real-time physiological health condition of the specific staff are obtained, and the function expandability is realized. The data stored by the data storage module can be used as input of the data processing module and can also be used as historical data to provide feedback for parameter optimization of a later system (data processing module).
Further, the system also comprises field video acquisition equipment;
the on-site video acquisition equipment is used for acquiring facial expressions, catch of eyes and limb action information of all employees in an on-site working environment in real time and transmitting the information to the information processor.
The beneficial effects of the further scheme are as follows: through on-site video acquisition equipment, facial expressions, catch of eyes and limb action information of all employees in the on-site working environment can be acquired in real time, and then whether the psychological state of each employee is in fatigue or not is judged, and the fatigue degree is judged, so that the employees who do not meet the post requirements are timely reminded and replaced, and the production efficiency is favorably improved.
Further, the system also comprises a communication module and a micro headset which are connected with the data processing module;
the communication module is used for transmitting the on-site working environment audio and video information of the staff, the personal physiological data information, the corresponding judgment result and the relevant alarm for judging whether the post-process physiological state meets the post requirement and whether the working physiological state is qualified for the current work to the external server, and simultaneously transmitting the instruction sent by the external server to the data processing module for displaying;
the micro headset is used for performing work coordination information interaction among the employees through the communication module, reporting the field condition of the employees to the superior level, and sending instruction information to the employees by the superior level.
The beneficial effects of the further scheme are as follows: through the communication module and the micro headset, information interaction between employees and between the employees and superior (including managers) can be carried out, the work efficiency of each employee is improved, and the user experience and the user satisfaction are improved.
Furthermore, the data processing module comprises a preprocessing submodule, a classification prediction submodule and a display submodule which are connected in sequence; wherein,
the preprocessing submodule is used for carrying out target identification on the audio and video data information obtained by the multifunctional sensor, obtaining signal lamp switching information and audio and video prompt information of the field equipment operated by the employee at the current moment, and taking the signal lamp switching information and the audio and video prompt information as identification indexes of the running condition of the field equipment; carrying out human body and four limbs positioning and face recognition on front and back frame pictures acquired by a field video acquisition device, taking difference data information which indicates whether the limb action of the employee is overstimulated in a certain time period in the human body and four limbs positioning result as an identification index of the limb action of the employee in a field working environment, and taking the mouth shape, the eye size and the shape of the employee at different moments in the face identification result as identification indexes of the facial expression and the eye spirit of the employee in the field working environment; carrying out noise reduction processing on the personal physiological data information obtained by the multifunctional sensor; then, respectively transmitting the identification indexes and the noise reduction processing results to the classification prediction submodule;
the classification prediction submodule judges the following through a pre-trained 3-class classification prediction model: firstly, inputting an identification index of the operation condition of field equipment operated by the employee in a field working environment into a first-class classification prediction model trained in advance to obtain a judgment result of whether the relevant field equipment is started or not; secondly, inputting the noise reduction processing result of the personal physiological data information into a second class classification prediction model trained in advance to obtain a judgment result of whether the real-time physiological health of the employee is competent for continuous work, and if the employee is not competent for work, sending out a incapability alarm; then, respectively inputting the recognition indexes of the facial expression, the eye spirit and the limb action information of the employee in the field working environment into a third class classification prediction model trained in advance, obtaining the judgment result of whether the mental state of the employee is in fatigue and the fatigue degree, and sending out fatigue alarm of a corresponding level; finally, judging whether the pre-post physiological state of the employee meets the post requirement or not by combining with the monitoring time, and whether the physiological and psychological states can be qualified for the current work or not in the work;
and the display module is used for displaying the judgment result and the related alarm in real time.
The beneficial effects of the further scheme are as follows: the composition of the data processing module is limited, so that the data processing module has the functions of data preprocessing, classification prediction and display. And 3 types of classification prediction models are established in the classification prediction submodule, after the classification prediction models are trained, the judgment result of whether the relevant field equipment is started, the judgment result of whether the real-time physiological health of the staff is competent for continuous work and the judgment result of whether the psychological state of the staff is in fatigue can be accurately obtained, and relevant alarms are sent out, so that the staff which does not meet the post requirements can be replaced in time, and the production efficiency and the user satisfaction are improved.
Further, the classification prediction submodule trains a 3-class classification prediction model by adopting the following steps:
acquiring a plurality of groups of training data including identification indexes of the operating conditions of the field equipment and corresponding opening and closing results of the field equipment, inputting the training data into a first-class classification prediction model to obtain parameters of the first-class classification prediction model, and finishing training of the first-class classification prediction model;
collecting a plurality of groups of training data containing personal physiological data of the staff and corresponding real-time physiological health competence of the staff, inputting the training data into a second class classification prediction model to obtain parameters of the second class classification prediction model, and finishing training of the second class classification prediction model;
and acquiring a plurality of groups of training data including facial expressions, eye spirit and limb action indexes of the employees in different psychological states of joy, anger, grief, joy and fright and whether the corresponding real-time psychological health of the employees is competent for the working result, inputting the training data into a third class classification prediction model to obtain parameters of the third class classification prediction model, and finishing the training of the third class classification prediction model.
The beneficial effects of the further scheme are as follows: and the method for training the 3-class classification prediction model is limited, and through the training, the judgment result of whether the relevant field equipment is started, the judgment result of whether the real-time physiological health of the staff is competent for continuing working and the judgment result of whether the psychological state of the staff is in fatigue can be accurately obtained.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a schematic layout diagram of an employee status monitoring system in embodiment 1 of the present invention;
fig. 2 is a schematic layout diagram of an employee status monitoring system in embodiment 2 of the present invention.
Reference numerals:
a 1-multifunctional sensor; a 2-multifunctional sensor; b-an information processor; 1-camera connecting wire; 2-a data line; 3-wrist band (including multifunction sensor); 4-micro headset; 5-audio line; 6-audio plug; 7-waist-tightening elastic bands of the safety work clothes (comprising an information processor); 8-information processor audio interface; 9-information processor data interface.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Example 1
The invention discloses an employee state monitoring system based on artificial intelligence, which comprises multifunctional sensors A1 and A2 arranged on a helmet and a wrist strap, and an information processor B arranged on a safety work clothes worn by an employee, as shown in figure 1.
And the multifunctional sensors A1 and A2 are used for acquiring the audio and video information and the personal physiological data information of the field working environment of the employee and transmitting the information to the information processor B.
And the information processor B is used for identifying the working state and the real-time physiological health condition of the employee according to the audio and video information of the field working environment of the employee and the personal physiological data information, further judging whether the pre-post physiological state meets the post requirement or not, or whether the physiological state can be qualified for the current work in the working process, and displaying the judgment result.
The data collected by the multifunctional sensor can be transmitted to the information processor through data transmission modes such as a data line or a wireless network.
When the system is implemented, the multifunctional sensor can collect the audio and video information and the personal physiological data information of the field working environment of the employee, so as to further obtain the working state and the real-time physiological health condition of the employee, and the monitoring time is combined, so that whether the physiological state of the employee meets the post requirement before the employee goes on duty or whether the physiological state of the employee can be qualified for the current work during the work can be judged, and if not, the employee who does not meet the requirement is timely given an alarm, and the employee goes on duty. In a conventional production place, the state monitoring system can be equipped only for the key links and the staff playing important roles.
Compared with the prior art, the staff state monitoring system based on artificial intelligence that this embodiment provided prepares on helmet, wrist strap and safe work clothes, and every staff of being convenient for dresses before the work, the during operation uses, changes clothes after the work. Different multifunctional sensors can be customized according to the physiological and psychological states of a single employee, the working state of a specific employee and the specific real-time physiological health condition are obtained for further analysis, and the function expandability is achieved. The system automatically judges the real-time working state (psychological) and real-time physiological health condition of the staff through the collected real-time audio and video information of the working site environment of the staff and the personal physiological data information, and further judges whether the pre-post physiological state meets the post requirement or not or whether the physiological state can be qualified for the current work in the working process. The system is applied to the existing production activities, so that active prevention in advance, in-process monitoring and after-process feedback can be realized, staff who do not meet the post requirements can be timely reminded and replaced, and safe production is guaranteed. A large number of tests prove that the system has high judgment accuracy and can meet the requirements of production, manufacture and use.
Example 2
Optimization is carried out on the basis of the embodiment 1, and as shown in fig. 2, the multifunctional sensor comprises a miniature audio-video acquisition device and a physiological data sensor.
And the miniature audio and video acquisition equipment is used for acquiring the audio and video information of the field working environment of the employee and transmitting the audio and video information to the information processor. When the helmet is used, the miniature audio and video acquisition equipment can be placed at a preset quick-connection base of the helmet or an additionally-arranged temporary base, and a proper position can be selected according to actual conditions.
And the physiological data sensor is used for acquiring the real-time physiological data information of the employee and transmitting the real-time physiological data information to the information processor. When in use, the physiological data sensor is arranged at a position on the wrist strap 3 which is not easy to collide. The real-time physiological data information includes body temperature, respiration, heartbeat frequency and alcohol content data. The physiological data sensor can adopt the existing body temperature, respiration, heartbeat frequency and alcohol content detection sensors, and is not repeated.
Preferably, the miniature audio and video acquisition equipment further comprises a high-definition camera, a camera base and a reinforcing device. The back of the high-definition camera adopts a clamping groove protrusion design; the front of the camera base adopts a groove design matched with the protruding design of the clamping groove, and the back of the camera base is fixed at the preset quick-connection base of the helmet or the additional temporary base through a reinforcing device. The reinforcing device comprises a double-sided adhesive tape and a magic tape elastic band. The camera base is pasted on the helmet through the double-sided adhesive, and the high-definition camera is bound on the helmet of the worker through the magic tape.
Preferably, the information processor comprises a data processing module, a data storage module and a power supply module. The data end of the data storage module is connected with the data end of the data processing module to perform bidirectional data transmission; the output end of the power supply module is respectively connected with the data processing module, the data storage module and the power supply end of the multifunctional sensor.
And the data processing module is used for identifying the working state of corresponding equipment in the working environment, the working state of the employee and real-time physiological health according to the audio and video information and the personal physiological data information of the field working environment of the employee, further judging whether the physiological state before the post meets the post requirement or whether the physiological state can be qualified for the current work during the work, and displaying the judgment result and related alarms.
The data storage module is used for storing the audio and video information of the field working environment of the staff, the personal physiological data information, and corresponding judgment results and alarm information on whether the pre-post physiological state meets the post requirement and whether the physiological state can be qualified for the current work in the work process; the data end of the data processing module is connected with the data section of the data processing module.
The power supply module is used for supplying power to the data processing module, the data storage module and the multifunctional sensor; the output end of the multifunctional sensor is respectively connected with the data processing module, the data storage module and the power supply end of the multifunctional sensor.
Preferably, staff's state monitoring system based on artificial intelligence still includes on-the-spot video acquisition equipment, communication module and miniature headset, and wherein, communication module and miniature headset are connected with the communication end and the headset end (audio interface 8) that data processing module is connected respectively.
And the field video acquisition equipment is used for acquiring facial expressions, catch and limb action information of all employees in a field working environment in real time and transmitting the information to the information processor for identifying the psychological state of each employee.
And the communication module is used for transmitting the on-site working environment audio and video information of the staff, the personal physiological data information, a judgment result and alarm information of whether the corresponding post-working physiological state meets the post requirement or not and whether the physiological state can be qualified for the current work or not in the work to the external server, and simultaneously transmitting an instruction sent by the external server to the data processing module for displaying.
The micro headset is used for performing work cooperation interaction between the employees through the communication module, reporting the field condition of the employees to a superior level, and sending instruction information to the employees by the superior level.
Preferably, the information processor is sewn at the waist position of the safety work clothes through a magic tape, or is sewn on the waist-retracting elastic band 7 of the safety work clothes through a magic tape.
Preferably, the data processing module comprises a preprocessing submodule, a classification predicting submodule and a display submodule which are connected in sequence.
The preprocessing submodule is used for identifying the facial expression, the eyesight and the limb action of each employee in the field working environment, identifying the running condition of field equipment operated by the employee in the field working environment, carrying out noise reduction processing on personal physiological data information, and then respectively transmitting the identification result and the noise reduction processing result to the classification prediction submodule. Specifically, single-frame target identification is carried out on audio and video data obtained by a miniature audio and video acquisition device, signal lamp switching information and other special prompt information (audio and video prompt information) of field equipment operated by the employee at the current moment are obtained, and the signal lamp switching information and the other special prompt information are used as identification indexes of the running condition of the field equipment; carrying out human body and four limbs positioning and face recognition on front and rear frame pictures of a video acquired by a field video acquisition device, taking distinguishing data information which represents whether the employee has an overstimulated behavior in a certain time period in a human body and four limbs positioning result as a recognition index of the employee's limb behavior in a field working environment (for example, whether the employee's limb behavior in a certain time period has the overstimulated behavior can be judged by comparing the actual action amplitude with a threshold value), and taking the mouth shape, the eye size and the shape of the employee at different moments in the face recognition result as recognition indexes of the employee's facial expression and eye spirit in the field working environment; carrying out noise reduction processing on the personal physiological data information obtained by the multifunctional sensor; and then, respectively transmitting the identification indexes and the noise reduction processing results to the classification prediction submodule. The other special prompt messages include alarm sound of the field device, screen display content of the field device, signal lamp opening and closing information of other devices working in cooperation with the field device, and the like.
The denoising treatment comprises the steps of performing curve fitting on the collected personal physiological data information, and removing discrete noise points and burrs so as to achieve the purpose of removing dryness of the collected original data. Optionally, denoising processing may be performed on the audio and video data obtained by the miniature audio and video acquisition device and the video acquired by the field video acquisition device.
The classification prediction submodule judges the following through a pre-trained 3-class classification prediction model: firstly, inputting an identification index of the operation condition of field equipment operated by the employee in a field working environment into a first-class classification prediction model trained in advance to obtain a judgment result of whether the relevant field equipment is started or not; secondly, inputting the noise reduction processing result of the personal physiological data information into a second class classification prediction model trained in advance to obtain a judgment result of whether the real-time physiological health of the employee is competent for continuous work, and if the employee is not competent for work, sending out a incapability alarm; then, respectively inputting the recognition indexes of the facial expression, the eye spirit and the limb action information of the employee in the field working environment into a third class classification prediction model trained in advance, obtaining the judgment result of whether the mental state of the employee is in fatigue and the fatigue degree, and sending out fatigue alarm of a corresponding level; and finally, judging whether the pre-post physiological state of the employee meets the post requirement or not and whether the physiological and psychological states can be qualified for the current work or not in the work by combining the monitoring time.
And the display module is used for displaying the judgment result and the related alarm in real time.
Alternatively, the 3 types of classification prediction models may employ a support vector machine model or an existing neural network model.
Preferably, the classification prediction sub-module trains the 3-class classification prediction model by adopting the following steps:
s1, collecting a plurality of groups of training data including identification indexes of field equipment operation conditions and corresponding field equipment opening and closing results, storing the training data in a classified mode, establishing a training data set, inputting the training data in the training data set into a first-class classification prediction model, obtaining parameters of the first-class classification prediction model, and finishing training of the first-class classification prediction model.
S2, collecting a plurality of groups of training data containing personal physiological data of the staff and corresponding real-time physiological health competence of the staff for the working result, storing the training data in a classified manner, establishing a training data set, inputting the training data in the training data set into a second class classified prediction model, obtaining parameters of the second class classified prediction model, and finishing training the second class classified prediction model;
and S3, acquiring a plurality of groups of training data including facial expressions, eye spirit and limb action indexes of the employees in different psychological states of joy, anger, grief, joy and fright and whether the corresponding real-time psychological health of the employees is competent for the working result, inputting the training data into a third class classification prediction model to obtain parameters of the third class classification prediction model, and finishing the training of the third class classification prediction model.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (6)
1. An employee status monitoring system based on artificial intelligence, comprising: the multifunctional sensor is arranged on the helmet and the wrist strap, and the information processor is arranged on the safety work clothes worn by the staff;
the multifunctional sensor is used for acquiring the audio and video information and the personal physiological data information of the field working environment of the employee and transmitting the information to the information processor; the multifunctional sensor comprises: the miniature audio and video acquisition equipment is arranged on the helmet and used for acquiring the audio and video information of the field working environment of the employee and transmitting the audio and video information to the information processor; the physiological data sensor is arranged on the wrist strap and used for acquiring real-time physiological data information of the employee and transmitting the real-time physiological data information to the information processor;
the information processor is used for identifying the working state and the real-time physiological health condition of the employee according to the audio and video information of the field working environment of the employee and the personal physiological data information, further judging whether the pre-post physiological state meets the post requirement or not, or whether the physiological state can be qualified for the current work in the working process, and displaying the judgment result; the information processor includes: the data processing module is used for identifying the working state of corresponding equipment in the working environment, the working state of the employee and real-time physiological health according to the audio and video information and the personal physiological data information of the field working environment of the employee, further judging whether the post-job physiological state of the employee meets the post requirement or not, judging whether the physiological state can be qualified for the current work or not in the working process, and displaying a judgment result and a related alarm; the data storage module is used for storing the audio and video information of the field working environment, the personal physiological data information, and corresponding judgment results and related alarms for judging whether the pre-post physiological state meets the post requirement or not, whether the physiological state can be qualified for the current work or not in the work process; the data end of the data processing module is connected with the data end of the data processing module; the power supply module is used for supplying power to the data processing module, the data storage module and the multifunctional sensor; the output end of the multifunctional sensor is respectively connected with the data processing module, the data storage module and the power supply end of the multifunctional sensor;
the system also includes a live video capture device;
the on-site video acquisition equipment is used for acquiring facial expressions, eye movements and limb movement information of all employees in an on-site working environment in real time and transmitting the information to the information processor;
the data processing module comprises a preprocessing submodule, a classification prediction submodule and a display submodule which are connected in sequence; the preprocessing submodule is used for carrying out target identification on audio and video data information obtained by the multifunctional sensor, obtaining signal lamp switching information and audio and video prompt information of field equipment operated by the staff at the current moment, and taking the signal lamp switching information and the audio and video prompt information as identification indexes of the operation condition of the field equipment, wherein the audio and video prompt information comprises alarm sound of the field equipment, screen display content of the field equipment and signal lamp switching information of other equipment working cooperatively with the field equipment; carrying out human body and four limbs positioning and face recognition on front and back frame pictures acquired by a field video acquisition device, taking difference data information which indicates whether the limb action of the employee is overstimulated in a certain time period in the human body and four limbs positioning result as an identification index of the limb action of the employee in a field working environment, and taking the mouth shape, the eye size and the shape of the employee at different moments in the face identification result as identification indexes of the facial expression and the eye spirit of the employee in the field working environment; carrying out noise reduction processing on the personal physiological data information obtained by the multifunctional sensor; then, respectively transmitting the identification indexes and the noise reduction processing results to the classification prediction submodule; the classification prediction submodule judges the following through a pre-trained 3-class classification prediction model: firstly, inputting an identification index of the operation condition of field equipment operated by the employee in a field working environment into a first-class classification prediction model trained in advance to obtain a judgment result of whether the relevant field equipment is started or not; secondly, inputting the noise reduction processing result of the personal physiological data information into a second class classification prediction model trained in advance to obtain a judgment result of whether the real-time physiological health of the employee is competent for continuous work, and if the employee is not competent for work, sending out a incapability alarm; then, respectively inputting the recognition indexes of the facial expression, the eye spirit and the limb action information of the employee in the field working environment into a third class classification prediction model trained in advance, obtaining the judgment result of whether the mental state of the employee is in fatigue and the fatigue degree, and sending out fatigue alarm of a corresponding level; finally, judging whether the pre-post physiological state of the employee meets the post requirement or not by combining with the monitoring time, and whether the physiological and psychological states can be qualified for the current work or not in the work; the display module is used for displaying the judgment result and the related alarm in real time;
and denoising, namely performing curve fitting on the acquired personal physiological data information to remove discrete noise points and burrs so as to achieve the purpose of denoising the acquired original data.
2. The artificial intelligence based employee state monitoring system according to claim 1, wherein the miniature audio/video acquisition device comprises a high-definition camera, a camera base and a reinforcing device; wherein,
the back of the high-definition camera adopts a clamping groove protrusion design; the front of the camera base adopts a groove design matched with the protruding design of the clamping groove, and the back of the camera base is fixed at a preset quick-connection base or an additional temporary base of the helmet through a reinforcing device.
3. The artificial intelligence based employee status monitoring system according to claim 2, wherein the reinforcement device includes a double-sided tape, a velcro tape; wherein,
the camera base is adhered to the helmet through the double-sided adhesive, and the high-definition camera is bound to the helmet of the worker through the magic tape elastic band.
4. An artificial intelligence based employee status monitoring system according to any one of claims 1 to 3 wherein the real time physiological data information of the employee includes at least one of body temperature, respiration, heart beat rate and alcohol content data.
5. The artificial intelligence based employee status monitoring system according to claim 1 further comprising a communication module and a micro headset connected to said data processing module;
the communication module is used for transmitting the on-site working environment audio and video information of the staff, the personal physiological data information, the corresponding judgment result and the relevant alarm for judging whether the post-process physiological state meets the post requirement and whether the working physiological state is qualified for the current work to the external server, and simultaneously transmitting the instruction sent by the external server to the data processing module for displaying;
the micro headset is used for performing work coordination information interaction among the employees through the communication module, reporting the field condition of the employees to the superior level, and sending instruction information to the employees by the superior level.
6. The artificial intelligence based employee status monitoring system of claim 1 wherein the class prediction sub-module trains the class 3 class classification prediction model by:
acquiring a plurality of groups of training data including identification indexes of the operating conditions of the field equipment and corresponding opening and closing results of the field equipment, inputting the training data into a first-class classification prediction model to obtain parameters of the first-class classification prediction model, and finishing training of the first-class classification prediction model;
collecting a plurality of groups of training data containing personal physiological data of the staff and corresponding real-time physiological health competence of the staff, inputting the training data into a second class classification prediction model to obtain parameters of the second class classification prediction model, and finishing training of the second class classification prediction model;
and acquiring a plurality of groups of training data including facial expressions, eye spirit and limb action indexes of the employees in different psychological states of joy, anger, grief, joy and fright and whether the corresponding real-time psychological health of the employees is competent for the working result, inputting the training data into a third class classification prediction model to obtain parameters of the third class classification prediction model, and finishing the training of the third class classification prediction model.
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