Monitoring and early warning system and method for abnormal working state of ship operator

Abstract

The invention provides a ship operator abnormal working state monitoring and early warning system and method, and the method comprises the steps: collecting the face, limb, language and body temperature information of an operator through a collection assembly disposed in an important operation cabin of a ship; the information is input into the state signal collecting and processing equipment, and the information is transmitted to the operator abnormal state analyzing and calculating equipment to calculate, analyze and evaluate the collected information after being subjected to centralized processing and conversion, so that information evaluation results of the face emotion, the working posture, the language communication and the body temperature of the operator are obtained; results are transmitted to an operator state management terminal, and result data are managed; and performing management intervention according to the severity of the abnormal working state of the operator. The system is used for monitoring, analyzing and managing fatigue, emotion abnormity, inattention, operation posture abnormity, term abnormity, body abnormity and the like of ship operators, performing graded reminding, management intervention and the like of abnormal working states of the operators, and reducing risks caused by the abnormal working states of the operators.

Description

Technical Field

[0001] The invention belongs to the technical field of ship personnel management, and in particular relates to a system and method for monitoring and warning abnormal working status of ship operators. Background Art

[0002] As an important water transport equipment, the operation safety of ships is very important. At present, the degree of ship automation is getting higher and higher, and the operating behavior of operators of important systems such as command and power has an increasingly greater impact on safety, and the consequences of wrong behavior are becoming more and more verified. Operators must have a good physiological and psychological operating state to be competent for ship operation tasks. However, due to the particularity of ship marine environment operations, long voyages, night shifts, circadian rhythm disruption, high workload, lack of sleep, illness and other factors, operators are often in a state of fatigue, depression, lack of concentration, fever, abnormal psychology, etc., resulting in a decrease in the efficiency of operators in performing operational tasks, and may even lead to operational accidents or malicious destructive operations. It is necessary to monitor and evaluate operator behavior, physiology and other information to ensure that operators in important compartments are in good operating conditions and standardized operating behaviors, so as to ensure that ships can operate safely and efficiently. Summary of the invention

[0003] In order to solve the technical problems in the prior art, the present invention provides a ship operator abnormal working state monitoring and early warning system, including an acquisition component, a state signal acquisition and processing device, an operator abnormal state analysis and calculation device, an operator state management and decision-making terminal and an operator terminal device. The acquisition component collects the operator's facial, limb, language, and body temperature information; the information is input into the state signal acquisition and processing device, and after centralized signal processing and conversion, it is transmitted to the operator abnormal state analysis and calculation device to perform calculation, analysis, and evaluation on the collected information to obtain the information evaluation results of the operator's facial emotions, working posture, language communication, and body temperature; the results are transmitted to the operator state management terminal to manage the result data; and management intervention is performed through the operator terminal device according to the severity of the operator's abnormal working state.

[0004] Furthermore, the acquisition component includes:

[0005] The facial image acquisition camera is used to collect facial image information of cabin operators during operation. The image information is used for calculation and analysis of the operator's facial expression, emotion, fatigue, etc.;

[0006] The working posture collection camera is used to collect working posture images of the cabin operator's head, neck, arms, chest and abdomen and other important parts during the operation. The working posture images are used for calculation and analysis of abnormal conditions such as the operator being absent from work, not looking at the screen, nodding or dragging the chin frequently, or sleeping on his stomach;

[0007] Voice information collection microphone, used to collect the language instruction information and language communication information of the cabin operator during operation. The voice information is used to analyze the use of instructions, negative words, angry words, terrifying words, and threatening words;

[0008] The temperature infrared acquisition sensor is used to collect the human body surface temperature information of the cabin operator during operation, and compare and analyze it with the human body temperature limit values ​​such as fever and hypothermia.

[0009] Furthermore, the operator terminal device includes:

[0010] A smart reminder wristwatch, which is worn by the operator and is wirelessly connected to the operator status management and decision-making terminal, and reminds the operator of abnormal working status through different vibration levels and different sound modes;

[0011] The vibration reminder seat can remind operators of abnormal working conditions when doing seated work through different levels of vibration of the seat cushion.

[0012] Furthermore, in the operator abnormal state analysis and calculation device, the operator's facial image information and working posture image information are calculated and analyzed using the operator intelligent learning and training method:

[0013] Recognition of facial graphic features and working posture graphic features: By analyzing and processing the operator's facial images and working posture images collected and input by the facial image acquisition camera and the working posture acquisition camera, a multi-task convolutional neural network method is used to model the face and limb bones, and determine the position, size, working posture skeleton and other information of the operator's face; extract the location and contour of the eye and mouth areas, accurately match the facial eye and mouth contours, and extract the working posture of the limb bones;

[0014] Extraction and judgment of facial emotions, fatigue status, and working posture features: Machine learning combined with deep learning methods are used to calculate the ship operator's eye closure time, blinking frequency, yawning time and frequency, distraction, and facial expression information. It also calculates the operator's absenteeism, not looking at the screen, frequent nodding or chin-dragging, and sleeping posture, analyzes and evaluates the operator's working status, and then conducts graded early warning management.

[0015] Furthermore, in the operator abnormal state analysis and calculation equipment, the operator's body surface problem information collected by the temperature infrared acquisition sensor is compared with the human body temperature limit values ​​such as fever and hypothermia to determine whether the operator is in a fever or hypothermia state, and then perform graded early warning management.

[0016] Furthermore, the operator status management and decision-making terminal can present the operator's work status analysis results, historical data, statistical data, and graded warning management results through the human-machine interface; it can send operator work status abnormality reminder instructions to the smart reminder watch and vibration reminder seat; it can intervene in the operator's work status abnormality management through the organization management level, and perform operator status assessment, suspension, and post adjustment intervention according to different levels. And the operator's work status monitoring information can be output to the upper-level information platform network of the entire ship through network signals.

[0017] The installation method of the above-mentioned ship operator abnormal working state monitoring and early warning system comprises:

[0018] Arrange various acquisition components at appropriate locations in important operating compartments of the ship;

[0019] Install the status signal acquisition and processing equipment in the important operation cabin of the ship, and connect all acquisition components to the status signal acquisition and processing equipment;

[0020] Install the abnormal state analysis and calculation equipment, and connect the state signal acquisition and processing equipment and the abnormal state analysis and calculation equipment with a network cable;

[0021] Install the operator status management and decision-making terminal in the operator working status management cabin and connect it to the abnormal status analysis and calculation equipment;

[0022] System connection and debugging.

[0023] Further, it is characterized by comprising:

[0024] System information collection, calculation, analysis and evaluation are all automated processes;

[0025] The acquisition component continuously collects the facial, posture, voice, and body temperature information of operators in important operating cabins of the ship, and transmits it to the status signal acquisition and processing equipment through the signal line for centralized signal uniform conversion and processing;

[0026] After unified processing, the data is transmitted to the operator abnormal status analysis and computing equipment through the network line to perform multi-task convolutional neural network calculations, machine learning, and deep learning to analyze and evaluate the operator's fatigue, abnormal emotions, lack of concentration, abnormal working posture, abnormal language, and physical abnormalities;

[0027] The results are transmitted to the operator status management and decision-making terminal through the network line. The management personnel can analyze the operator's work status, historical data, statistical data, and graded warning management results through the operator status management and decision-making terminal human-machine interface.

[0028] Furthermore, it is characterized in that it also includes:

[0029] Based on the calculation, analysis and statistical results, the operator's abnormal working status is warned in different levels;

[0030] When the severity level of abnormal working conditions such as operator fatigue, abnormal emotions, lack of concentration, abnormal working posture, abnormal language, physical abnormalities, etc. is low, the operator's abnormal working status reminder instructions are sent to the smart reminder watch and vibration reminder seat;

[0031] After receiving seat vibration, watch vibration or voice reminder, the operator will change the working status on his own;

[0032] Furthermore, it is characterized in that: when the severity level of abnormal working status is high, the frequency is high, and the number of historical statistical data is large, the management level will intervene in the abnormal management of the operator's working status based on the results, and evaluate the operator status according to different levels. If the evaluation status passes, the operator can continue to operate on the job. If it is different, the operator will be suspended for a period of time for adjustment, or even intervene by reassigning the operator.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention is used for monitoring, analyzing and managing fatigue, abnormal emotions, lack of concentration, abnormal working posture, abnormal language, physical abnormalities, etc. of ship operators, and for performing graded reminders and management interventions on operators' abnormal working states, thereby reducing the risks brought about by operators' abnormal working states. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a monitoring and early warning system for abnormal working status of ship operators of the present invention;

[0036] Figure 2 The present invention is a method for monitoring and early warning the facial state and working posture state of a ship operator;

[0037] Figure 3 The present invention is a method for monitoring and warning the voice information status of a ship operator;

[0038] Figure 4 The invention discloses a method for monitoring and early warning the body temperature status of a ship operator. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0040] like Figure 1As shown, the present application proposes a ship operator abnormal working status monitoring and early warning system consisting of 1 facial image acquisition camera, 2 working posture acquisition camera, 3 voice information acquisition microphone, 4 temperature infrared acquisition sensor, 5 status signal acquisition and processing equipment, 6 operator abnormal status analysis and calculation equipment, 7 smart reminder watch, 8 vibration reminder seat, 9 operator status management and decision terminal, etc.

[0041] Basic working principle: The operator’s facial, limb, language, body temperature and other information are collected through the operators’ 1 facial image acquisition camera, 2 working posture acquisition camera, 3 voice information acquisition microphone, 4 temperature infrared acquisition sensor and other equipment installed in the important working compartments of the ship; the information is input into the 5 state signal acquisition and processing equipment, and after centralized signal processing and conversion, it is transmitted to the 6 operator abnormal state analysis and calculation equipment to calculate, analyze and evaluate the collected information, and obtain the information evaluation results of the operator’s facial emotions, working posture, language communication, and body temperature; the results are transmitted to the operator status management terminal to manage the result data; according to the severity of the operator’s abnormal working state, smart watch reminders, seat vibration reminders, operator evaluation status, suspension of operation and transfer of positions and other management interventions are carried out. Figure 1 shown.

[0042] Among them, 1. facial image acquisition camera is used to collect facial image information of cabin operators during operation, and the image information is used for calculation and analysis of operator's facial expression, emotion, fatigue, etc.; 2. working posture acquisition camera is used to collect working posture images of important parts such as head, neck, arm, chest and abdomen of cabin operators during operation, and working posture images are used for calculation and analysis of abnormal states such as operator being absent from work, not looking at the screen, nodding or dragging chin frequently, sleeping on the stomach, etc.; 3. voice information acquisition microphone is used to collect language instruction information and language communication information during cabin operators' operation, and voice information is used for analysis of instruction usage norms, negative words, angry words, terrifying words, threatening words, etc.; 4. temperature infrared acquisition sensor is used to collect human body surface temperature information during cabin operators' operation, and is used for comparison and analysis with human body temperature limit values ​​such as fever and low temperature; 5. state signal acquisition and processing equipment is used to centrally unify the collected operator's facial information, working posture information, human body temperature information, and voice information , and then input into the computer for data calculation, analysis and evaluation; 6 The operator abnormal state analysis and calculation equipment is used to perform multi-task convolutional neural network calculation, machine learning, deep learning, etc. on the uniformly processed information, and analyze and evaluate the operator's fatigue, abnormal emotions, lack of concentration, abnormal working posture, abnormal language, physical abnormalities, etc. to obtain evaluation results; 7 The smart reminder watch is worn by the operator and is wirelessly connected to the 9 operator status management and decision-making terminal to remind the operator of abnormal working status through different vibration levels, different sounds, etc.; 8 The vibration reminder seat reminds the operator of abnormal working status of sitting working through different levels of vibration of the seat cushion; 9 The operator status management and decision-making terminal is used for the human-machine interface presentation and storage of the operator status calculation and analysis result data, as well as the management of the call of historical and statistical data; 10 is the signal line of analog signals, bus signals, etc. for signal acquisition and transmission; 11 is the industrial Ethernet network wiring for information transmission.

[0043] In the 6 operator abnormal state analysis and calculation devices, the operator's facial image information and working posture image information are calculated and analyzed by the operator intelligent learning and training methods: (1) Facial graphic features and working posture graphic feature recognition: By analyzing and processing the operator's facial image and working posture image collected and input by the 1 facial image acquisition camera and the working posture acquisition camera, the multi-task convolutional neural network method is used to model the face and limb bones, and determine the position, size, working posture skeleton and other information of the operator's face; extract the location and contour of the eye and mouth areas, accurately match the facial eye and mouth contours, and extract the limb bone working posture; (2) Facial emotion, fatigue state, working posture feature extraction and judgment: Machine learning combined with deep learning methods are used to calculate the ship operator's eye closing time, blinking frequency, mouth yawning time and frequency, distraction, facial expressions and other information, and calculate the operator's working postures such as being off duty, not looking at the screen, frequently nodding or dragging the chin, and sleeping on the stomach, so as to analyze and evaluate the operator's working status and then carry out graded warning management.

[0044] In the 6 operator abnormal state analysis and calculation equipment, the keyword retrieval method is used to directly identify the operator's voice information. In order to solve the interference problem of the complex background noise environment in the ship's cabin, the garbage model processing method is used to process the voice information collected by the 3 voice collection microphones. A model is established for each set instruction and term keyword. In addition, a filling model is established for non-keyword voices such as out-of-set words and common non-language phenomena (silence, pause, noise), and a network is connected in parallel. The Viterbi algorithm is used for decoding search to obtain the voice information recognition result. The GMM-HMM method is used to acoustically model the keywords and filling models. According to the characteristics of the vocabulary, a non-jump or jump-from-left-to-right HMM model is used. Multiple states are set for each syllable. The probability function of each state is composed of the sum of multiple Gaussian probability densities, and the covariance of each Gaussian density function uses a diagonal matrix. Large vocabulary continuous speech recognition technology based on DNN-HMM method transforms the original acoustic features with a multi-layer bottom network, performs discrimination training in the final top network, and optimizes the recognition performance by integrating feature optimization extraction and acoustic modeling in the same network. It can accurately identify the operator's voice information, analyze and evaluate the operator's voice status, calculate the operator's instruction standardization, and the negative words, angry words, terrifying words, threatening words, etc. in the voice, and then conduct graded early warning management. For details, see Figure 3 shown.

[0045] In the 6 operator abnormal state analysis and calculation equipment, the operator's body surface problem information collected by the 4 temperature infrared acquisition sensor is compared with the human body temperature limit values ​​such as fever and low temperature to determine whether the operator is in a state of fever or low temperature, and then perform graded early warning management. Figure 4 shown.

[0046] The operator status management and decision-making terminal can present the operator's work status analysis results, historical data, statistical data, and graded warning management results through the human-machine interface. The operator's abnormal work status reminder instructions can be sent to the smart reminder watch and vibration reminder seat. The operator's work status abnormal management intervention can be carried out through the organization management level, and the operator status assessment, suspension, and post transfer intervention can be carried out according to different levels. And the operator's work status monitoring information can be output to the upper-level information platform network of the whole ship through network signals.

[0047] The abnormal working status monitoring and early warning system for ship operators ensures the personal privacy of operators. The collection of images, voice and temperature information is subject to authority restrictions and is only available to authorized managers for decision-making.

[0048] (1) Arrange 1 facial image acquisition camera, 2 operation posture acquisition camera, 3 voice acquisition microphone, and 4 temperature infrared acquisition sensor at appropriate locations in the ship's important operation cabins. The 1 facial image acquisition camera needs to be arranged in front of the operator to be able to capture the operator's facial image. The camera, microphone, temperature infrared acquisition sensor, etc. should be installed in a concealed manner as much as possible to reduce the psychological impact on the operator.

[0049] (2) Install the 5-state signal acquisition and processing equipment in the important operation cabin of the ship, and connect all cameras, microphones, and temperature infrared acquisition sensors to the 5-state signal acquisition and processing equipment. It is recommended that the 5-state signal acquisition and processing equipment be installed directly in the same cabin as the cameras, microphones, and temperature infrared acquisition sensors to reduce the signal line passing through the cabin and the wiring distance to avoid signal loss.

[0050] (3) Install the 6 abnormal state analysis and calculation equipment, and connect the 5 state signal acquisition and processing equipment and the 6 abnormal state analysis and calculation equipment with a network cable. It is recommended that the 6 abnormal state analysis and calculation equipment be installed in the operator's work status management cabin.

[0051] (4) Install the 9 operator status management and decision-making terminal in the operator work status management cabin. The operator work status management cabin facility is an independent cabin, separated from the important operation cabin to avoid psychological pressure on the operator. At the same time, it also ensures the privacy and security of the collected and analyzed data. Use a network cable to connect the 6 abnormal state analysis and calculation equipment with the 9 operator status management and decision-making terminal, and connect the upper-level information platform network of the entire ship with the 9 operator status management and decision-making terminal; use a signal cable to connect the 9 operator status management and decision-making terminal 8 vibration reminder seat, and wirelessly connect 7 smart reminder watch.

[0052] (5) System connection and debugging completed.

[0053] (6) Usage process: The system information collection, calculation, analysis and evaluation are all automated processes. Cameras, microphones, temperature infrared acquisition sensors, etc. continuously collect information such as the operator's face, posture, voice, body temperature, etc. in the important operating cabins of the ship, and transmit them to the 5 state signal acquisition and processing equipment through the signal line for centralized signal unified conversion and processing; after unified processing, they are transmitted to the 6 operator abnormal state analysis and calculation equipment through the network line for multi-task convolutional neural network calculation, machine learning, deep learning, etc., to analyze and evaluate the operator's fatigue, abnormal emotions, lack of concentration, abnormal working posture, abnormal language, physical abnormalities, etc.; the results are transmitted to the 9 operator state management and decision-making terminal through the network line, and the management personnel can use the 9 operator state management and decision-making terminal human-machine interface to analyze the operator's work status results, historical data, statistical data, and graded warning management results.

[0054] According to the results of calculation, analysis, statistics, etc., the operator's abnormal working state is warned in different levels. When the severity level of abnormal working states such as operator fatigue, abnormal emotions, lack of concentration, abnormal working posture, abnormal language, and physical abnormalities is low, the operator's abnormal working state reminder instructions are sent to the smart reminder watch and vibration reminder seat. After receiving the seat vibration, watch vibration or voice reminder, the operator changes the working state on his own. When the severity level of the abnormal working state is high, the frequency is high, and the number of historical statistical data is large, the management will intervene in the abnormal management of the operator's working state based on the results, and evaluate the operator's status according to different levels. If the evaluation status passes, the operator can continue to operate on the job. If it is not, the operator will be suspended for a period of time for adjustment, or even transferred to another position.

[0055] In summary, the above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereto. All equivalent changes and modifications made according to the patent scope of the present invention and the contents of the specification are within the scope covered by the patent of the present invention.

Claims

1. A ship operator abnormal working state monitoring and early warning system, comprising an acquisition component, a state signal acquisition and processing device, an operator abnormal state analysis and calculation device, an operator state management and decision terminal and an operator terminal device, characterized in that: The acquisition component collects information about the operator's face, limbs, language, and body temperature; the information is input into the status signal acquisition and processing equipment, and after centralized signal processing and conversion, it is transmitted to the operator abnormal status analysis and calculation equipment to perform calculation, analysis, and evaluation on the collected information, and obtain information evaluation results on the operator's facial emotions, working posture, language communication, and body temperature; the results are transmitted to the operator status management terminal to manage the result data; management intervention is performed through the operator terminal equipment according to the severity of the operator's abnormal working status.

2. The system according to claim 1, characterized in that The acquisition component comprises: The facial image acquisition camera is used to collect facial image information of cabin operators during operation. The image information is used for calculation and analysis of the operator's facial expression, emotion, fatigue, etc.; The working posture collection camera is used to collect working posture images of the cabin operator's head, neck, arms, chest and abdomen and other important parts during the operation. The working posture images are used for calculation and analysis of abnormal conditions such as the operator being absent from work, not looking at the screen, nodding or dragging the chin frequently, or sleeping on his stomach; Voice information collection microphone, used to collect the language instruction information and language communication information of the cabin operator during operation. The voice information is used to analyze the use of instructions, negative words, angry words, terrifying words, and threatening words; The temperature infrared acquisition sensor is used to collect the human body surface temperature information of the cabin operator during operation, and compare and analyze it with the human body temperature limit values ​​such as fever and hypothermia.

3. The system according to claim 2, characterized in that Operator terminal equipment includes: A smart reminder wristwatch, which is worn by the operator and is wirelessly connected to the operator status management and decision-making terminal, and reminds the operator of abnormal working status through different vibration levels and different sound modes; The vibration reminder seat can remind operators of abnormal working conditions when doing seated work through different levels of vibration of the seat cushion.

4. The system according to claim 3, characterized in that: In the operator abnormal state analysis and calculation equipment, the operator's facial image information and working posture image information are calculated and analyzed using the operator intelligent learning and training method: Recognition of facial graphic features and working posture graphic features: By analyzing and processing the operator's facial images and working posture images collected and input by the facial image acquisition camera and the working posture acquisition camera, a multi-task convolutional neural network method is used to model the face and limb bones, and determine the position, size, working posture skeleton and other information of the operator's face; extract the location and contour of the eye and mouth areas, accurately match the facial eye and mouth contours, and extract the working posture of the limb bones; Extraction and judgment of facial emotions, fatigue status, and working posture features: Machine learning combined with deep learning methods are used to calculate the ship operator's eye closure time, blinking frequency, yawning time and frequency, distraction, and facial expression information. It also calculates the operator's absenteeism, not looking at the screen, frequent nodding or chin-dragging, and sleeping posture, analyzes and evaluates the operator's working status, and then conducts graded early warning management.

5. The system according to claim 4, characterized in that: In the operator abnormal state analysis and calculation equipment, the operator's body surface problem information collected by the temperature infrared acquisition sensor is compared with the human body temperature limit values ​​such as fever and hypothermia to determine whether the operator is in a fever or hypothermia state, and then perform graded early warning management.

6. The system according to claim 5, characterized in that: The operator status management and decision-making terminal can present the operator's work status analysis results, historical data, statistical data, and graded early warning management results through the human-machine interface; it can send operator work status abnormality reminder instructions to the smart reminder watch and vibration reminder seat; it can intervene in the operator's work status abnormality through the organizational management level, and perform operator status assessment, suspension, and post adjustment intervention according to different levels. And the operator's work status monitoring information can be output to the upper-level information platform network of the entire ship through network signals.

7. The installation method of the abnormal working state monitoring and early warning system for ship operators as claimed in claim 6 is characterized in that: include: Arrange various acquisition components at appropriate locations in important operating compartments of the ship; Install the status signal acquisition and processing equipment in the important operation cabin of the ship, and connect all acquisition components to the status signal acquisition and processing equipment; Install the abnormal state analysis and calculation equipment, and connect the state signal acquisition and processing equipment and the abnormal state analysis and calculation equipment with a network cable; Install the operator status management and decision-making terminal in the operator working status management cabin and connect it to the abnormal status analysis and calculation equipment; System connection and debugging.

8. The working method of the abnormal working state monitoring and early warning system for ship operators as claimed in claim 6, characterized in that: include: System information collection, calculation, analysis and evaluation are all automated processes; The acquisition component continuously collects the facial, posture, voice, and body temperature information of operators in important operating cabins of the ship, and transmits it to the status signal acquisition and processing equipment through the signal line for centralized signal uniform conversion and processing; After unified processing, the data is transmitted to the operator abnormal status analysis and computing equipment through the network line to perform multi-task convolutional neural network calculations, machine learning, and deep learning to analyze and evaluate the operator's fatigue, abnormal emotions, lack of concentration, abnormal working posture, abnormal language, and physical abnormalities; The results are transmitted to the operator status management and decision-making terminal through the network line. The management personnel can analyze the operator's work status, historical data, statistical data, and graded warning management results through the operator status management and decision-making terminal human-machine interface.

9. The working method of the abnormal working state monitoring and early warning system for ship operators as claimed in claim 8, characterized in that: Also includes: Based on the calculation, analysis and statistical results, the operator's abnormal working status is warned in different levels; When the severity level of abnormal working conditions such as operator fatigue, abnormal emotions, lack of concentration, abnormal working posture, abnormal language, physical abnormalities, etc. is low, the operator's abnormal working status reminder instructions are sent to the smart reminder watch and vibration reminder seat; After receiving seat vibration, watch vibration or voice reminder, the operator will change the working status on his own.

10. The working method of the abnormal working state monitoring and early warning system for ship operators according to claim 8, characterized in that: When the severity level of abnormal working status is high, the frequency is high, and the number of historical statistical data is large, the management will intervene in the operator's abnormal working status management based on the results, and evaluate the operator status according to different levels. If the evaluation status passes, the operator can continue to operate on the job. If it is different, the operator will be suspended for a period of time for adjustment, or even intervene by reassigning the operator.

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