Double-fan operation authority control system and method based on face recognition

Abstract

The invention relates to the field of fan operation safety, and discloses a double-fan operation authority control system and method based on face recognition, and the system comprises an identity recognition module, a verification control module, a state feedback module, an authority management module and an abnormity monitoring module. The identity recognition module is used for collecting information of operating personnel and completing two-person in-place confirmation; the verification control module dynamically manages the double-person verification progress and adjusts an operation strategy; the state feedback module monitors the verification state in real time and generates a deviation signal for dynamically adjusting the verification strategy; the authority management module issues fan door authority and tool cabinet authority after verification is completed, and records an authority operation log; the abnormity monitoring module detects abnormal behaviors in the verification and permission using process, and when abnormity is detected, an alarm is triggered, and permission distribution is paused. According to the invention, the safety, verification efficiency and system reliability of fan operation are improved, and dynamic authority control and abnormity tracing functions are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine operation safety, and in particular to a two-person wind turbine operation authority control system and method based on face recognition. Background Art

[0002] With the rapid development of the wind power industry, the safety and standardization of wind turbine maintenance and operations have received increasing attention. The high-altitude operations, complex environments, and strict safety requirements of wind turbines make the identity authentication and permission management of operators an important part of ensuring operational safety. At present, most wind turbine operation scenarios still rely on traditional manual management methods, such as confirming the identity of operators through paper work tickets and distributing permissions through key management systems. However, these traditional methods have many problems and are difficult to meet the safety and efficiency requirements of modern operations.

[0003] Traditional means of operator identity authentication lack accuracy and real-time performance, which can easily lead to unauthorized personnel entering the operation area by mistake, increasing the safety risk of the operation. At the same time, permission distribution is usually based on static management, and it is impossible to flexibly adjust the scope of permissions according to actual operation needs, resulting in permission abuse or management loopholes. In addition, the existing wind turbine operation management methods lack the ability to monitor and handle abnormal behaviors in real time, and cannot effectively respond to emergencies, making it difficult to fully guarantee the safety of the operation process. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a two-person wind turbine operation authority control system and method based on face recognition. During the wind turbine operation process, a dynamic verification and authority control mechanism is used to ensure the identity accuracy of the two-person operation, the security of authority distribution, and the real-time monitoring and processing of abnormal behavior.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a two-person fan operation authority control system based on face recognition, comprising:

[0006] Identity recognition module, used to collect facial image data of two-person operators and QR code information of wind turbines to complete identity authentication and arrival confirmation;

[0007] A verification control module, which is used to dynamically control the progress of two-person verification based on the verification results of the operators, and synchronize the two-person verification by adjusting the verification control signal;

[0008] The status feedback module is used to monitor the identity verification progress in real time and make feedback adjustments to the verification control module based on the monitored verification status deviation;

[0009] The permission management module is used to dynamically issue fan door permissions and tool cabinet permissions after two-person identity verification is completed, and record permission operation logs;

[0010] The exception monitoring module is used to monitor abnormal behavior during the verification process, and issue an alarm signal when verification failure, verification delay or permission distribution abnormality is detected, suspend the verification process and notify relevant personnel.

[0011] Preferably, the identity recognition module includes:

[0012] A face recognition unit is used to collect facial images of operators and compare them with pre-registered facial information;

[0013] QR code scanning unit, used to scan the QR code of the fan to confirm the operation position;

[0014] The verification result output unit is used to output the verification pass or fail result to the verification control module.

[0015] Preferably, the verification control module is controlled based on two-person verification dynamic modeling, including:

[0016] Each staff member's verification progress changes over time and is dynamically adjusted based on the difference between their verification completion and another staff member's verification progress;

[0017] The verification control module generates a dynamic feedback signal according to the verification progress deviation to reduce the time difference between the two people's verification completion.

[0018] Preferably, the verification control module adjusts the strength of the personnel's operation signal so that the total time of two-person identity verification is less than the preset maximum allowed time.

[0019] Preferably, the state feedback module generates a verification progress deviation signal by comparing the verification state with the expected state in real time, including:

[0020] Verification progress deviation is defined as the difference between the current verification status and the target verification status;

[0021] When the deviation exceeds the preset threshold, an alarm signal is sent to the abnormal monitoring module.

[0022] Preferably, the state feedback module further uses a Kalman filter to dynamically estimate the environmental disturbances generated during the verification process to improve the monitoring accuracy of the verification progress deviation.

[0023] Preferably, the authority management module issues fan door authority or tool cabinet authority only when the following conditions are met:

[0024] Two-way identity verification is completed, and the difference in verification progress is less than the preset threshold;

[0025] The state feedback module detected no abnormal behavior.

[0026] Preferably, after the personnel complete the verification, the authority management module scans the tool cabinet to record the current tool status, re-scans and compares tool changes after the tools are collected and returned, and updates the records.

[0027] Preferably, the abnormality monitoring module includes:

[0028] Anomaly detection unit to monitor authentication failures, authentication delays, and permission distribution errors;

[0029] The exception recording unit is used to record the exception log and notify the management personnel to handle it.

[0030] The present invention also provides a two-person fan operation authority control method based on face recognition, comprising the following steps:

[0031] Initialize the identity information of the operators and confirm that both operators are in place through face recognition and QR code scanning;

[0032] Dynamically manage the verification progress of two people, and adjust the operation intensity according to the verification completion status of each person and the progress difference between the two people;

[0033] Monitor verification status in real time and generate deviation feedback signals to dynamically adjust verification strategies;

[0034] After verification is completed, the fan door permissions and tool cabinet permissions are issued;

[0035] Detect abnormal behavior during the verification process, trigger an alarm and suspend permission distribution when an abnormality is detected.

[0036] The present invention provides a two-person fan operation authority control system and method based on face recognition. It has the following beneficial effects:

[0037] 1. The present invention ensures that only verified authorized personnel can enter the fan operation area and use related tool cabinets through dynamic management of face recognition and two-person verification, effectively preventing misoperation or illegal entry by unauthorized personnel and improving the safety of fan operation.

[0038] 2. The present invention adopts dynamic verification progress management and real-time feedback mechanism, which can timely adjust the operation intensity and verification strategy according to the verification status of each operator, ensure that the verification progress of two people is synchronized and completed in the shortest time, and significantly improve the efficiency of the verification process.

[0039] 3. The present invention can dynamically issue fan door permissions and tool cabinet permissions according to the specific requirements of the work order through the permission management module, and strictly limit the scope of use and effective time of permissions to ensure the reasonable allocation of permissions and operational standardization.

[0040] 4. The present invention monitors abnormal behaviors in verification and permission operations in real time through the abnormal monitoring module, and triggers alarm signals and linkage processing when abnormalities are detected, which can quickly respond to potential risks and ensure the safety of the operation process and the stable operation of the system.

[0041] 5. The present invention records relevant operation logs in real time during the verification and permission use process, including verification status, permission distribution status and exception handling records, providing detailed data support for subsequent review and problem tracing, and enhancing the traceability and management efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0043] Figure 2 It is a schematic diagram of the method flow of the present invention;

[0044] Figure 3 This is a schematic diagram of the process of Example 1 of the present invention;

[0045] Figure 4 This is a schematic diagram of the process of Example 2 of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Please see attached Figure 1 The embodiment of the present invention provides a two-person wind turbine operation authority control system based on face recognition, which is used to ensure the safety and efficiency of two-person wind turbine operations. This system realizes identity authentication, authority distribution and operation process monitoring of two-person operations through modular designs such as identity recognition, dynamic control of face verification, authority management and abnormal monitoring, ensuring operation safety and compliance of authority operations. The following is a detailed description of the various modules of the system of the present invention.

[0048] Identity recognition module

[0049] The identity recognition module is the core component of the two-person wind turbine operation authority control system based on face recognition in the present invention. Its main function is to collect the identity information of the two-person operators and verify the presence of the operators through the combination of face image data and the wind turbine QR code. In general, the identity recognition module needs to verify the identity information of the two people at the same time and compare it with the pre-registered data to ensure that both people are authorized operators.

[0050] The technical implementation of this module combines facial image acquisition, feature extraction and comparison, QR code scanning and analysis, and other technical contents, and can adapt to complex operating scenarios, such as low-light environments, dynamic facial expressions, operating position offsets, etc. In practical applications, the output results of the identity recognition module directly affect the dynamic adjustment of the subsequent verification control module and the operation of the authority management module.

[0051] In this embodiment, the main functions of the identity recognition module include the following aspects:

[0052] After the operator arrives at the wind turbine operation site, the identity recognition module first collects the operator's facial image data and confirms the operation location by scanning the QR code on the wind turbine. The collected data will be compared with the personnel information pre-stored in the system database to confirm the identity of the personnel. Specifically, this module mainly includes a face collection unit, a feature comparison unit, and a QR code scanning unit.

[0053] In terms of face collection:

[0054] Generally, the identification module is equipped with a high-precision camera to collect facial image data of the operator. The camera supports multi-angle imaging and combines image enhancement algorithms to improve image quality under complex lighting conditions (such as strong light, low light or backlight). In some embodiments, the collected image will be denoised to remove dynamic blur interference that may exist during the collection process.

[0055] As an option, the face acquisition unit can continuously capture facial images in a real-time video stream and select the image frame with the highest definition in the video stream for subsequent processing. Specifically, the continuous frames in the video stream can be analyzed by an optical flow algorithm to select the image with the smallest motion blur.

[0056] In terms of facial feature comparison:

[0057] The identity recognition module uses a deep learning model to extract and compare features of the collected facial images. Generally, a pre-trained neural network model (such as a network based on the ResNet or MobileFaceNet architecture) is used to convert facial images into high-dimensional feature vectors. The facial information of each person at the time of registration is stored as a feature vector template. During the verification process, the feature vector collected in real time will be calculated with the template for cosine similarity. Generally, when the similarity exceeds the set threshold, it is considered that the identity authentication of the operator has passed.

[0058] As an option, the identity recognition module can extract features from the same face multiple times to reduce the impact of single recognition errors. For example, in one possible implementation, the module calculates the average value of features through multiple acquisitions to improve the stability of the verification result.

[0059] In some embodiments, the identity recognition module supports multi-angle shooting, and further improves the verification accuracy by fusing facial features at different angles. The module can also adjust recognition parameters according to environmental changes, such as automatically increasing the image exposure value when the light is too dark.

[0060] In terms of QR code scanning:

[0061] The QR code scanning unit is used to scan the QR code installed on the wind turbine to confirm whether the current location of the operator is consistent with the location assigned by the task. Generally, the QR code information includes data such as the wind turbine number and geographical location. In a possible implementation, the QR code information will be matched with the work order location assigned by the system in real time, and the personnel verification request will be rejected if the verification does not match.

[0062] As an option, the QR code scanning module supports multiple scanning methods, such as scanning through the operator's mobile terminal camera or a fixed scanning device installed on the wind turbine. Specifically, during the scanning process, the module will parse the QR code data and verify its integrity to prevent information loss due to QR code damage.

[0063] In this embodiment, the workflow of the identity recognition module is as follows:

[0064] 1. The operator arrives at the wind turbine site and uses the face acquisition unit to capture facial images.

[0065] 2. The system passes the collected image to the feature extraction module and calculates its feature vector.

[0066] 3. Compare the feature vector with the pre-registered facial information template in the system, calculate the similarity and determine the verification result.

[0067] 4. The operator scans the fan QR code to confirm whether the current position matches the task requirements.

[0068] 5. The results of face verification and QR code matching are used as the verification status and passed to the verification control module for further processing.

[0069] In one possible implementation, the identity recognition module detects abnormal situations, such as non-registered personnel attempting verification or damaged QR code information, and transmits such abnormal situations to the abnormal monitoring module.

[0070] This module combines advanced face recognition algorithms with QR code parsing technology. While ensuring the identity security of operators, it also provides reliable data support for the normal operation of subsequent verification control modules and permission management modules.

[0071] Verification Control Module

[0072] The main function of the verification control module is to dynamically manage the progress of two-person identity verification based on the identity verification results of the operator, coordinate the verification synchronization of the two people through the feedback mechanism, and ensure the efficiency and consistency of the verification process. Generally, this module needs to monitor the verification status of each operator in real time, dynamically adjust the operation signal, and provide the final verification result to the authority management module after the verification is completed.

[0073] After the aforementioned identity recognition module completes basic data collection, the verification control module combines verification dynamic modeling and feedback control technology to achieve collaborative operation of two-person verification. Specifically, the verification control module calculates the verification progress, generates adjustment signals, and processes verification deviations to ensure that the verification process of two-person operations meets the predetermined timeliness and safety requirements.

[0074] In this embodiment, the implementation of the verification control module includes the following steps:

[0075] The verification control module is based on dynamic modeling technology, which models the identity verification progress of each operator as a function of time. Each verification progress is described by the following dynamic equation:

[0076]

[0077] in:

[0078] x i (t) represents the verification completion progress of person i, and its value range is [0,1], where 1 means that the verification is completed.

[0079] k i To verify the speed factor, it is related to the personnel operating environment and equipment performance.

[0080] u i To control the signal strength, it reflects the degree of guidance the system provides to personnel verification behavior.

[0081] α represents the collaboration coefficient, which is used to describe the synchronization of the verification progress of two people.

[0082] x j Verification progress for another operator.

[0083] Generally, the verification control module monitors x i (t) changes in real time, adjust u iTo keep the two people's verification progress synchronized. During the verification process, the module will continue to calculate x i (t) and x j (t), dynamically generating the feedback signal Δu i :

[0084] Δu i =-K(x i (t)-x j (t))

[0085] Among them, K is the feedback gain coefficient, which is used to adjust the speed of reducing the verification progress difference, x j (t) represents the verification completion progress of person j, and its value range is [0,1], where 1 means that the verification is completed.

[0086] As an option, the module can also set an upper limit T on the time it takes to complete the verification. max Specifically, when the personnel verification time T i >T max When the module is in abnormal state, it will automatically trigger an abnormal signal and transmit the delay situation to the abnormal monitoring module.

[0087] In this embodiment, the verification control module implements dynamic verification adjustment through the following steps:

[0088] First, the verification control module receives the verification status data from the identity recognition module in real time, including the verification completion degree and current operation progress of each person. Generally, these data are broken down into the current verification speed and the remaining verification workload.

[0089] Secondly, the verification control module generates a control signal u based on the feedback control mechanism i , in order to dynamically adjust the operation behavior of personnel. For example, in one possible implementation, when the system detects that the verification progress of one person is significantly slower than that of another person, the module will increase the operation signal strength u of the slow person. i , in order to narrow the difference in verification progress between the two.

[0090] In this embodiment, the working logic of the verification control module in a specific situation is:

[0091] When the difference in the completion of two-person verification is Δx=|x i (t)-x j (t)|Exceeds the preset threshold Δx max When the verification progress of the slow person is slow, the verification control module will prioritize the verification progress of the slow person to narrow the difference. As an option, the collaboration coefficient α can be dynamically adjusted to enhance verification synchronization. For example, when the progress difference between the two people is large, α is increased to quickly synchronize the progress; when the progress is close to completion, α is reduced to avoid unnecessary overshoot.

[0092] In one possible implementation, the module also supports setting a priority-based verification strategy. For example, when a person's verification task is temporarily interrupted due to environmental influences (such as device delay or face acquisition failure), the system will prioritize the verification of another person to ensure the overall completion time of the verification.

[0093] In this embodiment, the verification control module also includes the following functions:

[0094] In one possible implementation, the verification control module can be linked with the permission management module to automatically trigger a permission distribution signal after detecting that two-person verification is completed, thereby shortening the response time between verification and permission issuance.

[0095] In general, in order to further improve verification efficiency, the module supports predicting the verification completion time of personnel through machine learning models. For example, based on historical data, the module can predict the verification speed change trend of a certain person and adjust the feedback signal in advance.

[0096] In this embodiment, the workflow of the verification control module is as follows:

[0097] 1. Verify status received

[0098] Generally, the verification control module receives data input from the identity recognition module after the operator enters the wind turbine operation area. Specifically, it includes:

[0099] Identity verification results (pass / fail) for each operator.

[0100] Real-time verification progress data x i (t), represents the proportion of verification completion.

[0101] The QR code scanned confirmation information ensures that personnel are in the correct working position.

[0102] In a possible implementation, the module also records the initial state x of each operator. i (0)=0, so as to track the verification progress later.

[0103] 2. Verify dynamic modeling

[0104] The verification control module initializes the dynamic verification model based on the verification status received in real time. Specifically:

[0105] The verification progress of each operator x i (t) is modeled as the aforementioned dynamic equation.

[0106] Set initial parameters:

[0107] k i: Verification speed factor, adjusted based on equipment performance and personnel historical verification records.

[0108] α: Collaboration coefficient, used to control the synchronization of verification progress between two operators.

[0109] In one possible implementation, the module dynamically adjusts these parameters based on real-time environmental data (such as network delay and device status). For example, when network delay is detected, k can be reduced. i The model was validated with balance.

[0110] 3. Progress monitoring and feedback signal generation

[0111] The module continuously monitors the verification progress of each operator during the verification process. Specifically:

[0112] By calculating the verification progress difference between the two people, Δx = |x i (t)-x j (t)|, determine whether the verification signal needs to be adjusted.

[0113] When Δx exceeds the preset synchronization threshold Δx max When the module generates a feedback signal Δu i , adjust the verification speed.

[0114] Feedback signal Δu i Directly acts on the verification control signal u i , to speed up the verification progress of slow personnel.

[0115] In one possible implementation, the module will also evaluate the completion time. i Exceeds the set upper limit T max , then give priority to increasing its feedback signal strength Δu i .

[0116] 4. Verification completion and result confirmation

[0117] When the verification progress of two operators is x i (t) and x j (t) When both reach 1 (i.e. verification is completed) and the progress difference is less than the threshold, the verification control module generates a verification completion signal.

[0118] Specifically:

[0119] The module outputs the verification completion status to the permission management module to trigger permission distribution.

[0120] At the same time, the timestamp T of verification completion is recorded i and related data to support subsequent log analysis.

[0121] In one possible implementation, the module checks whether the total verification time of the person is less than the set upper limit T max If the timeout occurs, the module will send the exception information to the exception monitoring module.

[0122] 5. Exception handling and interruption mechanism

[0123] When one of the following abnormal conditions is detected during the verification process, the module automatically triggers the interrupt mechanism:

[0124] The verification progress of a certain person did not reach the target status within the set time;

[0125] The difference in the verification progress of two people exceeds the synchronization threshold Δx for a long time max ;

[0126] Environmental disturbance correction failed and verification progress fluctuated abnormally.

[0127] The module's exception handling includes:

[0128] Pause the verification process and send an alarm signal to the abnormal monitoring module.

[0129] Records status information when an exception occurs.

[0130] In a possible implementation, the module will also trigger a re-verification process, requiring the abnormal person to restart the verification.

[0131] In general, the verification control module combines dynamic modeling, feedback control and environmental disturbance compensation technology to ensure the synchronization and timeliness of the two-person identity authentication process, and provides stable data support for the normal operation of subsequent modules.

[0132] Status Feedback Module

[0133] The main function of the state feedback module is to monitor the real-time status during the verification process, and provide accurate feedback signals based on the deviation analysis results, providing accurate data support for the verification control module. In general, this module can achieve high-precision monitoring of the verification process through state acquisition, deviation calculation and dynamic correction of the verification progress, which can significantly improve the reliability of monitoring, especially when there are environmental disturbances.

[0134] In order to improve the accuracy of the feedback signal, the state feedback module further combines the Kalman filter to dynamically estimate the random disturbances that may exist in the verification process to correct the verification state data. Specifically, the module generates a deviation signal δx(t) by real-time analysis of the difference between the verification progress and the expected state, and transmits the signal to the verification control module for dynamic adjustment of the verification strategy.

[0135] In this embodiment, the main functions of the state feedback module include:

[0136] In terms of condition monitoring:

[0137] In general, the status feedback module collects the verification progress status x of each operator in real time. i (t), and compare it with the preset target state x expected (t) is compared to generate a deviation signal δx(t). Specifically, the deviation is defined as the difference between the current verification state and the target state, and the calculation formula is:

[0138] δx(t)=x expected (t)-x measured (t)

[0139] in:

[0140] x expected (t) indicates the preset target verification status;

[0141] x measured (t) indicates the current actual verification status.

[0142] As an option, the module can calculate the progress difference Δx between the two operators to reflect the verification synchronization in response to the synchronization requirement of two-person verification:

[0143] Δx=|x i (t)-x j (t)|

[0144] When Δx exceeds the preset threshold Δx max When the module fails to reach the state of synchronization, it will mark the progress as out of sync and send a feedback signal to the verification control module.

[0145] In terms of environmental disturbance correction:

[0146] Specifically, in order to improve the accuracy of verification progress deviation monitoring, the state feedback module uses a Kalman filter to detect possible environmental disturbances w in the verification process. i (t) is dynamically estimated and corrected. The Kalman filter uses the state estimation method to estimate the actual state x of the system. i (t) and the disturbance w i (t) Perform iterative optimization. In general, the prediction and correction process of the Kalman filter includes the following steps:

[0147] Prediction stage:

[0148]

[0149]

[0150] in:

[0151] is the predicted value of the validation status;

[0152] is the predicted value of environmental disturbance;

[0153] A and B are the state transfer matrix and control input matrix respectively.

[0154] Correction phase:

[0155]

[0156] in:

[0157] K is the Kalman gain coefficient, which determines the magnitude of the correction;

[0158] x measured (t+1) is the actual measured verification state.

[0159] In a possible implementation, the state feedback module can dynamically adjust the size of K to adapt to different disturbance environments. For example, when the network fluctuates greatly, the module will increase K to enhance the ability to correct the real-time state.

[0160] In terms of feedback signal generation:

[0161] Generally, after completing state monitoring and disturbance correction, the state feedback module generates a feedback signal based on the corrected verification state. Specifically:

[0162] The deviation signal δx(t) is directly transmitted to the verification control module to adjust the verification strategy;

[0163] When the deviation exceeds the threshold, the module will mark the abnormal state and pass the relevant data to the abnormal monitoring module.

[0164] As an option, the status feedback module can also predict the verification completion time T based on the verification progress curve. i , to assist the verification control module in optimizing the control signal. For example, the module can verify the speed v i (defined as the time rate of change of verification progress Estimated verification time remaining:

[0165]

[0166] When the predicted verification is completed at time T i Exceeds the set maximum allowed time T max When an error occurs, the module will generate a warning signal to notify related modules for processing.

[0167] In terms of exception marking:

[0168] In one possible implementation, the state feedback module supports real-time anomaly detection. For example:

[0169] When the deviation signal δx(t) does not return to zero for a long time, the module will mark the verification timeout;

[0170] When the environmental disturbance w i (t) exceeds the allowable range w max , the module will mark the system disturbance as abnormal.

[0171] In this embodiment, the state feedback module also includes the following functions:

[0172] As an option, the module supports multi-task monitoring mode, which monitors the verification status of multiple groups of operators at the same time and generates independent feedback signals. For example, when multiple wind turbine operation tasks are carried out in parallel, the module can dynamically adjust the monitoring strategy based on the task priority.

[0173] In one possible implementation, the module can also combine machine learning technology to analyze historical data to optimize the feedback algorithm. For example, the module can predict typical verification deviation patterns based on past verification records, thereby generating compensation signals in advance and reducing the probability of abnormalities.

[0174] In this embodiment, the working process of the state feedback module is as follows:

[0175] 1. Status collection:

[0176] The status feedback module first receives real-time verification progress information from the verification control module, including the current verification status and verification completion of each operator. Generally, the module also synchronously receives the preset target status and verification progress threshold for subsequent deviation calculation.

[0177] 2. Deviation calculation:

[0178] The module compares the current verification status with the target status and calculates the verification progress deviation. If the verification progress difference exceeds the preset threshold, the module will mark the deviation status and provide a basis for the subsequent feedback signal generation.

[0179] 3. Disturbance correction:

[0180] The module combines environmental monitoring data to correct interference factors in the verification status. For example, in the case of network delay or equipment failure, the module will adjust the calculation results of the verification status to eliminate the impact of external disturbances on the verification progress monitoring.

[0181] 4. Feedback signal generation:

[0182] The corrected deviation information is converted into a feedback signal and transmitted to the verification control module. Generally, when generating a feedback signal, the module will dynamically adjust the signal strength according to the different verification states to improve the response efficiency of the verification control.

[0183] In general, this module significantly improves the accuracy and reliability of the verification process by monitoring the verification status, dynamically correcting disturbances, and providing real-time feedback of deviation signals, providing key data support for the efficient operation of the verification control module.

[0184] Rights Management Module

[0185] The main function of the permission management module is to dynamically issue operation permissions (including fan door permissions and tool cabinet permissions) after two-person identity verification is completed, and record all permission operation logs. In general, the permission management module needs to ensure that permissions are only issued when security conditions are met based on the verification results provided by the verification control module, thereby preventing unauthorized personnel from operating. At the same time, the module is also responsible for real-time recording of permission usage to provide data support for subsequent audits and exception analysis.

[0186] The permission management module realizes the dynamic binding of permission distribution and verification status through the collaboration with other modules, ensuring the validity of permissions and the safety of operations. Specifically, the module generates permission control signals according to the verification status, and monitors the operation of working tools and access control in real time during the use of permissions to improve the safety and traceability of the system.

[0187] In this embodiment, the main functions of the rights management module include the following aspects:

[0188] In terms of issuing permissions:

[0189] Generally, after receiving the verification completion signal, the permission management module dynamically issues the fan door permission and tool cabinet permission based on the work order information and permission rules of the job task.

[0190] When the two-person identity verification status meets the following conditions, the module generates a permission release signal:

[0191] ||x(t)-x * ||≤∈

[0192] Among them, x(t) represents the two-person verification state, x * represents the verification target state, and ∈ is the allowed deviation threshold.

[0193] In addition to verifying progress synchronization, permission distribution must also meet the specific requirements of the work order. For example, some work tasks may limit the use of specific tools, and the module needs to fine-tune the control of tool cabinet permissions.

[0194] As an option, the module supports a time-delay control function for permission issuance. For example, after the fan door permission is issued, the module will set a dynamic timer. If the operator does not enter the fan within the specified time, the permission will be automatically revoked to prevent abuse of permission.

[0195] In terms of tool cabinet management:

[0196] Specifically, the permission management module is linked with the sensing device of the tool cabinet to realize the whole process of tool use monitoring. The permission distribution process of the tool cabinet includes the following steps:

[0197] After the verification is completed, the module scans the status of the tools in the tool cabinet and records the current tool status.

[0198] After the tool cabinet door opening authority is issued, operators can take the required tools, and the module records the tool information taken in real time.

[0199] After the tool cabinet is closed, the module scans the tool status again, compares the tool changes before and after use, and records the list of tools used.

[0200] As an option, the module supports tool usage restriction. For example, for certain specific tasks, the module can set the tool access scope based on the work order content, allowing personnel to only take out tools within the authorized scope.

[0201] In terms of permission records:

[0202] Generally, the permission management module generates an operation log each time a permission is issued or a tool cabinet operation is completed, and stores the log information in the system database. The log content includes:

[0203] Timestamp of permission issuance;

[0204] Work order number;

[0205] Verify the identity of the person who has completed the verification;

[0206] Tool usage records (including tool numbers and return status).

[0207] Specifically, the log record format adopts a standardized structure to facilitate subsequent rapid query and data analysis.

[0208] In a possible implementation, the rights management module also supports a log encryption storage function to ensure the security of sensitive data.

[0209] In terms of exception handling:

[0210] Specifically, the permission management module monitors abnormal behavior in real time during the use of permissions.

[0211] For example:

[0212] When an unauthorized tool cabinet opening request is detected, the module immediately denies the operation and logs the abnormal behavior.

[0213] When the personnel fail to enter the fan on time after verification, or when the number of tools returned after operating the tool cabinet does not match the number taken out, the module will send a warning signal to the abnormal monitoring module.

[0214] As an option, the module supports automatic permission recovery. For example, after detecting an anomaly, the module can proactively revoke the permissions that have been issued and lock the relevant permission usage records for subsequent review.

[0215] In terms of dynamic permission adjustment:

[0216] In general, the permission management module dynamically adjusts the permission range according to the real-time status of the operation process. For example:

[0217] When the job task is expanded to a new fan location, the module will automatically adjust the scope of authority by receiving updated work order information and add new permissions for the corresponding fan door.

[0218] In specific work scenarios (such as emergency maintenance tasks), the module supports temporary extension of permissions, such as allowing access to all tools in the tool cabinet.

[0219] In a possible implementation, the module predicts possible permission adjustment requirements by combining historical data of the verification status. For example, for certain periodic tasks, the module can pre-set permission adjustment rules to reduce human intervention.

[0220] In this embodiment, the rights management module also includes the following functions:

[0221] As an option, the module supports integration with a Bluetooth key cabinet. Specifically, in Bluetooth key cabinet mode, operators can pair with the key cabinet through their mobile phones and directly receive Bluetooth permission signals from the permission management module to complete key collection and return operations.

[0222] In a possible implementation, the module also supports a multi-level review function for authority distribution. For example, for certain high-risk tasks, the authority management module needs to wait for the system administrator's confirmation signal before issuing the authority to enhance operational security.

[0223] In this embodiment, the workflow of the rights management module is as follows:

[0224] 1. Verify status reception:

[0225] The module receives the verification completion signal from the verification control module, and confirms whether the two-person verification status meets the conditions for issuing permissions in combination with the work order information of the job task.

[0226] 2. Authorization issuance:

[0227] When the verification status meets the requirements, the module dynamically issues the corresponding permissions, including fan door permissions and tool cabinet permissions. The scope and timeliness of permission issuance are strictly bound to the content of the work order task to ensure the accuracy of permission distribution.

[0228] 3. Operation records:

[0229] After each permission is issued or the tool cabinet is operated, the module generates an operation log, recording the timestamp, operator information, work order number and tool usage. The log data is stored in real time for subsequent review.

[0230] 4. Exception handling:

[0231] When unauthorized operation, abnormal use of the tool cabinet, or unused permissions are detected, the module immediately triggers an abnormal alarm, suspends permission distribution, and reports to the abnormal monitoring module.

[0232] In general, the permission management module provides complete technical support for the safety and traceability of two-person fan operations through dynamic permission distribution, tool cabinet management, permission recording and exception handling.

[0233] Abnormal monitoring module

[0234] The main function of the abnormal monitoring module is to monitor abnormal behaviors in the verification and permission operation process in real time through the abnormal detection unit and the abnormal recording unit, and mark and process the abnormal status. In general, this module can monitor problems such as verification delay, abnormal permission use, abnormal tool cabinet operation, and failure to use permissions in a timely manner, and trigger alarm signals based on the severity of the abnormality to ensure the security of the system and the standardization of operations.

[0235] The abnormal monitoring module works in conjunction with the verification control module, the authority management module, and the status feedback module to achieve real-time detection, dynamic processing, and recording and archiving of abnormal events. Specifically, the abnormal detection unit is used to identify abnormal states and generate alarm signals, and the abnormal recording unit is used to archive and store abnormal related information for subsequent analysis and tracing.

[0236] In this embodiment, the main functions of the abnormality monitoring module include the following aspects:

[0237] In terms of verification delay monitoring:

[0238] Generally, the abnormal monitoring module analyzes the verification progress data provided by the verification control module in real time through the abnormal detection unit. When the verification time of a certain operator exceeds the threshold set by the system (such as the maximum allowed verification time), the abnormal detection unit will mark it as a delay abnormality and generate an alarm message.

[0239] Specifically, the anomaly detection unit calculates the difference in the two-person verification progress in combination with the two-person verification synchronization requirement. If the progress difference exceeds the system allowable range (such as exceeding the preset time deviation threshold), the detection unit will trigger a synchronization anomaly signal and pass this abnormal state to the verification control module to adjust the verification strategy.

[0240] The abnormality recording unit will record the above abnormal conditions, including the time of occurrence, abnormality type and related operator information. The recorded log data can provide support for subsequent abnormality analysis.

[0241] In terms of permission usage monitoring:

[0242] The exception monitoring module is responsible for real-time monitoring of the permissions that have been issued, and the exception detection unit will continuously monitor the use of fan door permissions and tool cabinet permissions. For example, when the fan door permission has been issued but the operator does not enter the fan operation area within the specified time, the exception detection unit will mark it as an unused permission exception and generate a warning signal.

[0243] As an option, the anomaly detection unit can also monitor the use of tool cabinet permissions. For example, if it detects that the number of tools taken out after the tool cabinet is opened does not meet the work order requirements or the tools are not returned on time, the module will mark the tool usage as abnormal and notify the permission management module to adjust the permissions.

[0244] The exception recording unit will synchronously record these permission anomalies, including the time when the permission was issued, the usage behavior, and the actual permission operation. Specifically, the recorded data can be stored in a standardized log format for subsequent review.

[0245] In terms of exception recording and archiving:

[0246] The exception record unit is used to archive the complete process of abnormal events, including the time of event occurrence, abnormal type, related operators and system status. Generally, the exception record unit will classify events according to the severity of the abnormality. For example:

[0247] For verification delays and synchronization anomalies, the recording unit is marked as low priority.

[0248] In the case of unauthorized tool cabinet operation or failure to return tools on time, the recording unit will mark it as high priority and attach specific exception information.

[0249] In a possible implementation, the exception recording unit also supports encrypted storage of log data to prevent leakage of sensitive information.

[0250] In terms of linkage processing:

[0251] The abnormal monitoring module realizes linkage processing with other modules through the abnormal detection unit. For example:

[0252] When a verification delay anomaly is detected, the anomaly detection unit will notify the verification control module to dynamically adjust the verification signal strength of the slow person.

[0253] When an abnormal use of permissions is detected, the abnormality detection unit will send an abnormal signal to the permission management module to trigger the permission recovery mechanism.

[0254] In the event of an abnormal operation of the tool cabinet, the abnormality detection unit will lock the tool cabinet permissions to prevent further unauthorized operations.

[0255] At the same time, the exception recording unit will record the above linkage processing results in detail for subsequent tracking and analysis.

[0256] In terms of system expansion functions:

[0257] As an option, the Abnormal Monitoring Module can be linked to an external alarm device to alert operators to abnormal situations through sound alarms or light signals. For example, when the tool cabinet permissions are abnormally used, the module can trigger a sound alarm to immediately notify the on-site personnel.

[0258] In another possible implementation, the abnormality recording unit can generate an analysis report in combination with historical abnormality data to identify common abnormality patterns. For example, the recording unit can analyze the frequency and reasons for tool non-return situations to provide a reference for system optimization.

[0259] In general, this module provides strong support for system security and anomaly tracing through real-time monitoring of the anomaly detection unit and detailed archiving of the anomaly recording unit, while achieving efficient processing of abnormal states and system linkage.

[0260] In summary, the present invention verifies the facial image of the two-person operator and the fan QR code through the identity recognition module to ensure the accuracy of the personnel identity and the location information; dynamically manages the two-person verification progress through the verification control module, and adjusts the verification strategy to ensure synchronization; monitors the verification status in real time through the state feedback module, corrects the environmental disturbance and generates a deviation signal; dynamically issues the fan door authority and tool cabinet authority after the verification is completed through the authority management module, and records the operation log; detects abnormal behavior during the verification and authority use process through the abnormal monitoring module and triggers an alarm signal. The system realizes the safety, dynamism and traceability during the fan operation process, and provides an efficient authority control solution for two-person operation.

[0261] Please see attached Figure 2 The present invention also provides a two-person wind turbine operation authority control method based on face recognition to achieve dynamic management and safety control of wind turbine operation authority. The specific implementation methods of each step are described in detail below.

[0262] S1. Initialize the identity information of the operators and confirm that both operators are in place through face recognition and QR code scanning

[0263] In this implementation, the system first initializes the identity information of the operator, including facial information registration and work order information binding. After the operator arrives at the wind turbine operation site, the facial recognition device collects his / her facial image and compares it with the identity information pre-stored in the system database to verify the legitimacy of the operator's identity. At the same time, the operator scans the QR code on the wind turbine, and the system confirms the personnel's presence by parsing the QR code content.

[0264] S2. Dynamically manage the verification progress of two people, and adjust the operation intensity according to the verification completion status of each person and the progress difference between the two people.

[0265] In this embodiment, the system dynamically adjusts the verification strategy according to the real-time verification progress of the operator. The verification control module continuously analyzes the verification completion of each operator and monitors the difference in the verification progress of the two operators. When the verification progress of one party lags behind significantly, the system will dynamically adjust the operation intensity to increase the verification speed of the lagging party and ensure the synchronization of the verification progress of the two operators.

[0266] Generally, this step combines dynamic data in the real-time operating environment, such as human operation delays or equipment response speed, to optimize the dynamic management of the verification progress.

[0267] S3, real-time monitoring of verification status, and generating deviation feedback signals to dynamically adjust verification strategies

[0268] In this embodiment, the system monitors the verification status of each operator in real time through the status feedback module, and analyzes the deviation between the current verification progress and the target progress. When the system detects that the verification deviation exceeds the preset range, it will generate a feedback signal and transmit it to the verification control module for dynamic adjustment of the verification strategy.

[0269] S4. After verification is completed, the fan door permission and tool cabinet permission are issued

[0270] In this embodiment, when the system confirms that the two-person verification is completed, the fan door permission and tool cabinet permission will be issued according to the task requirements of the work order. The permission management module will dynamically set the scope and validity period of the permission to ensure the reasonable allocation of permissions.

[0271] As an option, the system will record the use of permissions in real time after permissions are issued, including the time when the fan door is opened and the operation records of the tool cabinet. These recorded data will be stored as permission logs for subsequent review and management.

[0272] S5. Detect abnormal behavior during the verification process, trigger an alarm and suspend permission distribution when an abnormality is detected

[0273] In this embodiment, the system monitors the verification process and permission usage in real time through the abnormal monitoring module. When abnormal behavior is detected, such as verification time exceeding the limit, improper use of the tool cabinet, or failure to use the fan door permission on time, the system will immediately trigger an alarm signal.

[0274] In general, the exception monitoring module will suspend the distribution of permissions, lock the current operation status and record the exception information. At the same time, the system will push the exception information to the management personnel for timely intervention and processing.

[0275] The method of the present invention can effectively manage the authority distribution during the two-person wind turbine operation process, and ensure the synchronization, security and standardization of verification and authority use.

[0276] Example 1: Confirmation before entering work

[0277] Please see attached Figure 3 In this embodiment, the system implements a two-person verification process to ensure the authenticity of the identity of the operator and confirmation of presence before the wind turbine operation. The following is the specific operation process:

[0278] Before the operation begins, Operator 1 and Operator 2 need to arrive at the designated wind turbine operation site and confirm their presence by scanning the QR code on the wind turbine. The identity recognition module is activated, and the facial images of the two operators are collected through a high-precision camera and compared with the facial information database pre-stored in the system. The system independently verifies the identities of the two persons, and only after both pass the identity verification, the verification control module generates a verification completion signal.

[0279] The verification control module monitors the verification progress of two people in real time to ensure that the verification status of the two people is completed synchronously. If the verification progress of one person lags behind, the system will dynamically adjust the operation signal to prompt the relevant personnel to re-operate or optimize the verification conditions. When the identity verification of the two people is completed, the authority management module issues the fan door authority to allow the two people to enter the fan to carry out operations. The entire verification process is monitored by the state feedback module to monitor the deviation signal to ensure the accuracy of the verification data.

[0280] In this embodiment, the abnormal monitoring module will also monitor the verification process in real time. If the two-person verification cannot be completed within the specified time or an abnormal situation occurs (such as face recognition failure or network delay), the module will trigger a warning signal and suspend the issuance of fan door permissions.

[0281] Example 2: Confirmation when leaving

[0282] Please see attached Figure 4 In this embodiment, the system implements a two-person verification departure confirmation process to ensure the integrity of records and the security of authority management after personnel leave the wind turbine. The following is the specific operation process:

[0283] After the operation is completed, Personnel 1 withdraws from the wind turbine and enters the departure confirmation process by scanning the QR code on the wind turbine. The system opens the face verification page, and the identity recognition module collects the facial image of Personnel 1 and performs identity comparison. After the verification is passed, the system prompts "Waiting for Personnel 2 to complete the verification." At the same time, the permission management module records the departure time of Personnel 1.

[0284] Personnel 2 then leaves the wind turbine and completes the departure confirmation through the same QR code scanning and face verification. If Personnel 2 fails to complete the verification within 15 minutes after Personnel 1 completes the verification, the abnormal monitoring module will trigger an alarm message, prompting Personnel 1 to verify the status of Personnel 2. The system will also synchronize the alarm information to the background management system for review by relevant managers.

[0285] During the verification process, the system allows Person 2 to re-verify until both persons complete the exit process. After confirming that both persons have left, the authority management module updates the authority usage record and automatically closes the fan door authority, completing the entire operation authority management process.

[0286] In this embodiment, the status feedback module is responsible for monitoring the real-time status of personnel departure confirmation and generating deviation signals for adjusting the verification strategy, while the abnormality monitoring module responds quickly to delays or operational abnormalities to ensure system security and integrity.

[0287] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A two-person wind turbine operation authority control system based on face recognition, characterized in that: include: Identity recognition module, used to collect facial image data of two-person operators and QR code information of wind turbines to complete identity authentication and arrival confirmation; A verification control module, which is used to dynamically control the progress of two-person verification based on the verification results of the operators, and synchronize the two-person verification by adjusting the verification control signal; The status feedback module is used to monitor the identity verification progress in real time and make feedback adjustments to the verification control module based on the monitored verification status deviation; The permission management module is used to dynamically issue fan door permissions and tool cabinet permissions after two-person identity verification is completed, and record permission operation logs; The exception monitoring module is used to monitor abnormal behavior during the verification process, and issue an alarm signal when verification failure, verification delay or permission distribution abnormality is detected, suspend the verification process and notify relevant personnel.

2. The two-person wind turbine operation authority control system based on face recognition according to claim 1 is characterized in that: The identity recognition module comprises: A face recognition unit is used to collect facial images of operators and compare them with pre-registered facial information; QR code scanning unit, used to scan the QR code of the fan to confirm the operation position; The verification result output unit is used to output the verification pass or fail result to the verification control module.

3. The two-person wind turbine operation authority control system based on face recognition according to claim 1 is characterized in that: The verification control module is controlled based on two-person verification dynamic modeling, including: Each staff member's verification progress changes over time and is dynamically adjusted based on the difference between their verification completion and another staff member's verification progress; The verification control module generates a dynamic feedback signal according to the verification progress deviation to reduce the time difference between the two people's verification completion.

4. The two-person wind turbine operation authority control system based on face recognition according to claim 1 is characterized in that: The verification control module adjusts the strength of the personnel's operation signal so that the total time of the two-person identity verification is less than the preset maximum allowed time.

5. The two-person wind turbine operation authority control system based on face recognition according to claim 1 is characterized in that: The state feedback module generates a verification progress deviation signal by comparing the verification state and the expected state in real time, including: Verification progress deviation is defined as the difference between the current verification status and the target verification status; When the deviation exceeds the preset threshold, an alarm signal is sent to the abnormal monitoring module.

6. The two-person wind turbine operation authority control system based on face recognition according to claim 1 is characterized in that: The state feedback module further uses a Kalman filter to dynamically estimate the environmental disturbances generated during the verification process to improve the monitoring accuracy of the verification progress deviation.

7. The two-person wind turbine operation authority control system based on face recognition according to claim 1 is characterized in that: The authority management module issues fan door authority or tool cabinet authority when the following conditions are met: Two-way identity verification is completed, and the difference in verification progress is less than the preset threshold; The state feedback module detected no abnormal behavior.

8. The two-person wind turbine operation authority control system based on face recognition according to claim 1 is characterized in that: After the personnel complete the verification, the authority management module scans the tool cabinet to record the current tool status, re-scans and compares tool changes after the tools are collected and returned, and updates the records.

9. The two-person wind turbine operation authority control system based on face recognition according to claim 1 is characterized in that: The abnormal monitoring module includes: Anomaly detection unit to monitor authentication failures, authentication delays, and permission distribution errors; The exception recording unit is used to record the exception log and notify the management personnel to handle it.

10. A two-person wind turbine operation authority control method based on face recognition, applied to a system as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Initialize the identity information of the operators and confirm that both operators are in place through face recognition and QR code scanning; Dynamically manage the verification progress of two people, and adjust the operation intensity according to the verification completion status of each person and the progress difference between the two people; Monitor verification status in real time and generate deviation feedback signals to dynamically adjust verification strategies; After verification is completed, the fan door permissions and tool cabinet permissions are issued; Detect abnormal behavior during the verification process, trigger an alarm and suspend permission distribution when an abnormality is detected.

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