An intelligent inspection system based on personnel positioning

Abstract

The present invention discloses an intelligent patrol inspection system based on personnel positioning, comprising an on-site information confirmation module, an operation information confirmation module, a risk analysis module, an evaluation and inspection module, and an early warning prompt module. The on-site information confirmation module collects on-site feedback information of the intelligent patrol inspection system, the operation information confirmation module collects operation environment feedback information of the intelligent patrol inspection system, the risk analysis module comprehensively analyzes the on-site feedback information and the operation environment feedback information, establishes a patrol inspection risk model, and uses a logistic regression method to calculate the delay balance index of the intelligent patrol inspection system. The evaluation and inspection module compares the calculated delay balance index with the preset delay balance index, and performs signal classification on the operation status of the intelligent patrol inspection system according to the comparison result. The early warning prompt module performs early warning processing according to the signal type of the intelligent patrol inspection system, which can effectively improve the patrol inspection efficiency and reduce the burden on the staff.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and more specifically, to an intelligent inspection system based on personnel positioning. Background Art

[0002] The intelligent inspection system based on personnel positioning can improve the safety management level and inspection efficiency of oil and gas transmission stations. Through 3D modeling technology, a real-life 3D model of the station is established, and the real-time monitoring screen is integrated with the 3D model to achieve one-picture management of the station's panoramic view. By applying video intelligent recognition algorithms, it can intelligently identify and alarm unsafe incidents in the station, and set up inspection routes and historical video backtracking modules to achieve automatic inspection of the station's panoramic view.

[0003] Improving inspection efficiency and accuracy relies on reliable inspection data collection and more on real-time and stable monitoring data. The evaluation of the real-time nature of monitoring data and combining real-time nature with risk assessment can further effectively improve inspection efficiency and reduce the workload of inspection personnel.

[0004] In order to solve the above defects, a technical solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent inspection system based on personnel positioning to solve the problems in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent inspection system based on personnel positioning, comprising an in-place information confirmation module, an operation information confirmation module, a risk analysis module, an evaluation and inspection module, and an early warning prompt module;

[0007] The in-place information confirmation module is used to collect the in-place feedback information of the intelligent inspection system and transmit the personnel location information to the risk analysis module;

[0008] The operation information confirmation module is used to collect the operation environment feedback information of the intelligent inspection system and transmit the operation environment feedback information to the risk analysis module;

[0009] The risk analysis module is used to conduct a comprehensive analysis of the in-place feedback information and the operating environment feedback information, establish an inspection risk model, and use the logistic regression method to calculate the delay balance index of the intelligent inspection system;

[0010] The evaluation and inspection module is used to compare the calculated delay balance index with the preset delay balance index, and classify the signal of the operation status of the intelligent inspection system according to the comparison result;

[0011] The early warning prompt module is used to perform early warning processing according to the signal type of the intelligent inspection system.

[0012] Preferably, the in-position feedback information is a positioning feedback efficiency coefficient, which is calculated as follows:

[0013] S101, obtaining a data collection period for position data monitoring by the intelligent inspection system within a time period T, and calibrating the data collection period as Tp, where p = {1, 2, 3 ... m}, and m is a positive integer;

[0014] The acquisition logic of the data collection cycle for the intelligent inspection system to obtain location data monitoring within time T is:

[0015] S1011, obtaining the time required for the location data monitoring request signal to be transmitted from the intelligent inspection system to the data analysis server, and calibrating the time required for the location data monitoring request signal to be transmitted from the intelligent inspection system to the data analysis server as Tc;

[0016] S1012, obtaining the time required for the data obtained by the data analysis server to be transmitted to the intelligent inspection system, and calibrating the time required for the data obtained by the data analysis server to be transmitted to the intelligent inspection system as Tb;

[0017] S1013, the calculation method of the data collection period Tp of the intelligent inspection system for obtaining the position data monitoring within the time T is Tp=Tc+Tb;

[0018] S102. Calculate the standard deviation Se of the data collection cycle of the intelligent inspection system to obtain the position data monitoring within the time T. The calculation expression of the standard deviation Se is: Where m is the total number of data collection cycles for location data monitoring by the intelligent inspection system within time T. is the average value of the data collection cycle of the intelligent inspection system for position data monitoring within T time, and its calculation expression is:

[0019] S103. Calculate the efficiency coefficient of positioning feedback obtained by the intelligent inspection system within T time. The calculation expression of the efficiency coefficient of positioning feedback is Lf = Se × exp ( Se 2 +1 ) .

[0020] Preferably, the operating environment feedback information is the operating data monitoring drift coefficient, and the calculation method is:

[0021] S201, obtaining frequency data of the intelligent inspection system monitoring the operating environment data within the cycle time T, and integrating the frequency data of the intelligent inspection system monitoring the operating environment data within the cycle time T into a data set and merging them into a data set marked as Pj, where j is the number of the frequency data, and j={1,2,3…k}, where k is a positive integer;

[0022] S202. Calculate the standard deviation of the working environment data monitoring frequency data. The calculation expression is: In the formula, is the standard deviation of the working environment data monitoring frequency data, and its calculation expression is:

[0023] S203, calculate the operation data monitoring drift coefficient, the expression of which is:

[0024] Preferably, the method of calculating the delay balance index of the intelligent inspection system using the logistic regression method is:

[0025] Establish an inspection risk model and use the logistic regression method to mark and classify the operating status of the intelligent inspection system;

[0026] Calculate the delay balance index Db of the intelligent inspection system, and its calculation expression is: Where R is a coefficient related to the total mileage of the inspection route, and R is greater than 0. α and β are proportional coefficients of the positioning feedback efficiency coefficient and the operation data monitoring drift coefficient, respectively, and α and β are both greater than 0.

[0027] Preferably, the logic of signal classification of the operation status of the intelligent inspection system according to the comparison result of the calculated delay balance index and the preset delay balance index is:

[0028] The evaluation and inspection module compares the calculated delay balance index of the intelligent inspection system with the preset delay balance index threshold. If the calculated delay balance index is greater than or equal to the preset delay balance index, a maintenance signal is generated; if the calculated delay balance index is less than the preset delay balance index, a stability signal is generated.

[0029] Preferably, the logic of early warning prompts according to the signal type of the intelligent inspection system is:

[0030] The early warning module performs processing strategy analysis based on the maintenance signal generated by the evaluation and inspection module. After receiving the maintenance signal generated by the evaluation and inspection module, the early warning module integrates a number of consecutive delay balance index data of the intelligent inspection system within T time after the maintenance signal is generated to generate a data set, and calibrates the delay balance index in the data set as Hd, where d is the delay balance index number, that is, d = {1, 2, 3...h}, where h is a positive integer;

[0031] Calculate the standard deviation of several delay balance indexes in the data set, and calibrate the standard deviation of the delay balance index as Pn, and compare the delay balance index standard deviation Pn with the preset delay balance index standard deviation threshold Pc, and perform early warning processing according to the comparison result. The processing logic is as follows:

[0032] If Pn is greater than or equal to Pc, the intelligent inspection system is marked as a high-risk level, prompting the staff that the intelligent inspection system has a high risk of error and needs to be inspected and maintained;

[0033] If Pn is less than Pc, the intelligent inspection system is marked as a low risk level, reminding the staff that the intelligent inspection system has a low error risk hazard and does not need to be inspected and maintained.

[0034] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0035] The present invention detects the delay balance index of the intelligent inspection system. When abnormalities are found in data processing stability and real-time performance, a comprehensive analysis is performed on the subsequent operation status of the intelligent inspection system, abnormal hidden dangers are determined, and early warning prompts are issued. On the one hand, it is convenient for staff to perceive abnormal hidden dangers in time and detect them in advance, effectively preventing the risk of untimely warning of potential faults and abnormalities caused by reduced stability of imaging effects, thereby effectively preventing system failures. On the other hand, it is convenient for staff to detect the operating status of the intelligent inspection system, facilitate detection management, and improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0037] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments 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.

[0039] Example 1

[0040] See also Figure 1 As shown, the present invention is an intelligent inspection system based on personnel positioning, including a location information confirmation module, an operation information confirmation module, a risk analysis module, an evaluation and inspection module, and an early warning prompt module;

[0041] The in-place information confirmation module is used to collect the in-place feedback information of the intelligent inspection system and transmit the personnel location information to the risk analysis module;

[0042] The operation information confirmation module is used to collect the operation environment feedback information of the intelligent inspection system and transmit the operation environment feedback information to the risk analysis module;

[0043] The risk analysis module is used to conduct a comprehensive analysis of the in-place feedback information and the operating environment feedback information, establish an inspection risk model, and use the logistic regression method to calculate the delay balance index of the intelligent inspection system;

[0044] The evaluation and inspection module is used to compare the calculated delay balance index with the preset delay balance index, and classify the signal of the operation status of the intelligent inspection system according to the comparison result;

[0045] The early warning prompt module is used to perform early warning processing according to the signal type of the intelligent inspection system.

[0046] The intelligent inspection system based on personnel positioning uses positioning technology and intelligent analysis tools to monitor and guide the location and actions of inspection personnel in real time. The system uses GPS, RFID, Wi-Fi, Bluetooth or other wireless communication technologies to track the location of inspection personnel in real time, and can provide accurate indoor and outdoor positioning information to ensure personnel safety and optimize inspection routes. Inspection personnel are usually equipped with smart phones, tablets or dedicated mobile terminal devices, and the devices are installed with special applications for receiving task instructions, recording inspection data and real-time feedback on inspection conditions. The system background is integrated with data analysis and artificial intelligence algorithms, which can intelligently plan inspection routes based on personnel positioning information and historical inspection data, predict potential maintenance problems, and issue warnings when necessary. The system supports real-time communication functions, allowing inspection personnel to communicate with the command center or other personnel by voice or text, and report problems found or request support in a timely manner. The system can store and manage inspection data, including inspection records, problem reports, maintenance history, etc., to provide data support for maintenance decisions;

[0047] The intelligent inspection system based on personnel positioning relies on accurate and real-time personnel positioning information to plan inspection routes and conducts safety assessments of inspection risks based on real-time working environment monitoring of inspection personnel.

[0048] If the intelligent inspection system takes too long to confirm the feedback of personnel location information, the following adverse effects may occur:

[0049] Reduced efficiency: If the location information collection time is too long, the inspectors may waste time while waiting, thus reducing the overall inspection efficiency. The inspectors may not be able to perform the next task in time while waiting for the location information to be updated, affecting the inspection progress.

[0050] Safety risks: In some high-risk industrial environments, fast and accurate personnel positioning is essential to ensure the safety of patrol personnel. If the positioning information is collected late, it may increase the time personnel stay in dangerous areas and increase the risk of safety accidents.

[0051] Slow response: In emergency situations, such as equipment failure or unexpected events, it is necessary to quickly locate the inspection personnel to take appropriate measures. If the location information collection time is too long, it may delay the emergency response, affecting the handling of accidents and the rescue of personnel.

[0052] Inaccurate data: The real-time nature of positioning information is very important to ensure data accuracy. Long collection time may cause positioning data to be inconsistent with the actual situation, thus affecting inspection quality and decision-making.

[0053] Degraded user experience: Patrol personnel rely on the intelligent patrol system to carry out their daily work. If the system responds slowly, it may reduce the staff's trust and satisfaction with the system, thereby affecting their work efficiency and the promotion and use of the system.

[0054] Increased costs: Long time spent collecting positioning information may cause inspectors to require additional time and resources to complete their tasks, thereby increasing the company's operating costs.

[0055] Difficulty in data analysis: Intelligent inspection systems usually need to analyze a large amount of real-time data to optimize inspection routes and improve efficiency. If the positioning data is delayed, it may affect the accuracy and timeliness of data analysis, thereby affecting the intelligence level of the system.

[0056] The personnel positioning information of the intelligent inspection system is evaluated by calculating the positioning feedback efficiency coefficient. The calculation process is as follows:

[0057] S101, obtaining a data collection period for position data monitoring by the intelligent inspection system within a time period T, and calibrating the data collection period as Tp, where p = {1, 2, 3 ... m}, and m is a positive integer;

[0058] The acquisition logic of the data collection cycle for the intelligent inspection system to obtain location data monitoring within time T is:

[0059] S1011, obtaining the time required for the location data monitoring request signal to be transmitted from the intelligent inspection system to the data analysis server, and calibrating the time required for the location data monitoring request signal to be transmitted from the intelligent inspection system to the data analysis server as Tc;

[0060] S1012, obtaining the time required for the data obtained by the data analysis server to be transmitted to the intelligent inspection system, and calibrating the time required for the data obtained by the data analysis server to be transmitted to the intelligent inspection system as Tb;

[0061] S1013, the calculation method of the data collection period Tp of the intelligent inspection system for obtaining the position data monitoring within the time T is Tp=Tc+Tb;

[0062] It should be pointed out that the time required for the location data monitoring request signal to be transmitted from the intelligent inspection system to the data analysis server and the time required for the data obtained by the data analysis server to be transmitted to the intelligent inspection system are both obtained through the working process log of the intelligent inspection system;

[0063] S102. Calculate the standard deviation Se of the data collection cycle of the intelligent inspection system to obtain the position data monitoring within the time T. The calculation expression of the standard deviation Se is: Where m is the total number of data collection cycles for location data monitoring by the intelligent inspection system within time T. is the average value of the data collection cycle of the intelligent inspection system for position data monitoring within T time, and its calculation expression is:

[0064] S103. Calculate the efficiency coefficient of positioning feedback obtained by the intelligent inspection system within T time. The calculation expression of the efficiency coefficient of positioning feedback is Lf = Se × exp ( Se 2 +1 ) .

[0065] From the expression of the positioning feedback efficiency coefficient, it can be seen that the larger the positioning feedback efficiency coefficient of the intelligent inspection system within time T, the worse the real-time performance of the intelligent inspection system in processing position data, and the greater the risk of inaccurate and intime warning of potential faults and abnormalities. Conversely, the better the real-time performance of the intelligent inspection system in processing position data, the more accurate and timely the response to potential faults and abnormal warnings.

[0066] If the intelligent inspection system monitors the working environment status collected by the inspectors too infrequently, the following adverse effects may occur:

[0067] Untimely risk warning: Environmental monitoring data is crucial for early warning of potential safety risks. Too low a monitoring frequency may result in failure to detect environmental changes in a timely manner, such as toxic gas leaks, abnormal temperature rise, etc., and thus failure to issue early warnings, increasing the risk of accidents.

[0068] Delayed emergency response: In an emergency, rapid environmental data updates are essential for developing effective emergency response measures. Insufficient monitoring frequency may result in emergency decisions being based on outdated or inaccurate data, affecting the efficiency and effectiveness of emergency response.

[0069] Decreased inspection quality: Environmental data monitoring is an important part of inspection work. Low monitoring frequency may result in inspectors being unable to obtain sufficient environmental information, making it impossible to conduct comprehensive inspections, affecting the quality of inspection work.

[0070] Difficulty in equipment maintenance: Environmental data is instructive for the operating status and maintenance needs of equipment. Insufficient monitoring frequency may result in failure to detect abnormalities in equipment operation in a timely manner, delaying maintenance and repairs, and increasing the risk of equipment failure.

[0071] Compliance issues: For some industries and locations, the frequency and quality of environmental monitoring data may be subject to legal and regulatory requirements. Too low a monitoring frequency may result in an enterprise failing to meet compliance requirements, facing legal risks and economic losses.

[0072] Inaccurate data analysis: Intelligent inspection systems usually rely on a large amount of real-time data for analysis and optimization. The low frequency of environmental monitoring data may lead to inaccurate data analysis, affecting the system's intelligent optimization of inspection routes and strategies.

[0073] Increased safety risks for personnel: Environmental monitoring data is crucial to ensure the safety of inspection personnel. Low monitoring frequency may result in failure to timely detect environmental factors that are harmful to personnel health, increasing safety risks for personnel.

[0074] The operating environment information collection efficiency of the intelligent inspection system is evaluated by calculating the operating data monitoring drift coefficient of the intelligent inspection system. The calculation process is as follows:

[0075] S201, obtaining frequency data of the intelligent inspection system monitoring the operating environment data within the cycle time T, and integrating the frequency data of the intelligent inspection system monitoring the operating environment data within the cycle time T into a data set and merging them into a data set marked as Pj, where j is the number of the frequency data, and j={1,2,3…k}, where k is a positive integer;

[0076] It should be noted that the operating environment data is collected through the background status data of the intelligent inspection system. The operating environment data includes environmental analysis, sampling points, compliance verification of environmental analysis times, and data from gas detectors carried by inspection personnel;

[0077] S202. Calculate the standard deviation of the working environment data monitoring frequency data. The calculation expression is: In the formula, is the standard deviation of the working environment data monitoring frequency data, and its calculation expression is:

[0078] S203, calculate the operation data monitoring drift coefficient, the expression of which is:

[0079] From the expression of the operation data monitoring drift coefficient, it can be seen that the more discrete the frequency data of the intelligent inspection system monitoring the operation environment data within the cycle time T, that is, the larger the operation data monitoring drift coefficient, the worse the operation stability and data accuracy of the intelligent inspection system. Conversely, the smaller the operation data monitoring drift coefficient generated by the intelligent inspection system within the cycle time T, the better the operation stability and data accuracy of the intelligent inspection system.

[0080] Establish an inspection risk model and use the logistic regression method to mark and classify the operating status of the intelligent inspection system;

[0081] Calculate the delay balance index Db of the intelligent inspection system, and its calculation expression is: In the formula, R is a coefficient related to the total mileage of the inspection route, and R is greater than 0. α and β are proportional coefficients of the positioning feedback efficiency coefficient and the operation data monitoring drift coefficient, respectively, and α and β are both greater than 0.

[0082] From the calculation formula of the delay balance index, it can be seen that the greater the positioning feedback efficiency coefficient of the intelligent inspection system, the greater the operating data monitoring drift coefficient, that is, the smaller the delay balance index of the intelligent inspection system, the worse the stability of the intelligent inspection system in data processing. Conversely, the smaller the positioning feedback efficiency coefficient of the intelligent inspection system, the smaller the operating data monitoring drift coefficient, that is, the greater the delay balance index of the intelligent inspection system, the better the stability of the intelligent inspection system in data processing.

[0083] The evaluation and inspection module compares the calculated delay balance index of the intelligent inspection system with a preset delay balance index threshold, and generates a maintenance signal if the calculated delay balance index is greater than or equal to the preset delay balance index; and generates a stable signal if the calculated delay balance index is less than the preset delay balance index;

[0084] The early warning module performs processing strategy analysis based on the maintenance signal generated by the evaluation and inspection module. After receiving the maintenance signal generated by the evaluation and inspection module, the early warning module integrates a number of consecutive delay balance index data of the intelligent inspection system within T time after the maintenance signal is generated to generate a data set, and calibrates the delay balance index in the data set as Hd, where d is the delay balance index number, that is, d = {1, 2, 3...h}, where h is a positive integer;

[0085] Calculate the standard deviation of several delay balance indexes in the data set, and calibrate the standard deviation of the delay balance index as Pn, and compare the delay balance index standard deviation Pn with the preset delay balance index standard deviation threshold Pc, and perform early warning processing according to the comparison result. The processing logic is as follows:

[0086] If Pn is greater than or equal to Pc, the intelligent inspection system is marked as a high-risk level, prompting the staff that the intelligent inspection system has a high risk of error and needs to be inspected and maintained;

[0087] If Pn is less than Pc, the intelligent inspection system is marked as a low risk level, reminding the staff that the intelligent inspection system has a low error risk hazard and does not need to be inspected and maintained.

[0088] The present invention detects the delay balance index of the intelligent inspection system. When abnormalities are found in data processing stability and real-time performance, a comprehensive analysis is performed on the subsequent operation status of the intelligent inspection system, abnormal hidden dangers are determined, and early warning prompts are issued. On the one hand, it is convenient for staff to perceive abnormal hidden dangers in time and detect them in advance, effectively preventing the risk of untimely warning of potential faults and abnormalities caused by reduced stability of imaging effects, thereby effectively preventing system failures. On the other hand, it is convenient for staff to detect the operating status of the intelligent inspection system, facilitate detection management, and improve work efficiency.

[0089] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0090] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of computer program goods. The computer program goods include one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0091] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the method described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

Claims

1. An intelligent inspection system based on personnel positioning, characterized in that: It includes the in-place information confirmation module, the operation information confirmation module, the risk analysis module, the evaluation and inspection module, and the early warning prompt module; The in-place information confirmation module is used to collect the in-place feedback information of the intelligent inspection system and transmit the personnel location information to the risk analysis module; The operation information confirmation module is used to collect the operation environment feedback information of the intelligent inspection system and transmit the operation environment feedback information to the risk analysis module; The risk analysis module is used to conduct a comprehensive analysis of the in-place feedback information and the working environment feedback information, establish an inspection risk model, and use the logistic regression method to calculate the delay balance index of the intelligent inspection system; The evaluation and inspection module is used to compare the calculated delay balance index with the preset delay balance index, and classify the signal of the operation status of the intelligent inspection system according to the comparison result; The early warning prompt module is used to perform early warning processing according to the signal type of the intelligent inspection system; The in-position feedback information is the positioning feedback efficiency coefficient, which is calculated as follows: S101, obtaining a data collection period for position data monitoring by the intelligent inspection system within a time period T, and calibrating the data collection period as Tp, where p = {1, 2, 3 ... m}, and m is a positive integer; The acquisition logic of the data collection cycle for the intelligent inspection system to obtain location data monitoring within time T is: S1011, obtaining the time required for the location data monitoring request signal to be transmitted from the intelligent inspection system to the data analysis server, and calibrating the time required for the location data monitoring request signal to be transmitted from the intelligent inspection system to the data analysis server as Tc; S1012, obtaining the time required for the data obtained by the data analysis server to be transmitted to the intelligent inspection system, and calibrating the time required for the data obtained by the data analysis server to be transmitted to the intelligent inspection system as Tb; S1013, the calculation method of the data collection period Tp of the intelligent inspection system for obtaining the position data monitoring within the time T is Tp=Tc+Tb; S102. Calculate the standard deviation Se of the data collection cycle of the intelligent inspection system to obtain the position data monitoring within the time T. The calculation expression of the standard deviation Se is: Where m is the total number of data collection cycles for location data monitoring by the intelligent inspection system within time T. is the average value of the data collection cycle of the intelligent inspection system for position data monitoring within T time, and its calculation expression is: S103, calculate the efficiency coefficient of positioning feedback obtained by the intelligent inspection system within T time, and the calculation expression of the efficiency coefficient of positioning feedback is Lf = Se × exp (Se 2 +1); The feedback information of the working environment is the working data monitoring drift coefficient, which is calculated as follows: S201, obtaining frequency data of the intelligent inspection system monitoring the operating environment data within the cycle time T, and integrating the frequency data of the intelligent inspection system monitoring the operating environment data within the cycle time T into a data set and merging them into a data set marked as Pj, where j is the number of the frequency data, and j={1,2,3…k}, where k is a positive integer; S202. Calculate the standard deviation of the working environment data monitoring frequency data. The calculation expression is: In the formula, is the standard deviation of the working environment data monitoring frequency data, and its calculation expression is: S203, calculate the operation data monitoring drift coefficient, the expression of which is: The method of calculating the delay balance index of the intelligent inspection system using the logistic regression method is: Establish an inspection risk model and use the logistic regression method to mark and classify the operating status of the intelligent inspection system; Calculate the delay balance index Db of the intelligent inspection system, and its calculation expression is: In the formula, R is a coefficient related to the total mileage of the inspection route, and R is greater than 0. α and β are proportional coefficients of the positioning feedback efficiency coefficient and the operation data monitoring drift coefficient, respectively, and α and β are both greater than 0. The logic of signal classification of the operation status of the intelligent inspection system according to the comparison result of the calculated delay balance index and the preset delay balance index is: The evaluation and inspection module compares the calculated delay balance index of the intelligent inspection system with the preset delay balance index threshold. If the calculated delay balance index is greater than or equal to the preset delay balance index, a maintenance signal is generated; if the calculated delay balance index is less than the preset delay balance index, a stability signal is generated.

2. The intelligent inspection system based on personnel positioning according to claim 1 is characterized in that: The logic of early warning prompts based on the signal type of the intelligent inspection system is: The early warning module performs processing strategy analysis based on the maintenance signal generated by the evaluation and inspection module. After receiving the maintenance signal generated by the evaluation and inspection module, the early warning module integrates a number of consecutive delay balance index data of the intelligent inspection system within T time after the maintenance signal is generated to generate a data set, and calibrates the delay balance index in the data set as Hd, where d is the delay balance index number, that is, d = {1, 2, 3...h}, where h is a positive integer; Calculate the standard deviation of several delay balance indexes in the data set, and calibrate the standard deviation of the delay balance index as Pn, and compare the delay balance index standard deviation Pn with the preset delay balance index standard deviation threshold Pc, and perform early warning processing according to the comparison result. The processing logic is as follows: If Pn is greater than or equal to Pc, the intelligent inspection system is marked as a high-risk level, prompting the staff that the intelligent inspection system has a high risk of error and needs to be inspected and maintained; If Pn is less than Pc, the intelligent inspection system is marked as a low risk level, reminding the staff that the intelligent inspection system has a low error risk hazard and does not need to be inspected and maintained.

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