AI-based remote monitoring and fault diagnosis methods, systems, and media for self-service police terminals

Abstract

This application provides a method, system, and medium for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence. The method includes: acquiring real-time monitoring status information of the police terminal; preprocessing the real-time monitoring status information to obtain optimized monitoring data for the police terminal; analyzing the optimized monitoring data based on machine learning algorithms to obtain operational data of the police terminal; comparing the operational data of the police terminal with set operational data to obtain an operational deviation rate; determining whether the operational deviation rate is greater than or equal to a set deviation rate threshold; if it is greater than or equal to, generating a matching maintenance suggestion; if it is less than, generating monitoring data for the police terminal; by analyzing the operational data of the police terminal, the police terminal is monitored in real time, thereby accurately analyzing the operational safety of the police terminal in real time, and when the police terminal malfunctions, providing a matching maintenance suggestion to ensure the normal operation of the police terminal.

Description

Technical Field

[0001] This application relates to the field of police terminal monitoring technology, and more specifically, to a method, system, and medium for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence. Background Technology

[0002] Currently, processing documents such as driver's licenses, vehicle registration certificates, temporary license plates, and certificates of conformity requires cooperation between the police and the public. If either party fails to respond promptly, it will delay the processing time, which is highly detrimental to both parties. Moreover, users often have to wait a long time to receive their documents.

[0003] Existing self-service police terminals cannot be remotely monitored, making it difficult to accurately analyze the operational safety of the terminals in real time. When a malfunction occurs, no appropriate maintenance suggestions can be provided, affecting the operational safety of the terminals. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, and medium for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence. By analyzing the operational data of the police terminals, the system can monitor the police terminals in real time, thereby accurately analyzing the operational safety of the police terminals in real time. When an abnormality occurs in the police terminals, the system can provide matching maintenance suggestions to ensure the normal operation of the police terminals.

[0005] This application also provides a method for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence, including: Obtain real-time monitoring status information from police terminals, preprocess the real-time monitoring status information, and obtain optimized monitoring data for police terminals; Based on machine learning algorithms, the monitoring and optimization data of police terminals are analyzed to obtain the operation data of the police terminals. The operation data of the police terminals is compared with the set operation data to obtain the operation deviation rate. Determine whether the operating deviation rate is greater than or equal to the set deviation rate threshold; If the value is greater than or equal to the value, fault information is generated, a fault report is generated based on the fault information, and matching maintenance suggestions are generated based on the fault report. If the value is less than the specified value, then police terminal monitoring data is generated and transmitted to the terminal in real time. This includes generating a fault report based on fault information, and generating matching maintenance suggestions based on the fault report, specifically including: Obtain the parameter information of the police terminal and establish a police terminal node. The parameter information of the police terminal includes the model of the police terminal, the function of the police terminal, and the usage scenario of the police terminal. The location information of several police terminals is obtained, and the police terminal nodes are grouped based on the location information of the police terminals to generate multiple police terminal node clusters. Each police terminal node cluster includes at least two member nodes and at least one backup node. The member nodes are police terminal nodes, and the police terminal node cluster includes a cluster head node. Multiple police terminal node clusters are connected by gateway nodes. A police terminal operation logic network is formed based on the cluster head node and the gateway node. The operation status information of member nodes within the police terminal node cluster is queried based on the police terminal operation logic network, and the fault information of member nodes is analyzed to obtain the location of faulty nodes. The neighboring nodes of the fault node are generated based on the location of the fault node. The neighboring nodes are other member nodes with the location of the fault node as the center and the radius is smaller than a set range threshold. Based on the fault reports sent by neighboring nodes to the cluster head node, a link transmission protocol is established between the neighboring nodes and the faulty node, and each police terminal node has a unique identifier to identify the faulty node and the neighboring nodes. Based on the fault report analysis, the fault type of the fault node is analyzed. The fault types include protocol address fault, transmission link fault, or transmission path break of the fault node. A local fault recovery strategy is established based on the fault type. A maintenance setup is generated based on the local fault recovery strategy, and a backup node is started. The transmission link of the backup node is connected to the fault node for network communication and the backup node is replaced. The faulty node is disconnected and repaired. After the repair is completed, the successfully repaired faulty node is reconnected to the police terminal node cluster, and the backup node is disconnected.

[0006] Specifically, by clustering the police terminal nodes, the entire police terminal network is divided into multiple police terminal node clusters. This enables interconnection between the police terminal node clusters, while each police terminal node cluster analyzes and manages the fault status of multiple police terminal nodes through a cluster head node. This achieves distributed management of several police terminal nodes, improving the efficiency and accuracy of fault analysis. When a police terminal node fails, a backup node can be quickly activated within the police terminal node cluster to replace the faulty node, achieving rapid local fault recovery and improving fault recovery response speed.

[0007] Optionally, in the AI-based remote monitoring and fault diagnosis method for self-service police terminals described in this application embodiment, obtaining real-time monitoring status information of the police terminal and preprocessing the real-time monitoring status information to obtain optimized monitoring data for the police terminal specifically includes: Obtain real-time monitoring status information from police terminals and extract data features; The data features are normalized to obtain optimized data features; Based on optimized feature analysis, duplicate data between monitoring status information of adjacent time nodes; Duplicate data is removed to obtain optimized monitoring data from police terminals.

[0008] Optionally, in the AI-based remote monitoring and fault diagnosis method for self-service police terminals described in this application embodiment, the police terminal operation data is obtained by analyzing the monitoring optimization data based on machine learning algorithms, specifically including: A learning model is established based on machine learning algorithms, and a training set is established based on historical police terminal monitoring data. The learning model is trained based on the training set to obtain the training results; Determine whether the training results have converged; If convergence is achieved, a police terminal analysis model is generated. Based on the police terminal analysis model, the police terminal monitoring optimization data is analyzed to obtain the police terminal operation data. If the learning model does not converge, the parameters of the learning model are optimized until the learning model converges.

[0009] Optionally, in the AI-based remote monitoring and fault diagnosis method for self-service police terminals described in this application embodiment, the operation data of the police terminal is compared with the set operation data to obtain the operation deviation rate, specifically including: Acquire police terminal operation data, compare the police terminal operation data with the set operation data, and obtain data difference information; The data difference information is compared with the set data difference threshold, which includes a first data difference threshold and a second data difference threshold, and the first data difference threshold is less than the second data difference threshold. If the data difference is greater than the first data difference threshold but less than the second data difference threshold, then a first operational deviation rate is generated. If the data difference information is greater than or equal to the second data difference threshold, then a second operational deviation rate is generated.

[0010] Optionally, in the AI-based remote monitoring and fault diagnosis method for self-service police terminals described in this application embodiment, if the fault is greater than or equal to a certain value, fault information is generated, a fault report is generated based on the fault information, and matching maintenance suggestions are generated based on the fault report. Specifically, this includes: Obtain fault information and analyze the fault type based on the fault information; Analyze the fault types and generate different types of fault reports; The system analyzes fault types and corresponding fault reports, and generates maintenance recommendations based on an expert database.

[0011] Optionally, in the AI-based remote monitoring and fault diagnosis method for self-service police terminals described in this application embodiment, if the value is less than a certain threshold, then police terminal monitoring data is generated, and the police terminal monitoring data is transmitted to the terminal in real time, specifically including: Acquire monitoring data from police terminals and analyze the monitoring type of the data. Obtain the type of transmission link, match the type of transmission link with the type of monitoring data, and obtain the matching degree; Determine whether the matching degree is greater than or equal to the set matching degree threshold; If the value is greater than or equal to the set matching threshold, a matching transmission link is obtained and the monitoring data of the police terminal is transmitted to the terminal. If it is less than or equal to, then change the transmission link.

[0012] Secondly, embodiments of this application provide an artificial intelligence-based remote monitoring and fault diagnosis system for self-service police terminals. The system includes a memory and a processor. The memory includes a program for an artificial intelligence-based remote monitoring and fault diagnosis method for self-service police terminals. When executed by the processor, the program for the artificial intelligence-based remote monitoring and fault diagnosis method for self-service police terminals implements the following steps: Obtain real-time monitoring status information from police terminals, preprocess the real-time monitoring status information, and obtain optimized monitoring data for police terminals; Based on machine learning algorithms, the monitoring and optimization data of police terminals are analyzed to obtain the operation data of the police terminals. The operation data of the police terminals is compared with the set operation data to obtain the operation deviation rate. Determine whether the operating deviation rate is greater than or equal to the set deviation rate threshold; If the value is greater than or equal to the value, fault information is generated, a fault report is generated based on the fault information, and matching maintenance suggestions are generated based on the fault report. If the value is less than the specified value, then police terminal monitoring data is generated and transmitted to the terminal in real time. This includes generating a fault report based on fault information, and generating matching maintenance suggestions based on the fault report, specifically including: Obtain the parameter information of the police terminal and establish a police terminal node. The parameter information of the police terminal includes the model of the police terminal, the function of the police terminal, and the usage scenario of the police terminal. The location information of several police terminals is obtained, and the police terminal nodes are grouped based on the location information of the police terminals to generate multiple police terminal node clusters. Each police terminal node cluster includes at least two member nodes and at least one backup node. The member nodes are police terminal nodes, and the police terminal node cluster includes a cluster head node. Multiple police terminal node clusters are connected by gateway nodes. A police terminal operation logic network is formed based on the cluster head node and the gateway node. The operation status information of member nodes within the police terminal node cluster is queried based on the police terminal operation logic network, and the fault information of member nodes is analyzed to obtain the location of faulty nodes. The neighboring nodes of the fault node are generated based on the location of the fault node. The neighboring nodes are other member nodes with the location of the fault node as the center and the radius is smaller than a set range threshold. Based on the fault reports sent by neighboring nodes to the cluster head node, a link transmission protocol is established between the neighboring nodes and the faulty node, and each police terminal node has a unique identifier to identify the faulty node and the neighboring nodes. Based on the fault report analysis, the fault type of the fault node is analyzed. The fault types include protocol address fault, transmission link fault, or transmission path break of the fault node. A local fault recovery strategy is established based on the fault type. A maintenance setup is generated based on the local fault recovery strategy, and a backup node is started. The transmission link of the backup node is connected to the fault node for network communication and the backup node is replaced. The faulty node is disconnected and repaired. After the repair is completed, the successfully repaired faulty node is reconnected to the police terminal node cluster, and the backup node is disconnected.

[0013] Optionally, in the AI-based remote monitoring and fault diagnosis system for self-service police terminals described in this application embodiment, real-time monitoring status information of the police terminals is obtained, and the real-time monitoring status information is preprocessed to obtain optimized monitoring data for the police terminals, specifically including: Obtain real-time monitoring status information from police terminals and extract data features; The data features are normalized to obtain optimized data features; Based on optimized feature analysis, duplicate data between monitoring status information of adjacent time nodes; Duplicate data is removed to obtain optimized monitoring data from police terminals.

[0014] Optionally, in the AI-based remote monitoring and fault diagnosis system for self-service police terminals described in this application embodiment, police terminal monitoring optimization data is analyzed based on machine learning algorithms to obtain police terminal operation data, specifically including: A learning model is established based on machine learning algorithms, and a training set is established based on historical police terminal monitoring data. The learning model is trained based on the training set to obtain the training results; Determine whether the training results have converged; If convergence is achieved, a police terminal analysis model is generated. Based on the police terminal analysis model, the police terminal monitoring optimization data is analyzed to obtain the police terminal operation data. If the learning model does not converge, the parameters of the learning model are optimized until the learning model converges.

[0015] Thirdly, embodiments of this application also provide a computer-readable storage medium, which includes a program for a remote monitoring and fault diagnosis method for a self-service police terminal based on artificial intelligence. When the program for the remote monitoring and fault diagnosis method for a self-service police terminal based on artificial intelligence is executed by a processor, it implements the steps of the remote monitoring and fault diagnosis method for a self-service police terminal based on artificial intelligence as described in any of the above claims.

[0016] As can be seen from the above, the embodiments of this application provide a method, system, and medium for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence. This involves acquiring real-time monitoring status information of the police terminal, preprocessing the real-time monitoring status information to obtain optimized monitoring data for the police terminal, analyzing the optimized monitoring data based on machine learning algorithms to obtain police terminal operating data, comparing the police terminal operating data with set operating data to obtain an operating deviation rate, determining whether the operating deviation rate is greater than or equal to a set deviation rate threshold, generating fault information if it is greater than or equal to the threshold, generating a fault report based on the fault information, and generating matching maintenance suggestions based on the fault report, and generating police terminal monitoring data if it is less than the threshold. This allows for real-time monitoring of the police terminal by analyzing its operating data, thereby accurately analyzing the operational safety of the police terminal in real time. When an anomaly occurs in the police terminal, matching maintenance suggestions are provided to ensure the normal operation of the police terminal. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the method for remote monitoring and fault diagnosis of a self-service police terminal based on artificial intelligence, provided in this application embodiment; Figure 2 A flowchart of the police terminal monitoring optimization data acquisition method for the self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence provided in the embodiments of this application; Figure 3A flowchart illustrating the method for acquiring police terminal operation data in the AI-based remote monitoring and fault diagnosis method for self-service police terminals provided in this application embodiment. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an AI-based remote monitoring and fault diagnosis method for a self-service police terminal, as described in some embodiments of this application. This AI-based remote monitoring and fault diagnosis method for a self-service police terminal is used in a terminal device and includes the following steps: S101, Obtain real-time monitoring status information of the police terminal, preprocess the real-time monitoring status information, and obtain optimized monitoring data for the police terminal; S102, Based on machine learning algorithms, analyze the monitoring and optimization data of the police terminal to obtain the police terminal operation data, compare the police terminal operation data with the set operation data, and obtain the operation deviation rate; S103, determine whether the running deviation rate is greater than or equal to the set deviation rate threshold; S104, if it is greater than or equal to, generate fault information, generate a fault report based on the fault information, and generate matching maintenance suggestions based on the fault report; S105, if it is less than, then generate police terminal monitoring data and transmit the police terminal monitoring data to the terminal in real time.

[0022] It should be noted that by analyzing the real-time monitoring status data of the police terminal and optimizing the data, fault information can be analyzed, corresponding fault reports can be generated, and matching maintenance suggestions can be generated based on the fault reports, thereby improving the monitoring security of the police terminal.

[0023] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating a method for acquiring optimized monitoring data for a police terminal, based on artificial intelligence, in some embodiments of this application for remote monitoring and fault diagnosis of a self-service police terminal. According to embodiments of the present invention, real-time monitoring status information of the police terminal is acquired, and the real-time monitoring status information is preprocessed to obtain optimized monitoring data for the police terminal, specifically including: S201, Obtain real-time monitoring status information from police terminals and extract data features; S202, normalize the data features to obtain optimized data features; S203, Based on optimized feature analysis, duplicate data between monitoring status information of adjacent time nodes; S204 removes duplicate data to obtain optimized monitoring data for police terminals.

[0024] It should be noted that feature extraction is performed on the real-time monitoring status information of the police terminal, and the data features are normalized to improve the accuracy of the data feature response and remove duplicate data to ensure the accuracy of the monitoring data of the police terminal.

[0025] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating a method for acquiring police terminal operation data, based on an artificial intelligence-based remote monitoring and fault diagnosis method for self-service police terminals, as described in some embodiments of this application. According to embodiments of the present invention, police terminal monitoring optimization data is analyzed using machine learning algorithms to obtain police terminal operation data, specifically including: S301, a learning model is established based on machine learning algorithms, and a training set is established based on historical police terminal monitoring data; S302, Train the learning model based on the training set to obtain the training results; S303, determine whether the training results have converged; S304. If convergence is achieved, a police terminal analysis model is generated. Based on the police terminal analysis model, the police terminal monitoring optimization data is analyzed to obtain the police terminal operation data. S305 If the learning model does not converge, the parameters of the learning model are optimized until the learning model converges.

[0026] It should be noted that the initial model is continuously trained and learned by using historical police terminal monitoring data, thereby obtaining the police terminal analysis model and improving the monitoring accuracy of the police terminals.

[0027] According to an embodiment of the present invention, the operation data of the police terminal is compared with the set operation data to obtain the operation deviation rate, specifically including: Acquire police terminal operation data, compare the police terminal operation data with the set operation data, and obtain data difference information; The data difference information is compared with the set data difference threshold, which includes a first data difference threshold and a second data difference threshold, and the first data difference threshold is less than the second data difference threshold. If the data difference is greater than the first data difference threshold but less than the second data difference threshold, then a first operational deviation rate is generated. If the data difference information is greater than or equal to the second data difference threshold, then a second operational deviation rate is generated.

[0028] It should be noted that by analyzing the operational data of the police terminals and analyzing the data differences, different data differences yield different operational deviation rates, thereby accurately analyzing the operational deviations of the police terminals.

[0029] According to an embodiment of the present invention, if the fault is greater than or equal to the fault information, fault information is generated; a fault report is generated based on the fault information; and matching maintenance suggestions are generated based on the fault report. Specifically, this includes: Obtain fault information and analyze the fault type based on the fault information; Analyze the fault types and generate different types of fault reports; The system analyzes fault types and corresponding fault reports, and generates maintenance recommendations based on an expert database.

[0030] It should be noted that different fault information analyses can identify fault types, thereby accurately analyzing the fault situation and establishing the optimal matching maintenance plan. This improves the maintenance effectiveness of police terminals and ensures their safe operation.

[0031] According to an embodiment of the present invention, if the value is less than a certain threshold, then police terminal monitoring data is generated, and the police terminal monitoring data is transmitted to the terminal in real time, specifically including: Acquire monitoring data from police terminals and analyze the monitoring type of the data. Obtain the type of transmission link, match the type of transmission link with the type of monitoring data, and obtain the matching degree; Determine whether the matching degree is greater than or equal to the set matching degree threshold; If the value is greater than or equal to the set matching threshold, a matching transmission link is obtained and the monitoring data of the police terminal is transmitted to the terminal. If it is less than or equal to, then change the transmission link.

[0032] It should be noted that by analyzing the types of monitoring data from police terminals, the optimal transmission link can be selected, thereby improving data transmission efficiency.

[0033] Secondly, embodiments of this application provide an artificial intelligence-based remote monitoring and fault diagnosis system for self-service police terminals. The system includes a memory and a processor. The memory includes a program for an artificial intelligence-based remote monitoring and fault diagnosis method for self-service police terminals. When the program is executed by the processor, it performs the following steps: Obtain real-time monitoring status information from police terminals, preprocess the real-time monitoring status information, and obtain optimized monitoring data for police terminals; Based on machine learning algorithms, the monitoring and optimization data of police terminals are analyzed to obtain the operation data of the police terminals. The operation data of the police terminals is compared with the set operation data to obtain the operation deviation rate. Determine whether the operating deviation rate is greater than or equal to the set deviation rate threshold; If the value is greater than or equal to the value, fault information is generated, a fault report is generated based on the fault information, and matching maintenance suggestions are generated based on the fault report. If the value is less than the specified value, then police terminal monitoring data is generated and transmitted to the terminal in real time.

[0034] It should be noted that by analyzing the real-time monitoring status data of the police terminal and optimizing the data, fault information can be analyzed, corresponding fault reports can be generated, and matching maintenance suggestions can be generated based on the fault reports, thereby improving the monitoring security of the police terminal.

[0035] According to an embodiment of the present invention, real-time monitoring status information of a police terminal is obtained, and the real-time monitoring status information is preprocessed to obtain optimized monitoring data for the police terminal, specifically including: Obtain real-time monitoring status information from police terminals and extract data features; The data features are normalized to obtain optimized data features; Based on optimized feature analysis, duplicate data between monitoring status information of adjacent time nodes; Duplicate data is removed to obtain optimized monitoring data from police terminals.

[0036] It should be noted that feature extraction is performed on the real-time monitoring status information of the police terminal, and the data features are normalized to improve the accuracy of the data feature response and remove duplicate data to ensure the accuracy of the monitoring data of the police terminal.

[0037] According to an embodiment of the present invention, police terminal monitoring optimization data is analyzed based on machine learning algorithms to obtain police terminal operation data, specifically including: A learning model is established based on machine learning algorithms, and a training set is established based on historical police terminal monitoring data. The learning model is trained based on the training set to obtain the training results; Determine whether the training results have converged; If convergence is achieved, a police terminal analysis model is generated. Based on the police terminal analysis model, the police terminal monitoring optimization data is analyzed to obtain the police terminal operation data. If the learning model does not converge, the parameters of the learning model are optimized until the learning model converges.

[0038] It should be noted that the initial model is continuously trained and learned by using historical police terminal monitoring data, thereby obtaining the police terminal analysis model and improving the monitoring accuracy of the police terminals.

[0039] According to an embodiment of the present invention, the operation data of the police terminal is compared with the set operation data to obtain the operation deviation rate, specifically including: Acquire police terminal operation data, compare the police terminal operation data with the set operation data, and obtain data difference information; The data difference information is compared with the set data difference threshold, which includes a first data difference threshold and a second data difference threshold, and the first data difference threshold is less than the second data difference threshold. If the data difference is greater than the first data difference threshold but less than the second data difference threshold, then a first operational deviation rate is generated. If the data difference information is greater than or equal to the second data difference threshold, then a second operational deviation rate is generated.

[0040] It should be noted that by analyzing the operational data of the police terminals and analyzing the data differences, different data differences yield different operational deviation rates, thereby accurately analyzing the operational deviations of the police terminals.

[0041] According to an embodiment of the present invention, if the fault is greater than or equal to the fault information, fault information is generated; a fault report is generated based on the fault information; and matching maintenance suggestions are generated based on the fault report. Specifically, this includes: Obtain fault information and analyze the fault type based on the fault information; Analyze the fault types and generate different types of fault reports; The system analyzes fault types and corresponding fault reports, and generates maintenance recommendations based on an expert database.

[0042] It should be noted that different fault information analyses can identify fault types, thereby accurately analyzing the fault situation and establishing the optimal matching maintenance plan. This improves the maintenance effectiveness of police terminals and ensures their safe operation.

[0043] According to an embodiment of the present invention, if the value is less than a certain threshold, then police terminal monitoring data is generated, and the police terminal monitoring data is transmitted to the terminal in real time, specifically including: Acquire monitoring data from police terminals and analyze the monitoring type of the data. Obtain the type of transmission link, match the type of transmission link with the type of monitoring data, and obtain the matching degree; Determine whether the matching degree is greater than or equal to the set matching degree threshold; If the value is greater than or equal to the set matching threshold, a matching transmission link is obtained and the monitoring data of the police terminal is transmitted to the terminal. If it is less than or equal to, then change the transmission link.

[0044] It should be noted that by analyzing the types of monitoring data from police terminals, the optimal transmission link can be selected, thereby improving data transmission efficiency.

[0045] A third aspect of the present invention provides a computer-readable storage medium including a program for a remote monitoring and fault diagnosis method for a self-service police terminal based on artificial intelligence. When the program is executed by a processor, it implements the steps of the remote monitoring and fault diagnosis method for a self-service police terminal based on artificial intelligence as described in any of the above claims.

[0046] This invention discloses a method, system, and medium for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence. The method involves acquiring real-time monitoring status information of the police terminal, preprocessing this information to obtain optimized monitoring data, analyzing the optimized monitoring data using machine learning algorithms to obtain operational data, comparing this operational data with preset operational data to obtain an operational deviation rate, determining whether the deviation rate is greater than or equal to a preset threshold, generating fault information and a fault report based on the fault information, and generating matching maintenance suggestions based on the fault report, or generating monitoring data for the police terminal and transmitting this monitoring data to the terminal in real time. By analyzing the operational data of the police terminal, real-time monitoring of the terminal is achieved, enabling precise real-time analysis of the terminal's operational safety. When an anomaly occurs, matching maintenance suggestions are provided to ensure the normal operation of the police terminal.

[0047] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0048] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0049] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0050] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0051] Alternatively, if the integrated units of the present invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for remote monitoring and fault diagnosis of a self-service police terminal based on artificial intelligence, characterized in that, include: Obtain real-time monitoring status information from police terminals, preprocess the real-time monitoring status information, and obtain optimized monitoring data for police terminals; Based on machine learning algorithms, the monitoring and optimization data of police terminals are analyzed to obtain the operation data of the police terminals. The operation data of the police terminals is compared with the set operation data to obtain the operation deviation rate. Determine whether the operating deviation rate is greater than or equal to the set deviation rate threshold; If the value is greater than or equal to the value, fault information is generated, a fault report is generated based on the fault information, and matching maintenance suggestions are generated based on the fault report. If the value is less than the specified value, then police terminal monitoring data is generated and transmitted to the remote monitoring terminal in real time, specifically including: Acquire monitoring data from police terminals and analyze the monitoring type of the data. Obtain the type of transmission link, match the type of transmission link with the type of monitoring data, and obtain the matching degree; Determine whether the matching degree is greater than or equal to the set matching degree threshold; If the value is greater than or equal to the set matching threshold, a matching transmission link is obtained and the monitoring data of the police terminal is transmitted to the remote monitoring terminal. If it is less than or equal to, then change the transmission link; Among them, the analysis of police terminal monitoring optimization data based on machine learning algorithms yields police terminal operation data, specifically including: A learning model is established based on machine learning algorithms, and a training set is established based on historical police terminal monitoring data. The learning model is trained based on the training set to obtain the training results; Determine whether the training results have converged; If convergence is achieved, a police terminal analysis model is generated. Based on the police terminal analysis model, the police terminal monitoring optimization data is analyzed to obtain the police terminal operation data. If the learning model does not converge, the parameters of the learning model are optimized until the learning model converges. This includes generating a fault report based on fault information, and generating matching maintenance suggestions based on the fault report, specifically including: Obtain the parameter information of the police terminal and establish a police terminal node. The parameter information of the police terminal includes the model of the police terminal, the function of the police terminal, and the usage scenario of the police terminal. The location information of several police terminals is obtained, and the police terminal nodes are grouped based on the location information of the police terminals to generate multiple police terminal node clusters. Each police terminal node cluster includes at least two member nodes and at least one backup node. The member nodes are police terminal nodes, and the police terminal node cluster includes a cluster head node. Multiple police terminal node clusters are connected by gateway nodes. A police terminal operation logic network is formed based on the cluster head node and the gateway node. The operation status information of member nodes within the police terminal node cluster is queried based on the police terminal operation logic network, and the fault information of member nodes is analyzed to obtain the location of faulty nodes. The neighboring nodes of the fault node are generated based on the location of the fault node. The neighboring nodes are other member nodes with the location of the fault node as the center and the radius is smaller than a set range threshold. Based on the fault reports sent by neighboring nodes to the cluster head node, a link transmission protocol is established between the neighboring nodes and the faulty node, and each police terminal node has a unique identifier to identify the faulty node and the neighboring nodes. Based on the fault report analysis, the fault type of the fault node is analyzed. The fault types include protocol address fault, transmission link fault, or transmission path break of the fault node. A local fault recovery strategy is established based on the fault type. A maintenance setup is generated based on the local fault recovery strategy, and a backup node is started. The transmission link of the backup node is connected to the fault node for network communication and the backup node is replaced. The faulty node is disconnected and repaired. After the repair is completed, the successfully repaired faulty node is reconnected to the police terminal node cluster, and the backup node is disconnected. This involves comparing the operational data of the police terminal with the set operational data to obtain the operational deviation rate, specifically including: Acquire police terminal operation data, compare the police terminal operation data with the set operation data, and obtain data difference information; The data difference information is compared with the set data difference threshold, which includes a first data difference threshold and a second data difference threshold, and the first data difference threshold is less than the second data difference threshold. If the data difference is greater than the first data difference threshold but less than the second data difference threshold, then a first operational deviation rate is generated. If the data difference information is greater than or equal to the second data difference threshold, then a second operational deviation rate is generated.

2. The method for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence according to claim 1, characterized in that, Obtain real-time monitoring status information from police terminals, preprocess the real-time monitoring status information to obtain optimized monitoring data for police terminals, specifically including: Obtain real-time monitoring status information from police terminals and extract data features; The data features are normalized to obtain optimized data features; Based on optimized feature analysis, duplicate data between monitoring status information of adjacent time nodes; Duplicate data is removed to obtain optimized monitoring data from police terminals.

3. The method for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence according to claim 2, characterized in that, If the value is greater than or equal to the fault information, a fault report is generated based on the fault information, and matching maintenance suggestions are generated based on the fault report, specifically including: Obtain fault information and analyze the fault type based on the fault information; Analyze the fault types and generate different types of fault reports; The system analyzes fault types and corresponding fault reports, and generates maintenance recommendations based on an expert database.

4. A remote monitoring and fault diagnosis system for self-service police terminals based on artificial intelligence, characterized in that, The system includes a memory and a processor. The memory contains a program for a remote monitoring and fault diagnosis method for self-service police terminals based on artificial intelligence. When the program for the remote monitoring and fault diagnosis method for self-service police terminals based on artificial intelligence is executed by the processor, it performs the following steps: Obtain real-time monitoring status information from police terminals, preprocess the real-time monitoring status information, and obtain optimized monitoring data for police terminals; Based on machine learning algorithms, the monitoring and optimization data of police terminals are analyzed to obtain the operation data of the police terminals. The operation data of the police terminals is compared with the set operation data to obtain the operation deviation rate. Determine whether the operating deviation rate is greater than or equal to the set deviation rate threshold; If the value is greater than or equal to the value, fault information is generated, a fault report is generated based on the fault information, and matching maintenance suggestions are generated based on the fault report. If the value is less than the specified value, then police terminal monitoring data is generated and transmitted to the remote monitoring terminal in real time, specifically including: Acquire monitoring data from police terminals and analyze the monitoring type of the data. Obtain the type of transmission link, match the type of transmission link with the type of monitoring data, and obtain the matching degree; Determine whether the matching degree is greater than or equal to the set matching degree threshold; If the value is greater than or equal to the set matching threshold, a matching transmission link is obtained and the monitoring data of the police terminal is transmitted to the remote monitoring terminal. If it is less than or equal to, then change the transmission link; Among them, the analysis of police terminal monitoring optimization data based on machine learning algorithms yields police terminal operation data, specifically including: A learning model is established based on machine learning algorithms, and a training set is established based on historical police terminal monitoring data. The learning model is trained based on the training set to obtain the training results; Determine whether the training results have converged; If convergence is achieved, a police terminal analysis model is generated. Based on the police terminal analysis model, the police terminal monitoring optimization data is analyzed to obtain the police terminal operation data. If the learning model does not converge, the parameters of the learning model are optimized until the learning model converges. This includes generating a fault report based on fault information, and generating matching maintenance suggestions based on the fault report, specifically including: Obtain the parameter information of the police terminal and establish a police terminal node. The parameter information of the police terminal includes the model of the police terminal, the function of the police terminal, and the usage scenario of the police terminal. The location information of several police terminals is obtained, and the police terminal nodes are grouped based on the location information of the police terminals to generate multiple police terminal node clusters. Each police terminal node cluster includes at least two member nodes and at least one backup node. The member nodes are police terminal nodes, and the police terminal node cluster includes a cluster head node. Multiple police terminal node clusters are connected by gateway nodes. A police terminal operation logic network is formed based on the cluster head node and the gateway node. The operation status information of member nodes within the police terminal node cluster is queried based on the police terminal operation logic network, and the fault information of member nodes is analyzed to obtain the location of faulty nodes. The neighboring nodes of the fault node are generated based on the location of the fault node. The neighboring nodes are other member nodes with the location of the fault node as the center and the radius is smaller than a set range threshold. Based on the fault reports sent by neighboring nodes to the cluster head node, a link transmission protocol is established between the neighboring nodes and the faulty node, and each police terminal node has a unique identifier to identify the faulty node and the neighboring nodes. Based on the fault report analysis, the fault type of the fault node is analyzed. The fault types include protocol address fault, transmission link fault, or transmission path break of the fault node. A local fault recovery strategy is established based on the fault type. A maintenance setup is generated based on the local fault recovery strategy, and a backup node is started. The transmission link of the backup node is connected to the fault node for network communication and the backup node is replaced. The faulty node is disconnected and repaired. After the repair is completed, the successfully repaired faulty node is reconnected to the police terminal node cluster, and the backup node is disconnected. This involves comparing the operational data of the police terminal with the set operational data to obtain the operational deviation rate, specifically including: Acquire police terminal operation data, compare the police terminal operation data with the set operation data, and obtain data difference information; The data difference information is compared with the set data difference threshold, which includes a first data difference threshold and a second data difference threshold, and the first data difference threshold is less than the second data difference threshold. If the data difference is greater than the first data difference threshold but less than the second data difference threshold, then a first operational deviation rate is generated. If the data difference information is greater than or equal to the second data difference threshold, then a second operational deviation rate is generated.

5. The remote monitoring and fault diagnosis system for self-service police terminals based on artificial intelligence according to claim 4, characterized in that, Obtain real-time monitoring status information from police terminals, preprocess the real-time monitoring status information to obtain optimized monitoring data for police terminals, specifically including: Obtain real-time monitoring status information from police terminals and extract data features; The data features are normalized to obtain optimized data features; Based on optimized feature analysis, duplicate data between monitoring status information of adjacent time nodes; Duplicate data is removed to obtain optimized monitoring data from police terminals.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a method program for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence. When the method program is executed by a processor, it implements the steps of the method for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence as described in any one of claims 1 to 3.

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