Self-service police service terminal remote monitoring and fault diagnosis method and system based on artificial intelligence, and medium

Abstract

The embodiment of the invention provides a self-service police service terminal remote monitoring and fault diagnosis method and system based on artificial intelligence and a medium, and the method comprises the steps: obtaining the real-time monitoring state information of a police service terminal, carrying out the preprocessing of the real-time monitoring state information, and obtaining the monitoring optimization data of the police service terminal; analyzing the police service terminal monitoring optimization data based on a machine learning algorithm to obtain police service terminal operation data, and comparing the police service terminal operation data with set operation data to obtain an operation deviation rate; judging whether the operation deviation rate is greater than or equal to a set deviation rate threshold value; if yes, generating a matched maintenance suggestion; if yes, police service terminal monitoring data are generated; by analyzing the operation data of the police service terminal, the police service terminal is monitored in real time, so that the operation safety of the police service terminal is accurately analyzed in real time, and when the police service terminal is abnormal, matched maintenance suggestions are given, and normal operation of the police service terminal is ensured.

Description

Technical Field

[0001] The present application relates to the field of police terminal monitoring technology, and more specifically, to an artificial intelligence-based self-service police terminal remote monitoring and fault diagnosis method, system and medium. Background Art

[0002] Currently, the processing of certificates such as driver's license, vehicle registration, temporary license plate, and certificate of qualification requires the cooperation of the police and the public. If one party fails to respond in time, it will delay the processing time, which is greatly disadvantageous to both parties. Moreover, users who apply for certificates often have to wait for a long time to get the relevant certificates.

[0003] Existing self-service police terminals cannot be remotely monitored, making it difficult to accurately analyze the operational safety of police terminals in real time. When an abnormality occurs in a police terminal, no matching maintenance suggestions can be given, affecting the operational safety of the police terminal. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a self-service police terminal remote monitoring and fault diagnosis method, system and medium based on artificial intelligence, which can monitor the police terminal in real time by analyzing the operating data of the police terminal, so as to accurately analyze the operating safety of the police terminal in real time. When the police terminal has an abnormality, matching maintenance suggestions are given to ensure the normal operation of the police terminal.

[0005] The embodiment of the present application also provides a remote monitoring and fault diagnosis method for a self-service police terminal based on artificial intelligence, including: Obtain the real-time monitoring status information of the police terminal, pre-process the real-time monitoring status information, and obtain the police terminal monitoring optimization data; Analyze the police terminal monitoring optimization data based on the machine learning algorithm to obtain the police terminal operation data, compare the police terminal operation data with the set operation data, and obtain the operation deviation rate; Determining whether the operation deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to, 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 it is less than, the police terminal monitoring data is generated and transmitted to the terminal in real time; Among them, a fault report is generated based on the fault information, and a matching maintenance suggestion is generated according to the fault report, specifically including: Obtain 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; Acquire location information of several police terminals, group several police terminal nodes based on the location information of the police terminals, generate multiple police terminal node clusters, each police terminal node cluster includes at least two member nodes and at least one spare node, the member nodes are police terminal nodes, and the police terminal node cluster includes a cluster head node; A gateway node is connected between multiple police terminal node clusters. A police terminal operation logic network is formed based on the cluster head node and the gateway node. The operation status information of the member nodes in the police terminal node cluster is queried based on the police terminal operation logic network, and the fault information of the member nodes is analyzed to obtain the location of the faulty node. Based on the position of the faulty node, the neighboring nodes of the faulty node are identified, where the neighboring nodes are other member nodes within a circle with the position of the faulty node as the center and a radius smaller than a set range threshold; Based on the neighbor node sending a fault report to the cluster head node, a link transmission protocol is established between the neighbor node and the fault node, and each police terminal node has a unique identifier for identifying the fault node and the neighbor node; Analyze the fault type of the faulty node based on the fault report, the fault type includes protocol address failure, transmission link failure or transmission path breakage of the faulty node; Establish a local fault recovery strategy based on the fault type, generate a maintenance strategy based on the local fault recovery strategy, and start a standby node, so that the transmission link of the standby node communicates with the faulty node through the network and replaces the faulty node; 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 standby node is disconnected.

[0006] Specifically, by clustering the police terminal nodes, the entire police terminal network is divided into multiple police terminal node clusters. While the police terminal node clusters are interconnected, each police terminal node cluster analyzes and manages the fault conditions of multiple police terminal nodes through the cluster head node, thereby realizing distributed management of several police terminal nodes and improving fault analysis efficiency and accuracy. When a police terminal node fails, it is only necessary to quickly start a backup node in the police terminal node cluster to replace the failed node, thereby realizing rapid local fault recovery and improving the fault recovery response speed.

[0007] Optionally, in the artificial intelligence-based remote monitoring and fault diagnosis method for a self-service police terminal described in an embodiment of the present application, obtaining real-time monitoring status information of the police terminal, preprocessing the real-time monitoring status information, and obtaining police terminal monitoring optimization data specifically includes: Obtain real-time monitoring status information from police terminals and extract data features; Normalize the data features to obtain optimized data features; Analyze the duplicate data between the monitoring status information of adjacent time nodes based on the optimization characteristics; Duplicate data is eliminated to obtain optimized data for police terminal monitoring.

[0008] Optionally, in the artificial intelligence-based self-service police terminal remote monitoring and fault diagnosis method described in the embodiment of the present application, the police terminal monitoring optimization data is analyzed based on a machine learning algorithm to obtain the police terminal operation data, specifically including; Establish a learning model based on machine learning algorithms and a training set based on historical police terminal monitoring data; Train the learning model based on the training set to obtain the training results; Determining whether the training result converges; If converged, a police terminal analysis model is generated, and the police terminal monitoring optimization data is analyzed based on the police terminal analysis model to obtain the police terminal operation data; If it does not converge, the parameters of the learning model are optimized until the learning model converges.

[0009] Optionally, in the artificial intelligence-based remote monitoring and fault diagnosis method for a self-service police terminal described in an embodiment of the present application, the police terminal operation data is compared with the set operation data to obtain the operation deviation rate, which specifically includes: Obtain the police terminal operation data, compare the police terminal operation data with the set operation data, and obtain data difference information; Comparing the data difference information with a set data difference threshold, where the set data difference threshold 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 information is greater than a first data difference threshold and less than a second data difference threshold, generating a first running deviation rate; If the data difference information is greater than or equal to the second data difference threshold, a second running deviation rate is generated.

[0010] Optionally, in the artificial intelligence-based self-service police terminal remote monitoring and fault diagnosis method described in the embodiment of the present application, if is greater than or equal to, then generating fault information, generating a fault report based on the fault information, and generating matching maintenance suggestions based on the fault report, specifically including: Obtain fault information and analyze the fault type based on the fault information; Analyze the fault type and generate different types of fault reports; Analyze the fault type and the corresponding type of fault report, and generate maintenance suggestions based on the expert database.

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

[0012] In a second aspect, an embodiment of the present application provides a self-service police terminal remote monitoring and fault diagnosis system based on artificial intelligence, the system comprising: a memory and a processor, the memory comprising a program of a self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence, and the program of the self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence is executed by the processor to implement the following steps: Obtain the real-time monitoring status information of the police terminal, pre-process the real-time monitoring status information, and obtain the police terminal monitoring optimization data; Analyze the police terminal monitoring optimization data based on the machine learning algorithm to obtain the police terminal operation data, compare the police terminal operation data with the set operation data, and obtain the operation deviation rate; Determining whether the operation deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to, 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 it is less than, the police terminal monitoring data is generated and transmitted to the terminal in real time; Among them, a fault report is generated based on the fault information, and a matching maintenance suggestion is generated according to the fault report, specifically including: Obtain 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; Acquire location information of several police terminals, group several police terminal nodes based on the location information of the police terminals, generate multiple police terminal node clusters, each police terminal node cluster includes at least two member nodes and at least one spare node, the member nodes are police terminal nodes, and the police terminal node cluster includes a cluster head node; A gateway node is connected between multiple police terminal node clusters. A police terminal operation logic network is formed based on the cluster head node and the gateway node. The operation status information of the member nodes in the police terminal node cluster is queried based on the police terminal operation logic network, and the fault information of the member nodes is analyzed to obtain the location of the faulty node. Based on the position of the faulty node, the neighboring nodes of the faulty node are identified, where the neighboring nodes are other member nodes within a circle with the position of the faulty node as the center and a radius smaller than a set range threshold; Based on the neighbor node sending a fault report to the cluster head node, a link transmission protocol is established between the neighbor node and the fault node, and each police terminal node has a unique identifier for identifying the fault node and the neighbor node; Analyze the fault type of the faulty node based on the fault report, the fault type includes protocol address failure, transmission link failure or transmission path breakage of the faulty node; Establish a local fault recovery strategy based on the fault type, generate a maintenance strategy based on the local fault recovery strategy, and start a standby node, so that the transmission link of the standby node communicates with the faulty node through the network and replaces the faulty node; 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 standby node is disconnected.

[0013] Optionally, in the artificial intelligence-based self-service police terminal remote monitoring and fault diagnosis system described in the embodiment of the present application, the real-time monitoring status information of the police terminal is obtained, and the real-time monitoring status information is pre-processed to obtain the police terminal monitoring optimization data, which specifically includes: Obtain real-time monitoring status information from police terminals and extract data features; Normalize the data features to obtain optimized data features; Analyze the duplicate data between the monitoring status information of adjacent time nodes based on the optimization characteristics; Duplicate data is eliminated to obtain optimized data for police terminal monitoring.

[0014] Optionally, in the artificial intelligence-based self-service police terminal remote monitoring and fault diagnosis system described in the embodiment of the present application, the police terminal monitoring optimization data is analyzed based on the machine learning algorithm to obtain the police terminal operation data, specifically including; Establish a learning model based on machine learning algorithms and a training set based on historical police terminal monitoring data; Train the learning model based on the training set to obtain the training results; Determining whether the training result converges; If converged, a police terminal analysis model is generated, and the police terminal monitoring optimization data is analyzed based on the police terminal analysis model to obtain the police terminal operation data; If it does not converge, the parameters of the learning model are optimized until the learning model converges.

[0015] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a program for a self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence. When the program for a self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence is executed by a processor, the steps of the self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence as described in any one of the above items are implemented.

[0016] As can be seen from the above, the embodiment of the present application provides an artificial intelligence-based self-service police terminal remote monitoring and fault diagnosis method, system and medium, which obtains real-time monitoring status information of the police terminal, pre-processes the real-time monitoring status information, and obtains police terminal monitoring optimization data; analyzes the police terminal monitoring optimization data based on a machine learning algorithm to obtain police terminal operation data, and compares the police terminal operation data with the set operation data to obtain the operation deviation rate; determines whether the operation deviation rate is greater than or equal to the set deviation rate threshold; if greater than or equal to, generates fault information, generates a fault report based on the fault information, and generates matching maintenance suggestions based on the fault report; if less than, generates police terminal monitoring data, and transmits the police terminal monitoring data to the terminal in real time; by analyzing the operation data of the police terminal, the police terminal is monitored in real time, so as to accurately analyze the operation safety of the police terminal in real time, and when the police terminal is abnormal, gives matching maintenance suggestions to ensure the normal operation of the police terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of a remote monitoring and fault diagnosis method for a self-service police terminal based on artificial intelligence provided in an embodiment of the present application; Figure 2 A flow chart of a method for acquiring police terminal monitoring optimization data for a self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence provided in an embodiment of the present application; Figure 3A flow chart of a method for acquiring police terminal operation data for the artificial intelligence-based self-service police terminal remote monitoring and fault diagnosis method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 The flowchart of a self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence in some embodiments of the present application. The self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence is used in a terminal device, and the self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence includes the following steps: S101, obtaining real-time monitoring status information of the police terminal, pre-processing the real-time monitoring status information, and obtaining police terminal monitoring optimization data; S102, analyzing the police terminal monitoring optimization data based on a machine learning algorithm to obtain police terminal operation data, and comparing the police terminal operation data with the set operation data to obtain an operation deviation rate; S103, determining whether the operation deviation rate is greater than or equal to a set deviation rate threshold; S104, if greater than or equal to, generating fault information, generating a fault report based on the fault information, and generating matching maintenance suggestions based on the fault report; S105: If it is less than, 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 real-time monitoring status data of the police terminal, the fault information is analyzed, a corresponding fault report is formed, and matching maintenance suggestions are generated based on the fault report to improve the monitoring security of the police terminal.

[0023] Please refer to Figure 2 , Figure 2 The present invention is a flowchart of a method for acquiring police terminal monitoring optimization data in a self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence in some embodiments of the present invention. According to an embodiment of the present invention, the real-time monitoring status information of the police terminal is acquired, and the real-time monitoring status information is preprocessed to obtain the police terminal monitoring optimization data, which specifically includes: S201, obtaining real-time monitoring status information of the police terminal and extracting data features; S202, normalizing the data features to obtain optimized data features; S203, analyzing duplicate data between monitoring status information of adjacent time nodes based on the optimization feature; S204, remove duplicate data to obtain police terminal monitoring optimization data.

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

[0025] Please refer to Figure 3 , Figure 3 This is a flow chart of a method for acquiring police terminal operation data in a self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence in some embodiments of the present application. According to an embodiment of the present invention, the police terminal monitoring optimization data is analyzed based on a machine learning algorithm to obtain the police terminal operation data, specifically including; S301, establishing a learning model based on a machine learning algorithm and establishing a training set based on historical police terminal monitoring data; S302, training the learning model based on the training set to obtain a training result; S303, determining whether the training result has converged; S304, if converged, generate a police terminal analysis model, analyze the police terminal monitoring optimization data based on the police terminal analysis model, and obtain police terminal operation data; S305: If it does not converge, optimize the parameters of the learning model until the learning model converges.

[0026] It should be noted that the initial model is continuously trained and learned through historical police terminal monitoring data, so as to obtain a police terminal analysis model and improve the monitoring accuracy of the police terminal.

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

[0028] It should be noted that by analyzing the police terminal operation data and data difference information, different data difference information will obtain different operation deviation rates, thereby accurately analyzing the police terminal operation deviation.

[0029] According to an embodiment of the present invention, if it is greater than or equal to, fault information is generated, a fault report is generated based on the fault information, and a matching maintenance suggestion is generated according to the fault report, specifically including: Obtain fault information and analyze the fault type based on the fault information; Analyze the fault type and generate different types of fault reports; Analyze the fault type and the corresponding type of fault report, and generate maintenance suggestions based on the expert database.

[0030] It should be noted that different fault information analyzes the fault type, thereby accurately analyzing the fault situation, thereby forming the best matching maintenance establishment, improving the maintenance effect of the police terminal, and ensuring that the police terminal can operate safely.

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

[0032] It should be noted that by analyzing the type of monitoring data from the police terminal and selecting the best transmission link, the data transmission efficiency can be improved.

[0033] In a second aspect, an embodiment of the present application provides a self-service police terminal remote monitoring and fault diagnosis system based on artificial intelligence, the system comprising: a memory and a processor, the memory comprising a program of a self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence, and the program of the self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence is executed by the processor to implement the following steps: Obtain the real-time monitoring status information of the police terminal, pre-process the real-time monitoring status information, and obtain the police terminal monitoring optimization data; Analyze the police terminal monitoring optimization data based on the machine learning algorithm to obtain the police terminal operation data, compare the police terminal operation data with the set operation data, and obtain the operation deviation rate; Determine whether the running deviation rate is greater than or equal to the set deviation rate threshold; If it is greater than or equal to, 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 it is less than, the police terminal monitoring data will be 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 real-time monitoring status data of the police terminal, the fault information is analyzed, a corresponding fault report is formed, and matching maintenance suggestions are generated based on the fault report to improve the monitoring security of the police terminal.

[0035] According to an embodiment of the present invention, the real-time monitoring status information of the police terminal is obtained, and the real-time monitoring status information is preprocessed to obtain the police terminal monitoring optimization data, which specifically includes: Obtain real-time monitoring status information from police terminals and extract data features; Normalize the data features to obtain optimized data features; Analyze the duplicate data between the monitoring status information of adjacent time nodes based on the optimization characteristics; Duplicate data is eliminated to obtain optimized data for police terminal monitoring.

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

[0037] According to an embodiment of the present invention, the police terminal monitoring optimization data is analyzed based on a machine learning algorithm to obtain the police terminal operation data, specifically including; Establish a learning model based on machine learning algorithms and a training set based on historical police terminal monitoring data; Train the learning model based on the training set to obtain the training results; Determine whether the training results converge; If converged, a police terminal analysis model is generated, and the police terminal monitoring optimization data is analyzed based on the police terminal analysis model to obtain the police terminal operation data; If it 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 through historical police terminal monitoring data, so as to obtain a police terminal analysis model and improve the monitoring accuracy of the police terminal.

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

[0040] It should be noted that by analyzing the police terminal operation data and data difference information, different data difference information will obtain different operation deviation rates, thereby accurately analyzing the police terminal operation deviation.

[0041] According to an embodiment of the present invention, if it is greater than or equal to, fault information is generated, a fault report is generated based on the fault information, and a matching maintenance suggestion is generated according to the fault report, specifically including: Obtain fault information and analyze the fault type based on the fault information; Analyze the fault type and generate different types of fault reports; Analyze the fault type and the corresponding type of fault report, and generate maintenance suggestions based on the expert database.

[0042] It should be noted that different fault information analyzes the fault type, thereby accurately analyzing the fault situation, thereby forming the best matching maintenance establishment, improving the maintenance effect of the police terminal, and ensuring that the police terminal can operate safely.

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

[0044] It should be noted that by analyzing the type of monitoring data from the police terminal and selecting the best transmission link, the data transmission efficiency can be improved.

[0045] The third aspect of the present invention provides a computer-readable storage medium, which includes a program for the remote monitoring and fault diagnosis method of a self-service police terminal based on artificial intelligence. When the program for the remote monitoring and fault diagnosis method of a self-service police terminal based on artificial intelligence is executed by a processor, the steps of the remote monitoring and fault diagnosis method of a self-service police terminal based on artificial intelligence as described above are implemented.

[0046] The present invention discloses a self-service police terminal remote monitoring and fault diagnosis method, system and medium based on artificial intelligence. The method obtains real-time monitoring status information of the police terminal, pre-processes the real-time monitoring status information, and obtains police terminal monitoring optimization data; analyzes the police terminal monitoring optimization data based on a machine learning algorithm to obtain police terminal operation data, compares the police terminal operation data with the set operation data, and obtains the operation deviation rate; determines whether the operation deviation rate is greater than or equal to the set deviation rate threshold; if greater than or equal to, generates fault information, generates a fault report based on the fault information, and generates matching maintenance suggestions according to the fault report; if less than, generates police terminal monitoring data, and transmits the police terminal monitoring data to the terminal in real time; by analyzing the operation data of the police terminal, the police terminal is monitored in real time, so as to accurately analyze the operation safety of the police terminal in real time, and when the police terminal is abnormal, gives matching maintenance suggestions to ensure the normal operation of the police terminal.

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

[0048] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0049] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0050] A person skilled in the art can understand that all or part of the steps of the above method embodiment can be completed by hardware related to program instructions, and the above program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the above storage medium includes: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. Various media that can store program codes.

[0051] Or, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A remote monitoring and fault diagnosis method for a self-service police terminal based on artificial intelligence, characterized in that: include: Obtain the real-time monitoring status information of the police terminal, pre-process the real-time monitoring status information, and obtain the police terminal monitoring optimization data; Analyze the police terminal monitoring optimization data based on the machine learning algorithm to obtain the police terminal operation data, compare the police terminal operation data with the set operation data, and obtain the operation deviation rate; Determining whether the operation deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to, 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 it is less than, the police terminal monitoring data is generated and transmitted to the terminal in real time; Among them, a fault report is generated based on the fault information, and matching maintenance suggestions are generated according to the fault report, specifically including: Obtain 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; Acquire location information of several police terminals, group several police terminal nodes based on the location information of the police terminals, generate multiple police terminal node clusters, each police terminal node cluster includes at least two member nodes and at least one spare node, the member nodes are police terminal nodes, and the police terminal node cluster includes a cluster head node; A gateway node is connected between multiple police terminal node clusters. A police terminal operation logic network is formed based on the cluster head node and the gateway node. The operation status information of the member nodes in the police terminal node cluster is queried based on the police terminal operation logic network, and the fault information of the member nodes is analyzed to obtain the location of the faulty node. Identify neighbor nodes of the faulty node based on the position of the faulty node, where the neighbor nodes are other member nodes within a circle with the position of the faulty node as the center and a radius smaller than a set range threshold; Based on the neighbor node sending a fault report to the cluster head node, a link transmission protocol is established between the neighbor node and the fault node, and each police terminal node has a unique identifier for identifying the fault node and the neighbor node; Analyze the fault type of the faulty node based on the fault report, the fault type includes protocol address failure, transmission link failure or transmission path breakage of the faulty node; Establish a local fault recovery strategy based on the fault type, generate a maintenance strategy based on the local fault recovery strategy, and start a standby node, so that the transmission link of the standby node communicates with the faulty node through the network and replaces the faulty node; 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 standby node is disconnected.

2. The method for remote monitoring and fault diagnosis of a self-service police terminal based on artificial intelligence according to claim 1 is characterized in that: Obtain the real-time monitoring status information of the police terminal, pre-process the real-time monitoring status information, and obtain the police terminal monitoring optimization data, including: Obtain real-time monitoring status information from police terminals and extract data features; Normalize the data features to obtain optimized data features; Analyze the duplicate data between the monitoring status information of adjacent time nodes based on the optimization characteristics; Duplicate data is eliminated to obtain optimized data for police terminal monitoring.

3. The method for remote monitoring and fault diagnosis of self-service police terminals based on artificial intelligence according to claim 2 is characterized in that: Analyze the police terminal monitoring optimization data based on machine learning algorithms to obtain the police terminal operation data, including: Establish a learning model based on machine learning algorithms and a training set based on historical police terminal monitoring data; Train the learning model based on the training set to obtain the training results; Determining whether the training result converges; If converged, a police terminal analysis model is generated, and the police terminal monitoring optimization data is analyzed based on the police terminal analysis model to obtain the police terminal operation data; If it does not converge, the parameters of the learning model are optimized until the learning model converges.

4. The method for remote monitoring and fault diagnosis of a self-service police terminal based on artificial intelligence according to claim 3 is characterized in that: Compare the police terminal operation data with the set operation data to obtain the operation deviation rate, including: Obtain the police terminal operation data, compare the police terminal operation data with the set operation data, and obtain data difference information; Comparing the data difference information with a set data difference threshold, where the set data difference threshold 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 information is greater than a first data difference threshold and less than a second data difference threshold, generating a first running deviation rate; If the data difference information is greater than or equal to the second data difference threshold, a second running deviation rate is generated.

5. The method for remote monitoring and fault diagnosis of a self-service police terminal based on artificial intelligence according to claim 4 is characterized in that: If it is greater than or equal to, 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, including: Obtain fault information and analyze the fault type based on the fault information; Analyze the fault type and generate different types of fault reports; Analyze the fault type and the corresponding type of fault report, and generate maintenance suggestions based on the expert database.

6. The method for remote monitoring and fault diagnosis of a self-service police terminal based on artificial intelligence according to claim 5 is characterized in that: If it is less than, the police terminal monitoring data is generated and transmitted to the terminal in real time, including: Obtain monitoring data from police terminals and analyze the monitoring types of the monitoring data; Obtain the type of the transmission link, match the type of the transmission link with the type of the monitoring data, and obtain a matching degree; Determining whether the matching degree is greater than or equal to a set matching degree threshold; If it is greater than or equal to the set matching degree threshold, a matching transmission link is obtained and the police terminal monitoring data is transmitted to the terminal; If it is less than or equal to, change the transmission link.

7. A self-service police terminal remote monitoring and fault diagnosis system based on artificial intelligence, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of a self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence, and when the program of the self-service police terminal remote monitoring and fault diagnosis method based on artificial intelligence is executed by the processor, the following steps are implemented: Obtain the real-time monitoring status information of the police terminal, pre-process the real-time monitoring status information, and obtain the police terminal monitoring optimization data; Analyze the police terminal monitoring optimization data based on the machine learning algorithm to obtain the police terminal operation data, compare the police terminal operation data with the set operation data, and obtain the operation deviation rate; Determining whether the operation deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to, 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 it is less than, the police terminal monitoring data is generated and transmitted to the terminal in real time; Among them, a fault report is generated based on the fault information, and matching maintenance suggestions are generated according to the fault report, specifically including: Obtain 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; Acquire location information of several police terminals, group several police terminal nodes based on the location information of the police terminals, generate multiple police terminal node clusters, each police terminal node cluster includes at least two member nodes and at least one spare node, the member nodes are police terminal nodes, and the police terminal node cluster includes a cluster head node; A gateway node is connected between multiple police terminal node clusters. A police terminal operation logic network is formed based on the cluster head node and the gateway node. The operation status information of the member nodes in the police terminal node cluster is queried based on the police terminal operation logic network, and the fault information of the member nodes is analyzed to obtain the location of the faulty node. Identify neighbor nodes of the faulty node based on the position of the faulty node, where the neighbor nodes are other member nodes within a circle with the position of the faulty node as the center and a radius smaller than a set range threshold; Based on the neighbor node sending a fault report to the cluster head node, a link transmission protocol is established between the neighbor node and the fault node, and each police terminal node has a unique identifier for identifying the fault node and the neighbor node; Analyze the fault type of the faulty node based on the fault report, the fault type includes protocol address failure, transmission link failure or transmission path breakage of the faulty node; Establish a local fault recovery strategy based on the fault type, generate a maintenance strategy based on the local fault recovery strategy, and start a standby node, so that the transmission link of the standby node communicates with the faulty node through the network and replaces the faulty node; 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 standby node is disconnected.

8. The artificial intelligence-based self-service police terminal remote monitoring and fault diagnosis system according to claim 7 is characterized in that: Obtain the real-time monitoring status information of the police terminal, pre-process the real-time monitoring status information, and obtain the police terminal monitoring optimization data, including: Obtain real-time monitoring status information from police terminals and extract data features; Normalize the data features to obtain optimized data features; Analyze the duplicate data between the monitoring status information of adjacent time nodes based on the optimization characteristics; Duplicate data is eliminated to obtain optimized data for police terminal monitoring.

9. The artificial intelligence-based self-service police terminal remote monitoring and fault diagnosis system according to claim 8 is characterized in that: Analyze the police terminal monitoring optimization data based on machine learning algorithms to obtain the police terminal operation data, including: Establish a learning model based on machine learning algorithms and a training set based on historical police terminal monitoring data; Train the learning model based on the training set to obtain the training results; Determining whether the training result converges; If converged, a police terminal analysis model is generated, and the police terminal monitoring optimization data is analyzed based on the police terminal analysis model to obtain the police terminal operation data; If it does not converge, the parameters of the learning model are optimized until the learning model converges.

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

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