Intelligent charging pile interface docking platform system, request processing method and medium

Abstract

The invention discloses an intelligent charging pile interface docking platform system, a request processing method and a medium. The intelligent charging pile interface docking platform system comprises a data layer, a business processing layer, an AI Agent intelligent processing layer and an access layer. The data layer is used for receiving a charging request of a current charging pile and is also used for data storage and management service; the business processing layer is used for receiving a charging request of a current charging pile and sending the charging request to the AI Agent intelligent processing layer; the AI Agent intelligent processing layer carries out request processing operation on the current charging pile charging request to obtain a corresponding target charging pile charging request analysis processing result; and the access layer feeds back a result through a uniform interface service corresponding to the application programming interface gateway. According to the invention, the problems of lack of unified standard and intelligent processing capability, dependence on manual processing and high cost in the prior art are solved, different interface protocol requests are processed, and the labor cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an intelligent charging pile interface docking platform system, request processing method, and medium. Background Technology

[0002] With the rapid development of the new energy vehicle industry and the continuous improvement of charging infrastructure, the need for interconnection and interoperability among charging pile operators is becoming increasingly urgent. Currently, the charging pile industry has multiple interface standards and protocols, and data interoperability between different operators faces many technical challenges.

[0003] In the process of developing this invention, the inventors discovered the following shortcomings in the existing technology: Currently, existing interface integration technologies mainly adopt traditional point-to-point integration methods, including integration methods based on RESTful application programming interfaces. While these methods are relatively simple, they lack unified standards and intelligent processing capabilities. Another common approach is asynchronous integration based on message queues, such as using protocols like MQTT (Message Queuing Telemetry Transport) for data transmission, which is suitable for scenarios with high real-time requirements.

[0004] Furthermore, in terms of data processing, existing technologies primarily rely on technical personnel to develop, manually configure, and hard-code data format conversions and field mappings. When new systems or protocols need to be integrated, the relevant code must be redeveloped and deployed, resulting in long development cycles and high maintenance costs. Error handling mechanisms are relatively simple, mainly relying on retry mechanisms and manual intervention, lacking intelligent anomaly detection and automatic recovery capabilities. Summary of the Invention

[0005] This invention provides an intelligent charging pile interface docking platform system, request processing method, and medium to process requests from different interface protocols, thereby reducing labor costs.

[0006] According to one aspect of the present invention, an intelligent charging pile interface docking platform system is provided, wherein the intelligent charging pile interface docking platform system includes: a data layer, a business processing layer, an AIAgent intelligent processing layer, and an access layer;

[0007] The data layer includes a relational database component, a caching system component, a message queue component, and a search engine component. The data layer is used to receive current charging requests from charging piles and also for data storage and management services.

[0008] The business processing layer is used to receive the current charging request sent by the data layer and send the current charging request to the AI ​​Agent intelligent processing layer;

[0009] The AI ​​Agent intelligent processing layer includes a protocol identification agent, a data conversion agent, an error handling agent, a load balancing agent, and a monitoring and early warning agent; the AI ​​Agent intelligent processing layer performs request processing operations on the current charging pile charging request to obtain the corresponding target charging pile charging request analysis and processing results;

[0010] The access layer feeds back the analysis and processing results of the target charging pile's charging request through the unified interface service corresponding to the application programming interface gateway.

[0011] According to another aspect of the present invention, a request processing method is provided, wherein the request is executed by an AI Agent intelligent processing layer in an intelligent charging pile interface docking platform system, comprising:

[0012] The Agent is identified by protocol. Features of the received current charging request from the charging pile are extracted, and the target request protocol identification result and the current request feature extraction vector are obtained by performing identification processing operation through a pre-set request protocol classification and identification model.

[0013] The data conversion agent converts the current request feature extraction vector into a vector data format based on the target request protocol identification result, thereby obtaining the current request data format conversion vector.

[0014] The real-time anomaly detection module in the error handling agent performs anomaly analysis on the current request data format conversion vector. If it is normal, the current request data format conversion vector is sent to the load balancing agent.

[0015] The load balancing agent performs index weight scheduling analysis and processing on the current request data format conversion vector to obtain the target charging pile charging request analysis and processing result.

[0016] The monitoring and early warning agent monitors the current charging request processing process of the charging pile through the infrastructure monitoring module, application monitoring module, and business monitoring module. If any abnormality is found, an early warning operation is performed through the intelligent early warning module.

[0017] According to another aspect of the present invention, a request processing apparatus is provided, wherein the request is executed by an AI Agent intelligent processing layer in an intelligent charging pile interface docking platform system, comprising:

[0018] The target request protocol identification result and current request feature extraction vector determination module is used to identify the Agent through the protocol, extract features from the received current charging request of the charging pile, and perform identification processing operations through a pre-set request protocol classification and identification model to obtain the target request protocol identification result and the current request feature extraction vector;

[0019] The current request data format conversion vector determination module is used to convert the current request feature extraction vector into a vector data format based on the target request protocol identification result by the data conversion agent, so as to obtain the current request data format conversion vector;

[0020] The current request data format conversion vector sending module is used to perform anomaly analysis on the current request data format conversion vector through the real-time anomaly detection module in the error handling agent. If it is normal, the current request data format conversion vector is sent to the load balancing agent.

[0021] The target charging pile charging request analysis and processing result determination module is used to perform index weight scheduling analysis and processing operations on the current request data format conversion vector through the load balancing Agent to obtain the target charging pile charging request analysis and processing result.

[0022] The monitoring and early warning module is used to monitor the current charging request processing process of the charging pile through the infrastructure monitoring module, application monitoring module, and business monitoring module in the monitoring and early warning agent. If an anomaly is found, an early warning operation is performed through the intelligent early warning module.

[0023] According to another aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the request processing method described in any embodiment of the present invention.

[0024] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the request processing method described in any embodiment of the present invention.

[0025] The technical solution of this invention, the intelligent charging pile interface docking platform system, includes: a data layer, a business processing layer, an AI Agent intelligent processing layer, and an access layer. The data layer includes a relational database component, a caching system component, a message queue component, and a search engine component. The data layer is used to receive current charging pile charging requests and also for data storage and management services. The business processing layer receives the current charging pile charging request sent by the data layer and sends the current charging pile charging request to the AI ​​Agent intelligent processing layer. The AI ​​Agent intelligent processing layer includes a protocol identification agent, a data conversion agent, an error handling agent, a load balancing agent, and a monitoring and early warning agent. The AI ​​Agent intelligent processing layer performs request processing operations on the current charging pile charging request to obtain the corresponding target charging pile charging request analysis and processing results. The access layer feeds back the target charging pile charging request analysis and processing results through the unified interface service corresponding to the application programming interface gateway. This solves the problems of lack of unified standards and intelligent processing capabilities, reliance on manual processing, and high costs in the prior art, enabling the processing of requests from different interface protocols and reducing labor costs.

[0026] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1a This is a schematic diagram of the structure of an intelligent charging pile interface docking platform system provided in Embodiment 1 of the present invention;

[0029] Figure 1b This is a schematic diagram of the protocol identification Agent in an intelligent charging pile interface docking platform system provided according to Embodiment 1 of the present invention;

[0030] Figure 1c This is a schematic diagram of the structure of a data conversion agent in an intelligent charging pile interface docking platform system provided according to Embodiment 1 of the present invention;

[0031] Figure 1dThis is a schematic diagram of the structure of an error handling agent in an intelligent charging pile interface docking platform system provided according to Embodiment 1 of the present invention;

[0032] Figure 1e This is a schematic diagram of the load balancing Agent in an intelligent charging pile interface docking platform system provided according to Embodiment 1 of the present invention;

[0033] Figure 1f This is a schematic diagram of the monitoring and early warning agent in an intelligent charging pile interface docking platform system provided according to Embodiment 1 of the present invention;

[0034] Figure 2 This is a flowchart of a request processing method provided according to Embodiment 2 of the present invention;

[0035] Figure 3 This is a schematic diagram of the structure of a request processing device according to Embodiment 3 of the present invention;

[0036] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0038] It should be noted that the terms "target," "current," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0039] It is worth noting that the information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse; if the user chooses to refuse, the process will proceed to the expert decision-making process.

[0040] Example 1

[0041] Figure 1a This is a schematic diagram of the structure of an intelligent charging pile interface docking platform system according to Embodiment 1 of the present invention. The intelligent charging pile interface docking platform system 100 includes: a data layer 110, a business processing layer 120, an AIAgent intelligent processing layer 130, and an access layer 140;

[0042] The data layer 110 includes a relational database component 111, a cache system component 112, a message queue component 113, and a search engine component 114. The data layer 110 is used to receive the current charging request of the charging pile and is also used for data storage and management services.

[0043] The business processing layer 120 is used to receive the current charging request sent by the data layer 110 and send the current charging request to the AIAgent intelligent processing layer 130.

[0044] The AI ​​Agent intelligent processing layer 130 includes a protocol identification agent 131, a data conversion agent 132, an error handling agent 133, a load balancing agent 134, and a monitoring and early warning agent 135; the AI ​​Agent intelligent processing layer 130 performs request processing operations on the current charging pile charging request to obtain the corresponding target charging pile charging request analysis and processing results;

[0045] The access layer 140 feeds back the analysis and processing results of the target charging pile's charging request through the unified interface service corresponding to the application programming interface gateway.

[0046] Optionally, the service processing layer may also include: the service processing layer is further used for service logic processing; the service processing layer includes a charging station information management component, a device status management component, an order processing component, and a billing management component.

[0047] In this embodiment, the business data layer and the AI ​​Agent intelligent processing layer work closely together, and the intelligent capabilities provided by the AI ​​Agent intelligent processing layer can be used to improve the efficiency of business processing.

[0048] Furthermore, the AIAgent intelligent processing layer includes a protocol identification agent, a data transformation agent, an error handling agent, a load balancing agent, and a monitoring and early warning agent. Each agent has independent intelligent processing capabilities, implementing its corresponding function through machine learning algorithms.

[0049] Optionally, the access layer further includes: an application programming interface gateway, a protocol adapter, and a security module; wherein, the application programming interface gateway is used to provide feedback on the analysis and processing results of the target charging pile's charging request through a unified interface service; the protocol adapter is used to convert the interface protocol corresponding to the analysis and processing results of the target charging pile's charging request through a preset interface protocol format conversion method; and the security module is used to verify the security functions of the analysis and processing results of the target charging pile's charging request through preset token authentication, security verification, and data encryption and decryption methods.

[0050] The application programming interface (API) gateway handles request routing, load balancing, and rate limiting, supporting multiple protocols such as HTTP / HTTPS, WebSocket, and MQTT. The protocol adapter unifies the encapsulation of different API protocols through API protocol format conversion, transforming various protocol formats into an internal standard format. The security module verifies the results of the target charging pile's charging request analysis, implementing security functions such as token authentication, digital signature verification, and data encryption / decryption to ensure data transmission security.

[0051] Optionally, the following modules are included: The protocol identification agent comprises a request feature extraction module, a request protocol classification and identification model identification module, a protocol version detection module, and a protocol adaptation strategy module; the data conversion agent comprises a field mapping learning module, a data type conversion module, a data quality detection module, and a conversion rule management module; the error handling agent comprises a real-time anomaly detection module, an anomaly pattern identification module, a root cause analysis module, and an automatic recovery module; the load balancing agent comprises a load assessment model, an intelligent scheduling algorithm module, a predictive scaling module, and a failover mechanism module; and the monitoring and early warning agent comprises an infrastructure monitoring module, an application monitoring module, a business monitoring module, and an intelligent early warning module.

[0052] Specifically, Figure 1b This is a schematic diagram of the protocol identification agent in an intelligent charging pile interface docking platform system. The protocol identification agent 131 includes a request feature extraction module 1311, a request protocol classification and identification model identification module 1312, a protocol version detection module 1313, and a protocol adaptation strategy module 1314.

[0053] The protocol identification agent employs deep learning technology to achieve automatic protocol identification. The agent includes a request feature extraction module, which extracts features from the input current charging request. The resulting current request feature vector includes features such as vector header information, request body structure, field names, and vector data type. The feature extraction process uses natural language processing technology to convert text features into vector representations.

[0054] Furthermore, the request protocol classification and recognition model's recognition module employs a deep neural network architecture, including an embedding layer, an LSTM (Long Short-Term Memory Networks) layer, and a fully connected layer. The embedding layer transforms the current request feature extraction vector into a dense vector representation, the LSTM layer captures sequence features and contextual information, and the fully connected layer performs the final classification prediction. Model training utilizes a large amount of historical API call data, and various types of protocols can be used, specifically including cloud fast charging protocols, custom protocols from different companies, and other sample types.

[0055] A dedicated adaptation module is required for the cloud fast charging protocol. This module is designed strictly according to the cloud fast charging interface specifications, supporting core functions such as token authentication, charging station information query, and device status push. The token authentication adapter implements the cloud fast charging authentication process, including token application, verification, and refresh. The adapter automatically handles security operations such as encryption / decryption and signature verification to ensure the security and reliability of the authentication process. Token management employs a caching mechanism to avoid frequent authentication requests and improve system performance. The charging station information query adapter implements functions such as paginated query, conditional filtering, and data caching. Query results are automatically converted to a standard format, supporting multiple output formats. The caching strategy is dynamically adjusted based on the data update frequency to balance data real-time performance and system performance. The device status push adapter implements functions such as real-time status reception, data verification, and event distribution. Pushed data undergoes intelligent verification to ensure data integrity and correctness. The event distribution mechanism supports multiple subscribers, enabling real-time data synchronization and processing.

[0056] The data synchronization mechanism supports two modes: full synchronization and incremental synchronization. Full synchronization is used for data initialization, while incremental synchronization is used for routine updates. The synchronization process employs breakpoint resumption technology, supporting automatic recovery after network interruptions. Synchronization status is monitored in real time, and alarms are triggered promptly in case of anomalies.

[0057] Furthermore, the protocol version detection module identifies different versions of the same protocol by analyzing subtle differences in protocol characteristics. The version detection algorithm combines rule matching and machine learning methods. First, rule matching is used to identify obvious version identifiers, and then machine learning algorithms are used to analyze implicit version characteristics.

[0058] In addition, the protocol adaptation strategy module automatically selects the appropriate processing flow based on the recognition results. For known protocols, the corresponding adapter is directly invoked for processing; for unknown protocols, an online learning mechanism is activated to quickly learn the characteristics of the new protocol using a small number of samples.

[0059] Specifically, Figure 1c This is a schematic diagram of the structure of a data conversion agent in an intelligent charging pile interface docking platform system. The data conversion agent 132 includes a field mapping learning module 1321, a data type conversion module 1322, a data quality detection module 1323, and a conversion rule management module 1324.

[0060] The data transformation agent enables intelligent data format conversion and field mapping. It employs a hybrid architecture, combining a rule engine and machine learning algorithms to provide flexible and efficient data transformation capabilities. The data transformation agent can include a field mapping learning module, a data type conversion module, a data quality detection module, and a transformation rule management module.

[0061] Specifically, the field mapping learning module can automatically discover mapping relationships between fields using unsupervised learning algorithms. The algorithm first calculates the semantic similarity of field names, converting them into vector representations using word vector techniques, and then calculates the cosine similarity between the vectors. For field pairs with high similarity, further analysis is conducted on data types, value ranges, and statistical characteristics to determine the final mapping relationship.

[0062] Furthermore, the data type conversion module handles conversions between different data types, including basic types such as strings, numbers, dates, and booleans, as well as composite types such as arrays and objects. Conversion rules are based on the semantics and formatting requirements of the data types and support custom conversion functions. For complex data structures, such as nested objects and arrays, a recursive conversion algorithm is used.

[0063] Accordingly, the data quality detection module uses statistical learning methods to detect data anomalies, including missing values, outliers, and format errors. The detection algorithm builds a data quality model based on the statistical characteristics of historical data and evaluates the quality of the input data in real time. For detected data quality issues, the system provides various repair strategies, including default value imputation, interpolation calculation, and format correction.

[0064] In addition, the transformation rule management module provides dynamic configuration and update capabilities for rules. Rules are defined using declarative syntax and support complex logic such as conditional statements, loop processing, and function calls. The rule engine employs a high-performance execution engine, supporting parallel execution of rules and caching optimization.

[0065] Specifically, Figure 1d This is a schematic diagram of the structure of an error handling agent in an intelligent charging pile interface docking platform system. The error handling agent 133 includes a real-time anomaly detection module 1331, an anomaly pattern recognition module 1332, a root cause analysis module 1333, and an automatic recovery module 1334.

[0066] The error handling agent enables intelligent anomaly detection and fault handling. It employs a multi-layered anomaly detection architecture, comprising a real-time anomaly detection module, an anomaly pattern recognition module, and a root cause analysis module, as well as an automatic recovery module.

[0067] Specifically, the real-time anomaly detection module uses streaming computing technology to detect anomalies in real-time data from API calls. The detection algorithms are based on statistical learning and machine learning methods, including threshold-based detection, statistical distribution-based detection, and machine learning-based detection. Detection metrics include multiple dimensions such as response time, error rate, throughput, and resource utilization.

[0068] Furthermore, the anomaly pattern recognition module uses unsupervised learning algorithms to identify patterns and types of anomalies. The algorithm first extracts features from anomalous events, including temporal, frequency, and correlation features, and then uses clustering algorithms to categorize similar anomalous events, forming an anomaly pattern library. The pattern recognition results are used to guide the selection of subsequent processing strategies.

[0069] Furthermore, the root cause analysis module employs causal reasoning and expert system techniques to analyze the root causes of anomalies. The analysis process combines system topology, dependencies, historical fault information, and uses graph algorithms and inference engines to locate the fault source. The root cause analysis results are presented visually to help operations and maintenance personnel quickly understand the fault situation.

[0070] Accordingly, the automatic recovery module automatically selects an appropriate recovery strategy based on the type and severity of the anomaly. Recovery strategies include retry mechanisms, service degradation, traffic switching, and resource scaling. The retry mechanism employs an exponential backoff algorithm to avoid a cascading failure effect. Service degradation provides a simplified version of core functions to ensure basic service availability. Traffic switching forwards requests to healthy service instances to prevent the failure from propagating.

[0071] Specifically, Figure 1e This is a schematic diagram of the structure of a load balancing agent in an intelligent charging pile interface docking platform system. The load balancing agent 134 includes a load assessment model 1341, an intelligent scheduling algorithm module 1342, a predictive expansion module 1343, and a failover mechanism module 1344.

[0072] For a load balancing agent, intelligent request distribution and resource scheduling are implemented. This agent employs a multi-dimensional load assessment model, combined with machine learning algorithms, to achieve the optimal load allocation strategy. A load balancing agent can include a load assessment model, an intelligent scheduling algorithm module, a predictive scaling module, and a failover mechanism module.

[0073] Specifically, the load assessment model comprehensively considers multiple dimensions of metrics, including CPU utilization, memory utilization, network bandwidth, disk I / O, response time, and concurrent connections. Each metric has a corresponding weight, and the weight values ​​are dynamically adjusted through machine learning algorithms to adapt to different business scenarios and system states.

[0074] Furthermore, the intelligent scheduling algorithm module can be based on reinforcement learning technology, learning the optimal scheduling strategy through interaction with the environment. The algorithm takes the system state as input and the scheduling decision as output, evaluating the scheduling effect through a reward function. The reward function comprehensively considers multiple objectives such as response time, resource utilization, and system stability, achieving multi-objective optimization.

[0075] Correspondingly, the predictive scaling module uses time series analysis and machine learning algorithms to predict future load trends. The predictive algorithm considers factors such as historical load data, business cycle time, and external events to identify load peaks in advance and trigger an automatic scaling mechanism. Scaling strategies include horizontal and vertical scaling, selecting the appropriate method based on load characteristics.

[0076] In addition, the failover mechanism module implements health checks and automatic failover of service instances. Health checks include both proactive and reactive methods. Proactive checks periodically send heartbeat requests, while reactive checks monitor the success rate and response time of these requests. When a service instance failure is detected, traffic is automatically switched to a healthy instance to ensure service continuity.

[0077] Specifically, Figure 1f This is a schematic diagram of the monitoring and early warning agent in an intelligent charging pile interface docking platform system. The monitoring and early warning agent 135 includes an infrastructure monitoring module 1351, an application monitoring module 1352, a business monitoring module 1353, and an intelligent early warning module 1354.

[0078] The monitoring and early warning agent provides comprehensive system monitoring and intelligent early warning capabilities. It employs a multi-layered monitoring architecture, comprising infrastructure monitoring, application monitoring, and business monitoring modules, as well as an intelligent early warning module for issuing alerts.

[0079] Specifically, the infrastructure monitoring module monitors the operational status of infrastructure such as servers, networks, and storage. Monitoring metrics include the usage of resources such as the CPU, memory, disk, and network, as well as abnormal events such as hardware failures and network latency. Monitoring data is collected through an agent program and transmitted to the monitoring center in real time.

[0080] In addition, the application monitoring module monitors the application's performance and health. Monitoring metrics include performance indicators such as request volume, response time, error rate, and throughput, as well as operational indicators such as database connections and cache hit rate. Monitoring data is collected through application event tracking, supporting distributed tracing and link analysis.

[0081] Furthermore, the business monitoring module monitors key business metrics and user experience. Monitoring metrics include API call success rate, data synchronization latency, AI model accuracy, and user satisfaction. Business monitoring data is collected through business logs and user feedback, supporting real-time analysis and trend prediction.

[0082] Correspondingly, the intelligent early warning module uses machine learning algorithms for anomaly detection and early warning. The early warning algorithm builds a normal behavior model based on historical monitoring data, detecting abnormal behaviors that deviate from the normal pattern in real time. Early warning rules support multiple combinations of conditions, including threshold conditions, trend conditions, and correlation conditions. Early warning information is sent through various channels, including email, SMS, and instant messaging.

[0083] The technical solution of this invention, the intelligent charging pile interface docking platform system, includes: a data layer, a business processing layer, an AI Agent intelligent processing layer, and an access layer. The data layer includes a relational database component, a caching system component, a message queue component, and a search engine component. The data layer is used to receive current charging pile charging requests and also for data storage and management services. The business processing layer receives the current charging pile charging request sent by the data layer and sends the current charging pile charging request to the AI ​​Agent intelligent processing layer. The AI ​​Agent intelligent processing layer includes a protocol identification agent, a data conversion agent, an error handling agent, a load balancing agent, and a monitoring and early warning agent. The AI ​​Agent intelligent processing layer performs request processing operations on the current charging pile charging request to obtain the corresponding target charging pile charging request analysis and processing results. The access layer feeds back the target charging pile charging request analysis and processing results through the unified interface service corresponding to the application programming interface gateway. This solves the problems of lack of unified standards and intelligent processing capabilities, reliance on manual processing, and high costs in the prior art, enabling the processing of requests from different interface protocols and reducing labor costs.

[0084] Example 2

[0085] Figure 2 The flowchart of a request processing method is provided for Embodiment 2 of the present invention. This embodiment is applicable to the intelligent processing of charging requests for charging piles. The method can be executed by a request processing device, which can be implemented in hardware and / or software.

[0086] Correspondingly, such as Figure 2 As shown, the method includes:

[0087] S210. Identify the Agent through the protocol, extract features from the received current charging request from the charging pile, and perform identification processing operations through a pre-set request protocol classification and identification model to obtain the target request protocol identification result and the current request feature extraction vector.

[0088] Optionally, the step of identifying the Agent through the protocol, extracting features from the received current charging request from the charging pile, and performing identification processing through a pre-set request protocol classification and identification model to obtain a current request feature extraction vector and a target request protocol identification result includes: extracting features from the received current charging request from the Agent through the protocol identification model to obtain a current request feature extraction vector; wherein, the current request feature extraction vector includes vector header information, request body structure, field names, and vector data type; performing protocol classification processing on the current request feature extraction vector through the request protocol classification and identification model in the request protocol classification and identification model module to obtain a target protocol classification result; determining the target version corresponding to the target protocol classification result through the protocol version detection module based on the target protocol classification result; and performing identification processing through the protocol adaptation strategy module based on the target protocol classification result and the target version to obtain a target request protocol identification result.

[0089] In this embodiment, a request feature extraction module can be used to extract features from the current charging request of the charging station, resulting in a current request feature extraction vector. Specifically, the extracted current request feature extraction vector may include vector header information, request body structure, field names, and vector data type.

[0090] Furthermore, the current request feature extraction vector can be classified using a request protocol classification and recognition model to obtain the target protocol classification result. Combined with the target protocol classification result, the specific target version information corresponding to the target protocol classification result can be analyzed in detail. Then, the protocol adaptation strategy module can be used for adaptation processing to obtain the target request protocol recognition result.

[0091] S220. The data conversion agent converts the current request feature extraction vector into a vector data format based on the target request protocol identification result, thereby obtaining the current request data format conversion vector.

[0092] In this embodiment, the corresponding format can be converted by the data conversion agent. In this way, the protocol identification agent can automatically identify the protocol request type, and the data conversion agent can automatically generate field mapping relationships, which can quickly process different protocol requests. This can improve the efficiency of request processing and reduce workload and labor costs.

[0093] S230. The real-time anomaly detection module in the error handling agent performs anomaly analysis on the current request data format conversion vector. If it is normal, the current request data format conversion vector is sent to the load balancing agent.

[0094] Optionally, after performing anomaly analysis on the current request data format conversion vector by the real-time anomaly detection module in the error handling agent, the method further includes: if an anomaly is found, identifying the anomaly pattern of the current request data format conversion vector by the anomaly pattern recognition module in the error handling agent to obtain the current request anomaly pattern; based on the current request anomaly pattern, performing root cause analysis on the current request data format conversion vector by the root cause analysis module to determine the current request root cause information; based on the current request root cause information, and in conjunction with the automatic recovery module, recovering the current request data format conversion vector to obtain the current recovered request data format conversion vector; and sending the current recovered request data format conversion vector to the load balancing agent.

[0095] In this embodiment, if the error handling agent performs anomaly analysis on the current request data format conversion vector, and it is normal, the current request data format conversion vector can be sent to the load balancing agent. If it is abnormal, it is also necessary to combine the anomaly pattern recognition module, the root cause analysis module, and the automatic recovery module to identify the anomaly pattern, analyze the root cause, and recover the current request data format conversion vector, so as to further obtain the current recovered request data format conversion vector.

[0096] The advantage of this setup is that by using machine learning algorithms in the error handling agent, combined with the anomaly pattern recognition module, root cause analysis module, and automatic recovery module, intelligent detection and automatic recovery of anomalies are achieved, which significantly improves the accuracy of anomaly detection and enhances the stability of the system.

[0097] S240. The load balancing agent performs index weight scheduling analysis and processing on the current request data format conversion vector to obtain the target charging pile charging request analysis and processing result.

[0098] In this embodiment, the load assessment model, intelligent scheduling algorithm module, predictive scaling module, and failover mechanism module in the load balancing agent are used to perform index weight scheduling analysis and processing on the current request data format conversion vector to obtain the target charging pile charging request analysis and processing results.

[0099] This approach optimizes the request distribution strategy through reinforcement learning algorithms, significantly improving the system's data processing performance. In high-concurrency scenarios, the distribution strategy can be dynamically adjusted based on the server's real-time load status, increasing system throughput and reducing average response time. Data transformation performance is also significantly optimized.

[0100] S250. The current charging request processing process of the charging pile is monitored through the infrastructure monitoring module, application monitoring module, and business monitoring module in the monitoring and early warning agent. If there is an abnormality, an early warning operation is performed through the intelligent early warning module.

[0101] In this embodiment, the current charging request processing process of the charging pile can be monitored through the infrastructure monitoring module, application monitoring module, and business monitoring module in the monitoring and early warning agent. This significantly reduces the system's operation and maintenance costs and labor costs. Timely early warnings based on the intelligent early warning module ensure data security and flexibility in request processing during request analysis.

[0102] The technical solution of this invention involves: First, using a protocol identification agent, extracting features from the received current charging request from a charging pile. Then, using a pre-set request protocol classification and identification model, the agent performs identification processing to obtain the target request protocol identification result and the current request feature extraction vector. Next, using a data conversion agent, the agent converts the current request feature extraction vector to a vector data format based on the target request protocol identification result, resulting in a current request data format conversion vector. Finally, using a real-time anomaly detection module in an error handling agent, the agent performs anomaly analysis on the current request data format conversion vector. If normal, the current request data format conversion vector is sent to the load balancing agent. The load balancing agent then performs index weight scheduling analysis on the current request data format conversion vector to obtain the target charging pile charging request analysis processing result. Finally, using an infrastructure monitoring module, application monitoring module, and business monitoring module in a monitoring and early warning agent, the agent monitors the current charging request processing process. If anomalies are detected, the intelligent early warning module issues an early warning. It solves the problems of lack of unified standards and intelligent processing capabilities, reliance on manual processing and high costs in existing technologies. It enables the processing of requests from different interface protocols, reduces labor costs, improves the intelligence of charging pile request processing, saves time costs, improves the efficiency of request processing, and ensures the security and flexibility of request processing.

[0103] Example 3

[0104] Figure 3 This is a schematic diagram of a request processing device provided in Embodiment 3 of the present invention. The request processing device provided in this embodiment can be implemented by software and / or hardware, and can be configured in a terminal device or server to implement a request processing method in this embodiment of the invention. It is executed by the AIAgent intelligent processing layer in the intelligent charging pile interface platform system. Figure 3 As shown, the device includes: a target request protocol identification result and current request feature extraction vector determination module 310, a current request data format conversion vector determination module 320, a current request data format conversion vector sending module 330, a target charging pile charging request analysis and processing result determination module 340, and a monitoring and early warning operation module 350.

[0105] The target request protocol identification result and current request feature extraction vector determination module 310 is used to identify the Agent through the protocol, extract features from the received current charging request of the charging pile, and perform identification processing operations through a pre-set request protocol classification and identification model to obtain the target request protocol identification result and the current request feature extraction vector.

[0106] The current request data format conversion vector determination module 320 is used to convert the current request feature extraction vector into a vector data format based on the target request protocol identification result by the data conversion agent, so as to obtain the current request data format conversion vector;

[0107] The current request data format conversion vector sending module 330 is used to perform anomaly analysis on the current request data format conversion vector through the real-time anomaly detection module in the error handling agent. If it is normal, the current request data format conversion vector is sent to the load balancing agent.

[0108] The target charging pile charging request analysis and processing result determination module 340 is used to perform index weight scheduling analysis and processing operations on the current request data format conversion vector through the load balancing Agent to obtain the target charging pile charging request analysis and processing result.

[0109] The monitoring and early warning operation module 350 is used to monitor the current charging request processing process of the charging pile through the infrastructure monitoring module, application monitoring module, and business monitoring module in the monitoring and early warning agent. If there is an anomaly, an early warning operation is performed through the intelligent early warning module.

[0110] The technical solution of this invention involves: First, using a protocol identification agent, extracting features from the received current charging request from a charging pile. Then, using a pre-set request protocol classification and identification model, the agent performs identification processing to obtain the target request protocol identification result and the current request feature extraction vector. Next, using a data conversion agent, the agent converts the current request feature extraction vector to a vector data format based on the target request protocol identification result, resulting in a current request data format conversion vector. Finally, using a real-time anomaly detection module in an error handling agent, the agent performs anomaly analysis on the current request data format conversion vector. If normal, the current request data format conversion vector is sent to the load balancing agent. The load balancing agent then performs index weight scheduling analysis on the current request data format conversion vector to obtain the target charging pile charging request analysis processing result. Finally, using an infrastructure monitoring module, application monitoring module, and business monitoring module in a monitoring and early warning agent, the agent monitors the current charging request processing process. If anomalies are detected, the intelligent early warning module issues an early warning. It solves the problems of lack of unified standards and intelligent processing capabilities, reliance on manual processing and high costs in existing technologies. It enables the processing of requests from different interface protocols, reduces labor costs, improves the intelligence of charging pile request processing, saves time costs, improves the efficiency of request processing, and ensures the security and flexibility of request processing.

[0111] Based on the above embodiments, the target request protocol identification result and current request feature extraction vector determination module 310 can be specifically used to: extract features from the received current charging pile charging request through the protocol identification Agent to obtain a current request feature extraction vector; wherein, the current request feature extraction vector includes vector header information, request body structure, field names, and vector data type; perform protocol classification processing on the current request feature extraction vector through the request protocol classification and identification model in the request protocol classification and identification model module to obtain a target protocol classification result; determine the target version corresponding to the target protocol classification result through the protocol version detection module based on the target protocol classification result; and perform identification processing operation through the protocol adaptation strategy module based on the target protocol classification result and the target version to obtain the target request protocol identification result.

[0112] Based on the above embodiments, the system further includes a current recovery request data format conversion vector determination module, which can be specifically used for: after the real-time anomaly detection module in the error handling agent performs anomaly analysis on the current request data format conversion vector, if an anomaly is found, the anomaly pattern recognition module in the error handling agent identifies the anomaly pattern of the current request data format conversion vector to obtain the current request anomaly pattern; based on the current request anomaly pattern, the root cause analysis module performs root cause analysis on the current request data format conversion vector to determine the current request root cause information; based on the current request root cause information, the automatic recovery module restores the current request data format conversion vector to obtain the current recovery request data format conversion vector; and the current recovery request data format conversion vector is sent to the load balancing agent.

[0113] The request processing apparatus provided in the embodiments of the present invention can execute the request processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0114] Example 4

[0115] Figure 4A schematic diagram of an electronic device 10, which can be used to implement Embodiment 4 of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0116] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0117] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0118] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as request processing methods.

[0119] In some embodiments, the request processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the request processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured by any other suitable means (e.g., by means of firmware) to execute the request processing method, which may be executed by the AI ​​Agent intelligent processing layer in the intelligent charging pile interface docking platform system.

[0120] The method includes: using a protocol identification agent to extract features from the received current charging request from the charging pile, and performing identification processing operations through a pre-set request protocol classification and identification model to obtain the target request protocol identification result and the current request feature extraction vector; using a data conversion agent to convert the current request feature extraction vector into a vector data format based on the target request protocol identification result to obtain a current request data format conversion vector; using a real-time anomaly detection module in an error handling agent to perform anomaly analysis on the current request data format conversion vector, and if normal, sending the current request data format conversion vector to the load balancing agent; using a load balancing agent to perform indicator weight scheduling analysis processing operations on the current request data format conversion vector to obtain the target charging pile charging request analysis processing result; and using an infrastructure monitoring module, application monitoring module, and business monitoring module in a monitoring and early warning agent to monitor the current charging request processing process, and if an anomaly is found, issuing an early warning operation through the intelligent early warning module.

[0121] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0124] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0125] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0126] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0127] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0128] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0129] Example 5

[0130] Embodiment 5 of the present invention also provides a computer-readable storage medium, wherein the computer-readable instructions, when executed by a computer processor, are used to execute a request processing method, which is executed by the AIAgent intelligent processing layer in the intelligent charging pile interface docking platform system. The method includes: extracting features from the received current charging pile charging request through a protocol identification agent, and performing identification processing operations through a pre-set request protocol classification and identification model to obtain a target request protocol identification result and a current request feature extraction vector; converting the current request feature extraction vector into a vector data format according to the target request protocol identification result through a data conversion agent to obtain a current request data format conversion vector; performing anomaly analysis on the current request data format conversion vector through a real-time anomaly detection module in an error handling agent, and if normal, sending the current request data format conversion vector to the load balancing agent; performing index weight scheduling analysis processing operations on the current request data format conversion vector through the load balancing agent to obtain the target charging pile charging request analysis processing result; and monitoring the current charging pile charging request processing process through an infrastructure monitoring module, application monitoring module, and business monitoring module in a monitoring and early warning agent, and performing an early warning operation through the intelligent early warning module if an anomaly is found.

[0131] Of course, the computer-executable instructions provided in the embodiments of the present invention, which include a computer-readable storage medium, are not limited to the method operations described above, but can also perform related operations in the request processing provided in any embodiment of the present invention.

[0132] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0133] It is worth noting that in the above-described embodiments of request processing, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0134] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An intelligent charging pile interface docking platform system, characterized in that, The intelligent charging pile interface docking platform system includes: a data layer, a business processing layer, an AI Agent intelligent processing layer, and an access layer; The data layer includes a relational database component, a caching system component, a message queue component, and a search engine component. The data layer is used to receive current charging requests from charging piles and also for data storage and management services. The business processing layer is used to receive the current charging request sent by the data layer and send the current charging request to the AI ​​Agent intelligent processing layer; The AI ​​Agent intelligent processing layer includes a protocol identification agent, a data conversion agent, an error handling agent, a load balancing agent, and a monitoring and early warning agent; the AI ​​Agent intelligent processing layer performs request processing operations on the current charging pile charging request to obtain the corresponding target charging pile charging request analysis and processing results; The access layer feeds back the analysis and processing results of the target charging pile's charging request through the unified interface service corresponding to the application programming interface gateway.

2. The system according to claim 1, characterized in that, Also includes: The business processing layer is also used for business logic processing; The business processing layer includes a charging station information management component, an equipment status management component, an order processing component, and a billing management component.

3. The system according to claim 2, characterized in that, Also includes: The access layer includes an application programming interface gateway, a protocol adapter, and a security module; The application programming interface gateway is used to provide feedback on the analysis and processing results of the target charging pile charging request through a unified interface service. The protocol adapter is used to convert the interface protocol corresponding to the charging request analysis and processing result of the target charging pile through a preset interface protocol format conversion method. The security module is used to verify the security functions of the charging request analysis and processing results of the target charging pile through preset token authentication, security verification and data encryption and decryption methods.

4. The system according to claim 3, characterized in that, include: The protocol identification agent includes a request feature extraction module, a request protocol classification and identification model identification module, a protocol version detection module, and a protocol adaptation strategy module. The data conversion agent includes a field mapping learning module, a data type conversion module, a data quality detection module, and a conversion rule management module; The error handling agent includes a real-time anomaly detection module, an anomaly pattern recognition module, a root cause analysis module, and an automatic recovery module; The load balancing agent includes a load assessment model, an intelligent scheduling algorithm module, a predictive scaling module, and a failover mechanism module. The monitoring and early warning agent includes an infrastructure monitoring module, an application monitoring module, a business monitoring module, and an intelligent early warning module.

5. A request processing method, characterized in that, Executed by the AI ​​Agent intelligent processing layer in the intelligent charging pile interface platform system, including: The Agent is identified by protocol. Features of the received current charging request from the charging pile are extracted, and the target request protocol identification result and the current request feature extraction vector are obtained by performing identification processing operation through a pre-set request protocol classification and identification model. The data conversion agent converts the current request feature extraction vector into a vector data format based on the target request protocol identification result, thereby obtaining the current request data format conversion vector. The real-time anomaly detection module in the error handling agent performs anomaly analysis on the current request data format conversion vector. If it is normal, the current request data format conversion vector is sent to the load balancing agent. The load balancing agent performs index weight scheduling analysis and processing on the current request data format conversion vector to obtain the target charging pile charging request analysis and processing result. The monitoring and early warning agent monitors the current charging request processing process of the charging pile through the infrastructure monitoring module, application monitoring module, and business monitoring module. If any abnormality is found, an early warning operation is performed through the intelligent early warning module.

6. The method according to claim 5, characterized in that, The process of identifying the Agent through the protocol involves extracting features from the received current charging request from the charging station, and performing identification processing through a pre-set request protocol classification and recognition model to obtain the current request feature extraction vector and the target request protocol identification result, including: The Agent is identified through the protocol, and features are extracted from the received current charging request from the charging station to obtain the current request feature vector. The current request feature extraction vector includes vector header information, request body structure, field names, and vector data type; The request protocol classification and recognition model in the request protocol classification and recognition module is used to perform protocol classification processing on the current request feature extraction vector to obtain the target protocol classification result. Based on the target protocol classification result, the target version corresponding to the target protocol classification result is determined by the protocol version detection module; Based on the target protocol classification results and the target version, the target request protocol identification result is obtained by performing identification processing through the protocol adaptation strategy module.

7. The method according to claim 6, characterized in that, After the real-time anomaly detection module in the error handling agent performs anomaly analysis on the current request data format conversion vector, the method further includes: If an anomaly is detected, the anomaly pattern recognition module in the error handling agent will identify the anomaly pattern of the current request data format conversion vector to obtain the current request anomaly pattern. Based on the current request anomaly pattern, the root cause analysis module performs root cause analysis on the current request data format conversion vector to determine the current request root cause information. Based on the current request root cause information, the current request data format conversion vector is recovered by the automatic recovery module to obtain the current recovery request data format conversion vector; The current recovery request data format conversion vector is sent to the load balancing agent.

8. A request processing apparatus, characterized in that, Executed by the AI ​​Agent intelligent processing layer in the intelligent charging pile interface platform system, including: The target request protocol identification result and current request feature extraction vector determination module is used to identify the Agent through the protocol, extract features from the received current charging request of the charging pile, and perform identification processing operations through a pre-set request protocol classification and identification model to obtain the target request protocol identification result and the current request feature extraction vector; The current request data format conversion vector determination module is used to convert the current request feature extraction vector into a vector data format based on the target request protocol identification result by the data conversion agent, so as to obtain the current request data format conversion vector; The current request data format conversion vector sending module is used to perform anomaly analysis on the current request data format conversion vector through the real-time anomaly detection module in the error handling agent. If it is normal, the current request data format conversion vector is sent to the load balancing agent. The target charging pile charging request analysis and processing result determination module is used to perform index weight scheduling analysis and processing operations on the current request data format conversion vector through the load balancing Agent to obtain the target charging pile charging request analysis and processing result. The monitoring and early warning module is used to monitor the current charging request processing process of the charging pile through the infrastructure monitoring module, application monitoring module, and business monitoring module in the monitoring and early warning agent. If an anomaly is found, an early warning operation is performed through the intelligent early warning module.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a request processing method as described in any one of claims 5-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute a request processing method as described in any one of claims 5-7.

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