Interoperability test method and device of charging pile, intelligent load connector and system

Abstract

The invention provides an interoperability test method and device of a charging pile, an intelligent load connector and a system. The method comprises the following steps: collecting real-time monitoring data and test data of an interoperability test of the charging pile and an electric vehicle; according to the real-time monitoring data, entity nodes of the charging pile are determined, and the entity nodes comprise protocol versions, fault types, test conditions and environmental factors; according to the entity nodes of the charging piles, constructing an interoperability fault causal knowledge graph of the charging piles; according to the test data, abnormal events of the charging pile are determined, and the abnormal events comprise a communication signal abnormal event and an electrical parameter abnormal event; according to the abnormal event and the interoperability fault causal knowledge graph, reverse causal chain reasoning is carried out, the interoperability fault reason of the charging pile is determined, the interoperability test of the charging pile is completed, full-process automation of the interoperability test of the charging pile is achieved, and the test efficiency and accuracy are improved.

Description

Technical Field

[0001] This application relates to the field of electric vehicle charging technology, and in particular to an interoperability testing method, apparatus, smart load connector and system for charging piles. Background Technology

[0002] As a core component of electric vehicle energy replenishment, the interoperability of charging piles with various types of electric vehicles directly determines the stability and safety of the user's charging experience. During the research, development, production, and deployment of charging piles, rigorous interoperability testing is required to ensure that the charging piles are compatible with the charging protocols, electrical characteristics, and communication requirements of different vehicle models.

[0003] In existing technologies, charging pile interoperability testing mainly simulates interoperability performance in real-world scenarios by having actual vehicles interact with charging piles during charging; or it uses a dedicated testing platform to simulate the charging needs of electric vehicles, thereby completing the charging pile interoperability test.

[0004] However, the above testing process requires manual intervention, which affects the accuracy and consistency of the test results and reduces the overall testing efficiency. Summary of the Invention

[0005] This application provides a method, apparatus, smart load connector, and system for interoperability testing of charging piles, in order to improve the accuracy and efficiency of interoperability testing between charging piles and electric vehicles.

[0006] Firstly, this application provides an interoperability testing method for charging piles, applied to an interoperability testing system for charging piles. The interoperability testing system includes a cloud-based intelligent operation and maintenance platform and an intelligent load connector. The method includes:

[0007] Collect real-time monitoring and test data on the interoperability testing of charging piles and electric vehicles;

[0008] Based on the real-time monitoring data, the physical nodes of the charging pile are determined, and the physical nodes include protocol version, fault type, test conditions and environmental factors;

[0009] Based on the physical nodes of the charging pile, construct a causal knowledge graph of interoperability faults of the charging pile;

[0010] Based on the test data, abnormal events of the charging pile are determined, including abnormal communication signals and abnormal electrical parameters.

[0011] Based on the abnormal events and the interoperability failure causal knowledge graph, reverse causal chain reasoning is performed to determine the cause of the interoperability failure of the charging pile and complete the interoperability test of the charging pile.

[0012] Furthermore, based on the physical nodes of the charging pile, an interoperability fault causal knowledge graph of the charging pile is constructed, including:

[0013] Based on the physical nodes of the charging pile, identify and construct a set of relationships between the physical nodes, the set of relationships including causal relationships, attribute relationships, association relationships and temporal relationships;

[0014] Based on the entity nodes of the charging pile and the set of relationships, an interoperability fault causal knowledge graph of the charging pile is constructed.

[0015] Furthermore, based on the abnormal events and the interoperability failure causal knowledge graph, reverse causal chain reasoning is performed to determine the cause of the charging pile's interoperability failure, including:

[0016] The abnormal events are mapped to target entity nodes in the interoperability fault causal knowledge graph;

[0017] Based on the target entity node, the causal relationships in the interoperability fault causal knowledge graph are traced back to determine the probability of occurrence of each causal relationship;

[0018] The cause of the interoperability failure of the charging pile is determined based on the probability of occurrence of each of the aforementioned causal relationships.

[0019] Furthermore, before collecting real-time monitoring data and test data for interoperability testing between charging piles and electric vehicles, the method further includes:

[0020] Based on the load connector, the operating environment data of the charging pile is collected;

[0021] Simulate the load demand of the electric vehicle at different charging stages to generate dynamic load test data;

[0022] The charging process of the charging pile is simulated to generate key parameters of the charging pile, including voltage signal, current signal and PWM signal;

[0023] The operating environment data, dynamic load test data, and key parameters of the charging pile are fused together to generate test data.

[0024] Furthermore, based on the abnormal event and the interoperability failure causal knowledge graph, reverse causal chain reasoning is performed to determine the cause of the charging pile's interoperability failure. After completing the interoperability test of the charging pile, the method further includes:

[0025] Based on the cause of the interoperability failure of the charging pile, determine the operation and maintenance data of the charging pile;

[0026] Based on the operation and maintenance data of the charging pile, the causal relationships in the interoperability fault causal knowledge graph are updated to obtain an updated relationship set;

[0027] Based on the set of update relationships, an optimized test plan for the charging pile is generated, and the test optimization of the charging pile is completed.

[0028] Furthermore, based on the abnormal event and the interoperability failure causal knowledge graph, reverse causal chain reasoning is performed to determine the cause of the charging pile's interoperability failure. After completing the interoperability test of the charging pile, the method further includes:

[0029] Obtain updated test data, operation and maintenance feedback, and updated charging protocol standards for interoperability testing between the charging pile and the electric vehicle;

[0030] Based on the updated test data, the operation and maintenance feedback, and the updated charging protocol standard, the interoperability fault causal knowledge graph is updated.

[0031] Secondly, this application provides an interoperability testing device for charging piles, comprising:

[0032] The data acquisition module is used to collect real-time monitoring data and test data for interoperability testing between charging piles and electric vehicles;

[0033] The entity node determination module is used to determine the entity node of the charging pile based on the real-time monitoring data. The entity node includes protocol version, fault type, test conditions and environmental factors.

[0034] An interoperability fault causal knowledge graph construction module is used to construct an interoperability fault causal knowledge graph of the charging pile based on the physical nodes of the charging pile.

[0035] An abnormal event determination module is used to determine abnormal events of the charging pile based on the test data. The abnormal events include communication signal abnormal events and electrical parameter abnormal events.

[0036] The causal diagnosis module is used to perform reverse causal chain reasoning based on the abnormal event and the interoperability failure causal knowledge graph to determine the cause of the interoperability failure of the charging pile and complete the interoperability test of the charging pile.

[0037] Thirdly, this application provides a smart load connector, comprising:

[0038] A bidirectional connection structure is used to connect the charging pile under test and the test load;

[0039] An embedded interoperability test module is used to perform charging protocol parsing and verification.

[0040] A smart load connector housing for accommodating the bidirectional connection structure and the embedded interoperability test module;

[0041] The diagnostic unit is used to monitor key parameters in real time and perform data interaction.

[0042] Furthermore, a handle is provided on the top of the intelligent load connector housing;

[0043] The smart load connector housing is equipped with a touch display screen on the top front side;

[0044] The intelligent load connector housing has heat dissipation holes on the front bottom side.

[0045] Fourthly, this application provides an interoperability testing system for charging piles, including a cloud-based intelligent operation and maintenance platform, an intelligent load connector, and a computer program;

[0046] The intelligent load connector is as described in the third aspect;

[0047] The computer program is used to perform the method as described in any of the first aspects.

[0048] This application provides a method, apparatus, intelligent load connector, and system for interoperability testing of charging piles. It collects real-time monitoring and test data from the charging pile and electric vehicle for interoperability testing. Based on the real-time monitoring data, it identifies the physical nodes of the charging pile, including protocol version, fault type, test conditions, and environmental factors. Based on the physical nodes, it constructs a causal knowledge graph of interoperability faults for the charging pile. Based on the test data, it identifies abnormal events of the charging pile, including communication signal abnormalities and electrical parameter abnormalities. Based on the abnormal events and the causal knowledge graph of interoperability faults, it performs reverse causal chain reasoning to determine the causes of interoperability faults in the charging pile, completing the interoperability testing of the charging pile. This achieves full automation of the charging pile interoperability testing process, improving testing efficiency and accuracy. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0050] Figure 1 A schematic diagram of the interoperability testing system for the charging pile provided in this application;

[0051] Figure 2 A schematic diagram of the structure of the smart load connector provided in this application;

[0052] Figure 3 A flowchart illustrating an embodiment of the interoperability testing method for charging piles provided in this application;

[0053] Figure 4 A flowchart illustrating Embodiment 2 of the interoperability testing method for charging piles provided in this application;

[0054] Figure 5 A schematic diagram of the interoperability testing device for the charging pile provided in this application;

[0055] Figure 6 A schematic diagram of the interoperability testing equipment for the charging pile provided in this application.

[0056] Figure label:

[0057] 100 - Smart load connector; 101 - Smart load connector housing; 102 - Handle; 103 - Touch screen; 104 - Heat dissipation holes; 105 - Adapted standard charging interface; 106 - Communication analog interface.

[0058] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0059] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0060] This application primarily covers the entire lifecycle testing and operation and maintenance scenarios of charging piles, including compatibility verification during the R&D phase, protocol consistency testing before deployment, fault diagnosis during operation, and predictive maintenance in large-scale operation and maintenance. Its network architecture consists of load connectors (edge ​​devices) and a cloud-based operation and maintenance platform (central node). The load connectors are deployed at the charging pile site, establishing physical connections with the charging pile under test and the load equipment through a bidirectional connection structure. They collect key parameters such as the charging pile's PWM signal, PLC communication, and contactor status in real time and upload them to the cloud via Wi-Fi / 4G. The cloud-based operation and maintenance platform constructs a causal relationship network based on a knowledge graph, encompassing multi-dimensional information such as charging pile protocols, fault modes, and test scenarios. It uses causal reasoning algorithms to identify the root causes of faults and generates early warnings and optimization suggestions through predictive modules. This architecture, through the lightweight design of edge devices and the collaborative nature of the cloud platform, solves the pain points of traditional testing equipment being bulky, having limited functionality, and relying on manual experience for operation and maintenance.

[0061] Based on the above scenarios, the existing technical solutions have the following problems in the interoperability testing of charging piles: (1) Bulky and poor portability of equipment: Traditional simulated load equipment is bulky (>50kg) and difficult to deploy flexibly to different test locations, resulting in low efficiency of on-site testing; (2) Single function and incomplete test coverage: Existing equipment only supports limited protocol or electrical parameter testing, and cannot dynamically simulate the complex load requirements of electric vehicles (such as constant current / constant voltage switching) or inject diverse faults (such as CP / PP signal abnormalities), resulting in test results that cannot truly reflect the compatibility of charging piles with different models; (3) Low degree of automation: The collection, analysis and fault diagnosis of test data depend on manual operation, which is inefficient and prone to errors. For example, manual verification of protocol messages or manual adjustment of load parameters can take several hours for a single test; (4) Lack of intelligent diagnosis and predictive maintenance: Traditional equipment cannot automatically identify the root cause of faults from test data, and it is even more difficult to predict potential faults and provide optimization suggestions, resulting in lagging and passive operation and maintenance strategies.

[0062] To address the aforementioned technical challenges, this application integrates hardware testing equipment with a cloud-based intelligent analysis platform to construct a diagnostic and predictive operation and maintenance system centered on an interoperable fault causal knowledge graph. Specifically, it utilizes intelligent load connectors to achieve multi-protocol parsing, dynamic load simulation, and fault injection functions for charging piles. Combined with the cloud platform's causal reasoning diagnosis and knowledge graph self-learning mechanism, discrete test data is transformed into structured fault analysis results. Predictive operation and maintenance enables a shift from fault response to proactive prevention. This system combines the physical characteristics of hardware testing with the logical connections of software analysis, achieving accurate identification of fault root causes and dynamic optimization of operation and maintenance strategies through causal reasoning of the knowledge graph.

[0063] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0064] Figure 1 This is a schematic diagram of the interoperability testing system for the charging pile provided in this application. Figure 1 As shown, the interoperability testing system for charging piles includes intelligent load connectors and a cloud-based intelligent operation and maintenance platform.

[0065] The intelligent diagnostic unit in the intelligent load connector is configured to upload test data, diagnostic data, and real-time monitoring parameters generated by the embedded interoperability test module to the cloud-based intelligent operation and maintenance platform via a communication network.

[0066] The cloud-based intelligent operation and maintenance platform, as the core processing component of the charging pile interoperability testing system, is deployed on a cloud computing server cluster and communicates with the intelligent load connector through a secure encrypted channel.

[0067] Specifically, the cloud-based intelligent operation and maintenance platform includes a data receiving module, an interoperable fault causal knowledge graph construction module, a causal reasoning diagnosis module, a predictive operation and maintenance and optimization suggestion module, and a knowledge graph self-learning and updating module.

[0068] The data receiving module receives test data, diagnostic data, and real-time monitoring parameters uploaded by the intelligent diagnostic unit. This module establishes a connection with the intelligent load connector via standard network protocols (such as TCP / IP, MQTT, or HTTP / HTTPS). The data receiving module can process data streams concurrently uploaded from multiple intelligent load connectors and performs preliminary integrity checks and format conversions on the received data to ensure it conforms to the platform's internal processing specifications. The received data includes, but is not limited to, charging pile PWM signal waveform data, PLC communication message sequences, contactor switch status logs, real-time voltage and current curves, protocol parsing error codes, dynamic load simulation process records, and response reports after fault injection.

[0069] The interoperability fault causal knowledge graph construction module is used to construct a knowledge graph with entities and causal relationships from information such as charging pile protocol standards, communication timing, electrical parameters, hardware and software modules, historical fault modes, and test scenarios. This interoperability fault causal knowledge graph construction module obtains information from various structured and unstructured data sources through a data ETL (extract, transform, load) process.

[0070] Specifically, the interoperability fault causal knowledge graph construction module includes an entity recognition unit, a relation extraction unit, and a knowledge graph storage unit. The entity recognition unit uses natural language processing technology and rule matching algorithms to identify and extract entity nodes from text data such as charging protocol specification documents, historical maintenance work orders, and charging pile technical manuals. These entity nodes include charging pile model, protocol version, fault type (e.g., abnormal CP signal duty cycle, PLC communication message CRC check failure), test conditions (e.g., ambient temperature, charging power), and environmental factors. The relation extraction unit uses pattern matching and dependency parsing to identify and construct causal relationships (e.g., "PWM signal abnormality caused handshake failure"), attribute relationships (e.g., "charging pile type is DC fast charging"), association relationships (e.g., "GB / T27930 protocol is associated with CP signal"), or temporal relationship edges (e.g., "charging interruption occurred after CP abnormality was detected") between entity nodes. The knowledge graph storage unit uses a graph database (e.g., Neo4j) or a relational database (combined with graph structure indexes) to store the constructed interoperability fault causal knowledge graph, supporting efficient graph traversal and query operations.

[0071] The causal reasoning diagnostic module is used to identify the root cause of interoperability failures by starting from observed interoperability failure phenomena and performing reverse causal chain reasoning through the knowledge graph, based on the data received by the data receiving module and the interoperability failure causal knowledge graph.

[0072] The causal reasoning diagnostic module is configured to use observed events from protocol parsing data (e.g., error messages, timeout records), dynamic load simulation results (e.g., load curve mismatch with charging pile output), and fault injection reports (e.g., charging pile not responding as expected after injecting an abnormal CP signal) generated by the embedded interoperability test module and uploaded by the intelligent diagnostic unit as observational evidence. The module maps these abnormal events to symptom nodes in a knowledge graph and, based on the observed evidence, traces back the causal paths related to the abnormal events within the causal knowledge graph. The module iterates through all paths tracing back from the symptom nodes to the potential root causes and calculates the probability of occurrence of each causal path based on the weight of each causal relationship along the path.

[0073] The predictive operation and maintenance and optimization suggestion module is used to identify early warning causal factors that lead to potential interoperability problems through continuous learning and reasoning of causal knowledge graphs, and to provide early warning and operation and maintenance optimization suggestions based on early warning causal factors.

[0074] Specifically, the predictive maintenance and optimization suggestion module is configured to continuously analyze historical test data and maintenance data received by the data receiving module, and periodically update the strength and probability of causal relationships in the causal knowledge graph. Based on known causal chains and corresponding solutions in the knowledge graph, the predictive maintenance and optimization suggestion module generates preventive maintenance plans (e.g., suggestions to periodically calibrate the CP signal output circuit or update the charging pile firmware version) or optimized test scheme suggestions (e.g., suggestions to increase the test frequency of specific protocol interactions or adjust the test parameter range) for specific charging piles or test scenarios.

[0075] The knowledge graph self-learning and updating module automatically or semi-automatically updates and improves the entities, relationships, and causal reasoning rules of the interoperability fault causal knowledge graph based on new test data, feedback from maintenance personnel, and updates to charging protocol standards. This module can extract new entities and relationships from fault analysis reports manually entered by maintenance personnel, or discover new causal relationships from newly added test data using machine learning algorithms. When a new charging protocol standard is released, the module automatically parses the new standard document and updates the protocol entities and related rules in the knowledge graph. This module ensures that the knowledge graph continuously evolves and optimizes with the development of charging technology and the accumulation of maintenance experience.

[0076] The interoperability testing system for charging piles provided in this application achieves accurate causal reasoning and root cause localization of charging pile faults by constructing and dynamically updating an interoperability fault causal knowledge graph, significantly improving diagnostic efficiency and accuracy. Simultaneously, the system can perform predictive analysis based on the knowledge graph, proactively identifying potential interoperability risks and generating targeted operation and maintenance suggestions. This effectively reduces charging pile operation and maintenance costs, improves test coverage and reliability, and supports rapid adaptation to charging protocol standards and continuous optimization of the knowledge system.

[0077] Figure 2 A schematic diagram of the structure of the smart load connector provided in this application. Figure 2 As shown, the smart load connector 100 includes a bidirectional connection structure, an embedded interoperability test module, a smart load connector housing 101, and a smart diagnostic unit.

[0078] The intelligent load connector housing 101 is used to house the bidirectional connection structure, the embedded interoperability test module, and the intelligent diagnostic unit. In this application, the intelligent load connector housing 101 is made of high-strength engineering plastic or lightweight alloy material, with external dimensions less than or equal to 300mm × 200mm × 150mm and an overall weight of less than 5kg.

[0079] A handle 102 is fixedly mounted on the top of the intelligent load connector housing. The handle 102 is ergonomically designed for easy one-handed or two-handed gripping and moving of the intelligent load connector. A touch screen display 103 is embedded in the top front side of the intelligent load connector housing. The display area of ​​the touch screen 103 is no less than 5 inches and is used to display test data, diagnostic information, and the operating interface in real time. Ventilation holes 104 are provided on the bottom front side of the intelligent load connector housing 101. The ventilation holes 104, together with an internal cooling fan (not shown), constitute an active cooling system to ensure that the internal temperature of the intelligent load connector 100 remains within the rated operating range under prolonged high-power testing conditions, preventing overheating.

[0080] A bidirectional connection structure is provided at both ends of the smart load connector housing 101 to establish an electrical connection between the smart load connector and the charging pile under test, as well as an electrical connection with an external test load such as an actual electric vehicle or an auxiliary load box.

[0081] The bidirectional connection structure includes a standard charging interface 105, a communication simulation interface 106, and mechanical safety design. The standard charging interface 105 is configured according to the interface type of the charging pile under test, and its physical interface form includes at least one of GB / T20234, CCS1 / 2, or CHAdeMO, and includes corresponding control signal pins and power transmission pins. The communication simulation interface 106 physically corresponds to the standard charging interface 105, and integrates a programmable load module and an electric vehicle communication simulation interface.

[0082] The programmable load module can simulate the characteristics of electric vehicle battery packs and output constant current, constant voltage or constant power load characteristic curves. Its maximum simulated power is 60kW, maximum current is 150A, maximum voltage is 1000V, and response time is less than 20ms.

[0083] The electric vehicle communication simulation interface is used to simulate the communication protocol interaction between electric vehicles and charging piles, including sending charging requests and receiving charging pile status information. Mechanical safety design includes: a physical anti-misinsertion mechanism to ensure interface connection direction and type matching; internal circuit fuses or circuit breakers for short-circuit protection; and an overcurrent protection mechanism based on current sensors and controller logic, which automatically cuts off power output when the detected current exceeds a set threshold.

[0084] The embedded interoperability test module is integrated on the main control circuit board inside the intelligent load connector housing 101. Its hardware foundation includes a high-performance microcontroller, a communication interface chip, and a digital signal processor (DSP). The embedded interoperability test module is used to perform charging protocol parsing, verification, dynamic load simulation control, and fault injection. This embedded interoperability test module includes a multi-protocol parsing unit, a dynamic load simulation module, and a fault injection function module. The multi-protocol parsing unit contains a pre-installed software protocol stack, supporting real-time capture, decoding, error checking, and data extraction of messages from mainstream charging protocols such as GB / T27930, ISO15118, and DIN70121, with a message parsing speed of no less than 1000 frames / second.

[0085] The dynamic load simulation module precisely controls the programmable load module in the bidirectional connection structure to simulate the dynamic load requirements of electric vehicles at different charging stages according to preset test curves or external commands, such as the smooth switching from the low current pre-charging stage to the high current constant current charging stage, and then to the constant voltage charging stage.

[0086] The fault injection module simulates various charging pile faults, such as abnormal CP / PP signals, communication interruptions, or voltage fluctuations, by precisely controlling the CP / PP signal level, frequency, and duty cycle of the communication simulation interface, or by introducing data packet loss, delay, or tampering into the PLC communication link, and by controlling the output voltage ripple of the programmable load module. The module's fault injection delay is less than 10ms, and its fault injection accuracy is ±1%.

[0087] The intelligent diagnostic unit is also integrated inside the intelligent load connector housing 101. It works closely with the embedded interoperability test module to monitor key parameters between the charging pile and the electric vehicle in real time and to exchange data. This intelligent diagnostic unit includes a real-time monitoring module, a data upload module, and a report generation module. The real-time monitoring module acquires data through sensors connected to the bidirectional communication and power lines, such as voltage sensors, current sensors, PWM signal acquisition devices, and PLC communication interfaces. This data is used to monitor the PWM signal characteristics of the charging pile, the integrity and timing of PLC communication frames, the switching signals of the contactor status, and key parameters such as voltage, current, and power during the charging process. The data upload module includes Wi-Fi, 4G, or Bluetooth communication modules to upload the raw data collected by the real-time monitoring module, the protocol parsing data generated by the embedded interoperability test module, the dynamic load simulation results, and the fault injection report to the cloud-based operation and maintenance platform via a wireless communication network. The report generation module automatically organizes the test data and diagnostic results into structured test report files (e.g., PDF or XML format) according to preset templates and international standards such as OCPP1.6 / 2.0.

[0088] The intelligent load connector provided in this application, by integrating an embedded interoperability test module and an intelligent diagnostic unit, achieves high-precision simulation and real-time monitoring of charging pile protocols, load characteristics, and fault scenarios. Its compact and portable housing design, active cooling system, and multiple safety protection mechanisms ensure the reliability and operational safety of the device in complex testing environments. Simultaneously, the connector supports multi-protocol parsing and high-speed data upload, providing a comprehensive and accurate test data foundation for cloud-based intelligent operation and maintenance platforms, thereby effectively improving the automation level, test coverage, and diagnostic efficiency of charging pile interoperability testing.

[0089] Figure 3 This is a flowchart illustrating an embodiment of the interoperability testing method for charging piles provided in this application. Figure 3 As shown, this method is applied to Figure 1 The interoperability testing system for the charging pile shown includes the following method:

[0090] S301. Collect real-time monitoring and test data for interoperability testing between charging piles and electric vehicles.

[0091] Real-time monitoring data refers to the raw signals and parameters that reflect the real-time working status of the charging pile, which are directly collected by sensors (such as voltage sensors, current sensors, and PWM signal acquisition devices) in the intelligent diagnostic unit of the intelligent load connector during the interaction between the charging pile and the intelligent load connector (simulating an electric vehicle). Examples include real-time voltage values, current values, PWM signal waveforms, and contactor switch status.

[0092] Test data refers to structured data generated by actively executing test scripts or simulating specific scenarios through the intelligent load connector, including dynamic load simulation curves, protocol interaction message sequences, fault injection records, and the results of protocol parsing by the embedded interoperability test module (such as error codes and timeout records).

[0093] In this step, the intelligent load connector serves as the core device for test execution and data acquisition, simultaneously performing passive monitoring and active testing. Its intelligent diagnostic unit's real-time monitoring module continuously collects electrical and communication signals output by the charging pile, forming a real-time monitoring data stream. Simultaneously, the embedded interoperability test module, based on preset or cloud-based test tasks, controls the programmable load module to simulate the charging behavior of an electric vehicle and controls the communication simulation interface to execute standard or fault-tolerant protocol interactions, thereby generating test data containing scenario information. This integrated monitoring and testing synchronous execution mode solves the problems of limited testing equipment functionality (e.g., load banks can only simulate loads, protocol analyzers can only capture packets for analysis), fragmented data sources, and difficulty in aligning timestamps in existing technologies. Correlating real-time status with active testing behavior provides a highly correlated, time-aligned, multi-dimensional data foundation for subsequent accurate causal analysis, greatly improving the accuracy of fault reproduction and diagnosis.

[0094] Additionally, prior to this step, the method includes:

[0095] Based on the intelligent load connector, operating environment data of the charging pile is collected. This operating environment data refers to external condition parameters that may affect the interoperability performance of the charging pile, such as ambient temperature, humidity, and grid voltage fluctuations obtained through built-in or external sensors. For example, before or during testing, the intelligent load connector acquires environmental information through its sensors or from the charging-to-communication interface, incorporating environmental factors into the test data. This solves the problem that traditional testing is only conducted in ideal experimental environments and cannot reflect complex on-site operating conditions. It also helps to build a more comprehensive knowledge graph and identify environment-sensitive faults.

[0096] Then, the load demands of an electric vehicle at different charging stages are simulated to generate dynamic load test data. This dynamic load test data refers to the load demand values ​​(target current, voltage, and power) and actual response values ​​recorded by the programmable load module in real time, based on a preset battery charging characteristic curve (such as a constant current-constant voltage curve) or random change commands, as determined by the module's adjustment of its impedance characteristics. For example, the dynamic load simulation module controls the programmable load module in the bidirectional connection structure to simulate the complete process of an electric vehicle from handshake, pre-charging, high-power charging to completion, or any sudden changes within it. This step solves the problems of high cost, poor repeatability, and inability to cover extreme load conditions when using real electric vehicles for testing. Through programmable dynamic load simulation, the response capability and stability of charging piles under different load conditions can be systematically verified, especially their adaptability to load step changes.

[0097] Simultaneously, the charging process of the charging pile is simulated to generate key parameters of the charging pile, including voltage, current, and PWM signals. These key parameters are the core physical quantities and control signals that the charging pile actually outputs when responding to a simulated electric vehicle's request, characterizing its operating state.

[0098] For example, the smart load connector sends a compliant charging request to the charging pile via a communication simulation interface, triggering the charging pile to start the charging process. During this process, the real-time monitoring module collects the actual output voltage, current, and PWM signal of the control guidance circuit of the charging pile. This step, by simulating the standard charging process, obtains the actual output parameters of the charging pile in the "injected fault" state, providing a benchmark and comparison sample for judging whether its behavior is normal.

[0099] Finally, the operating environment data, dynamic load test data, and key parameters of the charging piles are fused together to generate test data. This fusion process involves aligning, correlating, tagging, and encapsulating data from different modules and describing different dimensions according to a unified time base, forming a structured dataset with complete contextual information. For example, the report generation module or data upload module of the intelligent diagnostic unit synchronizes and correlates environmental data, load simulation commands / results, collected key parameters of the charging piles, and protocol interaction logs in time, packaging them into a complete data package for a single test task. This data fusion step creates a complete test "digital twin" record, ensuring that any anomalies can be traced back to specific test actions, environmental conditions, and charging pile responses during subsequent analysis, laying a solid data foundation for accurate causal reasoning.

[0100] S302. Based on real-time monitoring data, determine the physical nodes of the charging pile.

[0101] In this context, an entity node refers to a node in the interoperability fault causal knowledge graph that represents a specific food or concept. In this application, it may refer to key elements abstracted from the field of charging interoperability, including protocol version, fault type, test conditions, and environmental factors.

[0102] In this step, after the data receiving module of the cloud-based intelligent operation and maintenance platform receives the data, its entity recognition unit uses Natural Language Processing (NLP) technology and rule matching algorithms to parse the real-time monitoring data and its accompanying metadata (such as charging pile model and test task ID). For example, it parses "GB / T 27930-2015" from the protocol message and identifies it as the protocol version entity; it matches "overcurrent protection" from the abnormal current record and identifies it as the fault type entity; it extracts "ambient temperature: 45°C" from the data packet and identifies it as the environmental factor entity; and it reads "simulated load step: 30kW->60kW" from the test script and identifies it as the test condition entity.

[0103] This step transforms and refines unstructured, massive amounts of test data into structured knowledge units that can be understood and processed by the knowledge graph. This solves the problem that fault descriptions in traditional operations and maintenance rely on manual processes, are subjective, and are not standardized. It realizes the automated and standardized conversion of test data into domain knowledge, preparing for the construction of a unified knowledge graph.

[0104] S303. Based on the physical nodes of the charging pile, construct a causal knowledge graph of interoperability faults of the charging pile.

[0105] Among them, the interoperability fault causal knowledge graph is a knowledge representation form stored in a graph structure, where nodes are various entities determined in S302, and edges represent the relationships between entities, especially causal relationships, which are used to describe the logic that "the state or event of a certain entity may lead to the state or event of another entity".

[0106] Specifically, this step includes: identifying and constructing a set of relationships between the entity nodes of the charging pile, including causal relationships, attribute relationships, association relationships, and temporal relationships. For example, the relationship extraction unit establishes connections between the identified entities based on pattern matching and dependency parsing, or by utilizing predefined domain rules.

[0107] Then, based on the entity nodes and relationship set of the charging piles, a causal knowledge graph of interoperability faults for the charging piles is constructed. Specifically, the knowledge graph storage unit stores the aforementioned entities and relationships into a graph database (such as Neo4j), forming an initial or dynamically expanded knowledge graph. In the graph, weights can be assigned to the edges of causal relationships to represent the strength or confidence of the causal relationship.

[0108] This step organically organizes discrete, point-like fault phenomena and parameters through causal chains, forming a computationally achievable, reasonable networked knowledge model that incorporates the experience of domain experts. This solves the problems of traditional fault diagnosis relying on individual expert experience, the difficulty in knowledge accumulation and sharing, and the inability to handle complex cascading faults.

[0109] S304. Based on the test data, determine the abnormal events of the charging pile.

[0110] Among them, abnormal events refer to specific events in the test data that are determined to deviate from the expected or standard by comparing with protocol standards, electrical specifications or historical normal baselines, including abnormal communication signal events and abnormal electrical parameter events.

[0111] Specifically, the causal reasoning diagnostic module performs in-depth analysis of the received test data. For example, by parsing the protocol communication logs and finding "continuous failures in CRC check of PLC communication messages," it identifies this as a communication signal anomaly event; by analyzing the voltage and current curves and finding "excessive ripple in the constant voltage stage of the output voltage," it identifies this as an electrical parameter anomaly event.

[0112] This step enables the automatic and accurate extraction of fault characteristic signals from massive amounts of data, replacing the inefficient method of manually checking waveforms and logs one by one. It solves the problem that key anomalies are easily buried or missed in complex tests, providing clear input for subsequent reasoning.

[0113] S305. Based on the causal knowledge graph of abnormal events and interoperability failures, perform reverse causal chain reasoning to determine the cause of the charging pile's interoperability failure and complete the interoperability test of the charging pile.

[0114] Among them, reverse causal chain reasoning is a reasoning method that starts from the observed result (abnormal event / symptom) and traces back along the reverse edge of the causal relationship in the knowledge graph to find all potential causes (root causes) that may have led to the result.

[0115] Interoperability failure causes refer to the fundamental defects that lead to a series of observed abnormal events, which may be located at the hardware level (such as CP circuit failure), software level (such as protocol stack logic error), configuration level (such as improper parameter settings), or environmental level (such as long-term high temperature aging).

[0116] Specifically, the causal reasoning diagnostic module maps the abnormal events identified in S304 to symptom nodes in a knowledge graph. Then, starting from these symptom nodes, it traverses backwards through all causal edges pointing to them, layer by layer, forming one or more causal paths that may lead to different root causes. The module comprehensively calculates the weight and prior probability of the causal relationships on each path, and uses Bayesian inference formulas to quantify the posterior probability of each candidate root cause leading to the current symptom set. Finally, it selects the cause with the highest probability as the most likely root cause of the failure and generates a diagnostic report.

[0117] This approach addresses the limitations of traditional diagnostic methods, which often rely on symptom description, trial-and-error troubleshooting, and difficulty in pinpointing deep-seated and complex causes. By employing probabilistic reasoning based on knowledge graphs, it systematically and logically derives the most probable root causes, significantly improving diagnostic accuracy and efficiency, and achieving a leap from "symptom repair" to "root cause repair."

[0118] Following this step, the method also includes a closed-loop process for optimizing the test, including:

[0119] Based on the causes of interoperability failures in charging piles, the operation and maintenance data of the charging piles are determined. For example, the diagnosed causes of failures, the repair measures taken (such as replacing modules or upgrading firmware), and the results of post-repair retests are fed back as new operation and maintenance data to the charging pile's interoperability testing system.

[0120] Then, based on the operation and maintenance data of the charging piles, the causal relationships in the interoperability fault causal knowledge graph are updated to obtain an updated set of relationships. For example, the knowledge graph self-learning and update module adjusts the weights of relevant causal relationships or adds new entities and relationships based on the actual results of operation and maintenance feedback.

[0121] Based on the updated relationship set, an optimized test plan for the charging pile is generated, completing the test optimization for the charging pile. In this step, the predictive maintenance and optimization suggestion module analyzes the coverage blind spots of the current test plan based on the updated and more accurate knowledge graph, and intelligently generates optimization suggestions. For example, for a newly discovered firmware defect in a certain model of charging pile, it is suggested to add fault injection test cases with specific message sequences in subsequent tests.

[0122] This step forms a closed-loop self-learning process of "testing-diagnosis-optimization," solving the problems of static and rigid test plans that cannot learn from historical issues and improve upon them. The system can leverage the experience of each test and maintenance operation to continuously evolve its knowledge base and testing strategies, making testing more targeted and operations more forward-looking.

[0123] On the other hand, following this step, there is also a mechanism for continuously updating the interoperability fault causal knowledge graph:

[0124] The system acquires updated test data, operation and maintenance feedback, and updated charging protocol standards for interoperability testing between charging piles and electric vehicles; then, based on the updated test data, operation and maintenance data, and updated charging protocol standards, it updates the causal knowledge graph of interoperability faults.

[0125] This mechanism ensures the dynamism and timeliness of the interoperability fault causal knowledge graph. It can evolve based on new test data, human experience, and industry standards, keeping the graph in sync with current technological developments and operational practices. This solves the problem that static knowledge bases are prone to becoming outdated and unable to adapt to new technologies and fault modes.

[0126] This embodiment achieves the fusion and acquisition of multi-dimensional test data by synchronously executing dynamic load simulation, protocol interaction, fault injection, and real-time monitoring through an integrated intelligent load connector. Then, it utilizes a cloud platform to construct and apply an interoperable fault causal knowledge graph, transforming test data into structured knowledge and automatically locating the root cause of the fault through reverse causal chain reasoning. This method solves the problems of dispersed equipment, fragmented data, reliance on manual experience for diagnosis, and difficulty in locating deep-seated root causes in traditional testing. It achieves full automation and intelligence from data acquisition and knowledge construction to intelligent diagnosis, significantly improving testing efficiency, diagnostic accuracy, and the professionalism of operation and maintenance.

[0127] Figure 4 This is a flowchart illustrating Embodiment Two of the interoperability testing method for charging piles provided in this application. Figure 4 As shown, in Figure 3 Based on the implementation examples, and using a causal knowledge graph of abnormal events and interoperability failures, reverse causal chain reasoning is performed to determine the causes of interoperability failures in charging stations, including:

[0128] S401. Map abnormal events to target entity nodes in an interoperable fault causal knowledge graph.

[0129] Mapping refers to finding the entity node in the knowledge graph that has the same meaning as or corresponds to the current abnormal event.

[0130] The target entity node represents the symptom node of the currently observed abnormal event.

[0131] In this step, the causal reasoning diagnosis module matches and associates the "PLC communication message CRC check failure" event identified by S304 with the existing fault entity nodes in the knowledge graph, and determines it as one of the starting points of this reasoning.

[0132] This step serves as a bridge connecting the data layer and the knowledge layer, transforming specific test exception instances into abstract symbols in the knowledge graph that can participate in reasoning, thus providing a clear entry point for subsequent graph traversal operations.

[0133] S402. Based on the target entity node, trace back the causal relationships in the interoperability fault causal knowledge graph to determine the probability of occurrence of each causal relationship.

[0134] In graph theory, backtracking refers to the process of starting from the current node and searching backwards along the incoming edges (i.e., edges pointing to the current node) to find the predecessor node.

[0135] The probability of a causal relationship is the probability that the upstream cause connected to the causal relationship edge is true given the observed downstream result (symptom). It is a component of Bayesian inference computation.

[0136] For example, starting from the faulty node, the system reverses its path to find all possible causal nodes that could cause it, such as "charging pile PLC communication module hardware failure," "strong electromagnetic interference," and "protocol stack software bug." The system then follows each reverse causal path, combining the historical weights and prior probabilities of each edge along the path, and uses a Bayesian network inference algorithm to calculate the posterior probability of each potential causal node causing the current symptom. The Bayesian formula is as follows:

[0137]

[0138] in, This indicates the j-th possible root cause (e.g., "charging pile CP signal hardware failure" or "BMS communication protocol version mismatch").

[0139] S represents the observed symptom set, which contains one or more symptom nodes formed by mapping anomalous events.

[0140] This indicates that, given the observed symptom set S, the root cause... The posterior probability of occurrence, which represents the likelihood that the root cause will lead to the occurrence of the current symptom set.

[0141] Indicates the root cause In the event of an event, the conditional probability of the symptom set S is observed. This probability is determined by traversing the knowledge graph from the root cause. All causal paths to symptom set S are obtained by cumulatively calculating the strength of each causal relationship on the path;

[0142] Indicates the root cause The prior probability is initialized based on historical fault statistics, charging pile type, or manufacturer reliability data, and is continuously adjusted in the knowledge graph self-learning and update module.

[0143] P(S) represents the total probability of the occurrence of symptom set S, calculated by considering all possible root causes. Marginalization summation is performed to obtain .

[0144] This step not only identifies possible causes but also ranks the probabilities of these causes through probability calculations. This solves the problem of determining primary and secondary causes in complex scenarios with multiple causes and a single effect, making the diagnostic conclusions more scientific and reliable.

[0145] S403. Determine the cause of the interoperability failure of the charging pile based on the probability of occurrence of each causal relationship.

[0146] In this step, the posterior probability values ​​of all candidate root cause nodes obtained through backtracking are compared. For example, if the probability of "charging pile PLC communication module hardware failure" is calculated to be 85%, "strong electromagnetic interference" to be 10%, and "protocol stack software bug" to be 5%, then the system will determine "charging pile PLC communication module hardware failure," which has the highest posterior probability (85%), as the most likely cause of the interoperability failure and output this diagnostic result.

[0147] Finally, the causal reasoning diagnostic module selects the one with the highest value. of Assess the most likely root cause of the current failure and generate a diagnostic report.

[0148] This step provides clear and actionable diagnostic suggestions based on quantitative analysis, directly guiding maintenance personnel to carry out targeted repairs (such as replacing communication modules), which greatly shortens the fault location time, improves maintenance efficiency, and reduces the risk of misdiagnosis.

[0149] This embodiment, building upon Embodiment 1, further refines the knowledge graph-based intelligent diagnostic process. By precisely mapping abnormal events to target nodes in the knowledge graph and performing probabilistic backtracking and reasoning along causal edges, the most probable root cause of the failure is ultimately quantitatively assessed and determined. This process achieves a leap from qualitative analysis to quantitative judgment, solving the problem that traditional methods struggle to determine primary and secondary causes and their probabilities in multi-cause-single-effect or complex cascading failure scenarios. It enables diagnostic conclusions to not only indicate possibilities but also provide probabilistic confidence levels, thereby making operational decisions more scientific, accurate, and actionable, significantly reducing the risk of misjudgment and improving the first-time success rate of repairs.

[0150] Figure 5 This is a schematic diagram of the interoperability testing device for the charging pile provided in this application. Figure 5 As shown, the interoperability testing device 50 for charging piles provided in this embodiment includes:

[0151] The data acquisition module 501 is used to collect real-time monitoring data and test data for interoperability testing between charging piles and electric vehicles;

[0152] The entity node determination module 502 is used to determine the entity nodes of the charging pile based on real-time monitoring data. The entity nodes include protocol version, fault type, test conditions and environmental factors.

[0153] Interoperability fault causal knowledge graph construction module 503 is used to construct an interoperability fault causal knowledge graph of the charging pile based on the physical nodes of the charging pile.

[0154] The abnormal event determination module 504 is used to determine abnormal events of the charging pile based on test data. Abnormal events include communication signal abnormal events and electrical parameter abnormal events.

[0155] The causal diagnosis module 505 is used to perform reverse causal chain reasoning based on the causal knowledge graph of abnormal events and interoperability failures to determine the cause of interoperability failures of charging piles and complete the interoperability test of charging piles.

[0156] In one possible implementation, the interoperability fault causal knowledge graph construction module 503 is also specifically used for:

[0157] Based on the physical nodes of the charging pile, identify and construct a set of relationships between the physical nodes. The set of relationships includes causal relationships, attribute relationships, association relationships, and temporal relationships.

[0158] Based on the entity nodes and relationship set of charging piles, a causal knowledge graph of interoperability faults of charging piles is constructed.

[0159] In one possible implementation, the causal diagnosis module 505 is further specifically used for:

[0160] Map abnormal events to target entity nodes in an interoperable fault causal knowledge graph;

[0161] Based on the target entity node, backtrack the causal relationships in the interoperability fault causal knowledge graph to determine the probability of occurrence of each causal relationship;

[0162] Based on the probability of occurrence of each causal relationship, the cause of the interoperability failure of the charging pile is determined.

[0163] In one possible implementation, the data acquisition module 501 is further specifically used for:

[0164] Based on the load connector, collect the operating environment data of the charging pile;

[0165] Simulate the load demand of electric vehicles at different charging stages to generate dynamic load test data;

[0166] The charging process of a charging pile is simulated to generate key parameters of the charging pile, including voltage signal, current signal and PWM signal.

[0167] The system integrates operating environment data, dynamic load test data, and key parameters of charging piles to generate test data.

[0168] In one possible implementation, the causal diagnosis module 505 is further specifically used for:

[0169] Based on the cause of the interoperability failure of the charging pile, determine the operation and maintenance data of the charging pile;

[0170] Based on the operation and maintenance data of the charging piles, the causal relationships in the interoperability fault causal knowledge graph are updated to obtain the updated relationship set;

[0171] Based on the updated relationship set, an optimized test plan for the charging pile is generated, and the test optimization of the charging pile is completed.

[0172] In one possible implementation, the causal diagnosis module 505 is further specifically used for:

[0173] Obtain updated test data, operation and maintenance feedback, and updated charging protocol standards for interoperability testing between charging piles and electric vehicles;

[0174] Based on updated test data, operation and maintenance data, and updated charging protocol standards, the causal knowledge graph of interoperability faults was updated.

[0175] The interoperability testing device for charging piles provided in this embodiment can execute the methods provided in the above-described method embodiments. Its implementation principle and technical effects are similar, and will not be described in detail here.

[0176] Figure 6 A schematic diagram of the interoperability testing equipment for the charging pile provided in this application. Figure 6 As shown, the interoperability testing device 60 for charging piles provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device 60 further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0177] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0178] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0179] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0180] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0181] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0182] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0183] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0184] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0185] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0186] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0187] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0188] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0189] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0190] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0191] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for testing the interoperability of charging piles, characterized in that, An interoperability testing system for charging piles, the interoperability testing system including a cloud-based intelligent operation and maintenance platform and an intelligent load connector, the method comprising: Collect real-time monitoring and test data on the interoperability testing of charging piles and electric vehicles; Based on the real-time monitoring data, the physical nodes of the charging pile are determined, and the physical nodes include protocol version, fault type, test conditions and environmental factors; Based on the physical nodes of the charging pile, construct a causal knowledge graph of interoperability faults of the charging pile; Based on the test data, abnormal events of the charging pile are determined, including abnormal communication signals and abnormal electrical parameters. Based on the abnormal events and the interoperability failure causal knowledge graph, reverse causal chain reasoning is performed to determine the cause of the interoperability failure of the charging pile and complete the interoperability test of the charging pile.

2. The interoperability testing method according to claim 1, characterized in that, Based on the physical nodes of the charging pile, an interoperability fault causal knowledge graph of the charging pile is constructed, including: Based on the physical nodes of the charging pile, identify and construct a set of relationships between the physical nodes, the set of relationships including causal relationships, attribute relationships, association relationships and temporal relationships; Based on the entity nodes of the charging pile and the set of relationships, an interoperability fault causal knowledge graph of the charging pile is constructed.

3. The interoperability testing method according to claim 1, characterized in that, Based on the abnormal events and the interoperability failure causal knowledge graph, reverse causal chain reasoning is performed to determine the cause of the charging pile's interoperability failure, including: The abnormal events are mapped to target entity nodes in the interoperability fault causal knowledge graph; Based on the target entity node, the causal relationships in the interoperability fault causal knowledge graph are traced back to determine the probability of occurrence of each causal relationship; The cause of the interoperability failure of the charging pile is determined based on the probability of occurrence of each of the aforementioned causal relationships.

4. The interoperability testing method according to any one of claims 1 to 3, characterized in that, Before collecting real-time monitoring data and test data for interoperability testing between charging piles and electric vehicles, the method further includes: Based on the load connector, the operating environment data of the charging pile is collected; Simulate the load demand of the electric vehicle at different charging stages to generate dynamic load test data; The charging process of the charging pile is simulated to generate key parameters of the charging pile, including voltage signal, current signal and PWM signal; The operating environment data, dynamic load test data, and key parameters of the charging pile are fused together to generate test data.

5. The interoperability testing method according to any one of claims 1 to 3, characterized in that, Based on the abnormal event and the interoperability failure causal knowledge graph, reverse causal chain reasoning is performed to determine the cause of the charging pile's interoperability failure. After completing the interoperability test of the charging pile, the method further includes: Based on the cause of the interoperability failure of the charging pile, determine the operation and maintenance data of the charging pile; Based on the operation and maintenance data of the charging pile, the causal relationships in the interoperability fault causal knowledge graph are updated to obtain an updated relationship set; Based on the set of update relationships, an optimized test plan for the charging pile is generated, and the test optimization of the charging pile is completed.

6. The interoperability testing method according to any one of claims 1 to 3, characterized in that, Based on the abnormal event and the interoperability failure causal knowledge graph, reverse causal chain reasoning is performed to determine the cause of the charging pile's interoperability failure. After completing the interoperability test of the charging pile, the method further includes: Obtain updated test data, operation and maintenance feedback, and updated charging protocol standards for interoperability testing between the charging pile and the electric vehicle; Based on the updated test data, the operation and maintenance feedback, and the updated charging protocol standard, the interoperability fault causal knowledge graph is updated.

7. An interoperability testing device for charging piles, characterized in that, include: The data acquisition module is used to collect real-time monitoring data and test data for interoperability testing between charging piles and electric vehicles; The entity node determination module is used to determine the entity node of the charging pile based on the real-time monitoring data. The entity node includes protocol version, fault type, test conditions and environmental factors. An interoperability fault causal knowledge graph construction module is used to construct an interoperability fault causal knowledge graph of the charging pile based on the physical nodes of the charging pile. An abnormal event determination module is used to determine abnormal events of the charging pile based on the test data. The abnormal events include communication signal abnormal events and electrical parameter abnormal events. The causal diagnosis module is used to perform reverse causal chain reasoning based on the abnormal event and the interoperability failure causal knowledge graph to determine the cause of the interoperability failure of the charging pile and complete the interoperability test of the charging pile.

8. A smart load connector, characterized in that, include: A bidirectional connection structure is used to connect the charging pile under test and the test load; An embedded interoperability test module is used to perform charging protocol parsing and verification. A smart load connector housing for accommodating the bidirectional connection structure and the embedded interoperability test module; The diagnostic unit is used to monitor key parameters in real time and perform data interaction.

9. The intelligent load connector according to claim 8, characterized in that, The top of the housing of the intelligent load connector is provided with a handle; The smart load connector housing is equipped with a touch display screen on the top front side; The intelligent load connector housing has heat dissipation holes on the front bottom side.

10. An interoperability testing system for charging piles, characterized in that, This includes a cloud-based intelligent operations and maintenance platform, intelligent load connectors, and computer programs; The intelligent load connector is as described in any one of claims 8-9; The computer program is used to perform the method as described in any one of claims 1-7.

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