A remote detection method for interaction performance of a charging and discharging facility

Abstract

The present application relates to the technical field of electric vehicle and power grid interaction, and specifically provides a kind of charging and discharging facility interaction performance remote detection method, comprising: through the edge controller deployed in charging and discharging facility side, at least including car end, stake end, power grid side and platform end multiple source operation data are collected in real time through fieldbus, and mapping to unified time axis;Through cloud to issue standard test sequence to charging and discharging facility, the actual response data of multiple links are recorded synchronously, and mapping to unified time axis;Based on multiple source operation data and actual response data, in combination with the preset interaction performance evaluation index system, the measured value of each evaluation index is extracted;The measured value and the corresponding comprehensive weight are fused, the interaction performance comprehensive score of charging and discharging facility is calculated, and the comprehensive evaluation result is obtained.The present application at least solves the problem that charging and discharging facility detection method has performance blind area, and cannot be traced to specific link in whole chain.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle and power grid interaction technology, and in particular to a method for remotely detecting the interaction performance of charging and discharging facilities. Background Technology

[0002] As charging and discharging facilities develop towards massive scale and distribution, traditional manual on-site testing methods can no longer meet the needs of large-scale performance surveys, laboratory evaluations cannot reproduce complex on-site conditions, and existing operating platforms lack the ability to remotely quantify dynamic indicators such as adjustment response time and communication latency. As a result, the power grid cannot grasp the true performance of adjustable resources, and there is an urgent need for a remote intelligent testing method that covers the entire chain.

[0003] Currently, the common method for testing charging and discharging facilities involves manually carrying equipment such as power quality analyzers and oscilloscopes to the site for "point-to-point" physical connection tests. Electrical parameters at specific times are then analyzed manually to determine facility performance. This method has the following drawbacks: The testing scope is severely limited, allowing only random sampling of a large number of geographically dispersed facilities, failing to achieve full coverage and routine monitoring; it lacks end-to-end attribution capabilities, focusing only on isolated pile-end tests and failing to incorporate the vehicle-side BMS (Battery Management System) status, grid-side power quality, and platform-side communication performance into a unified evaluation framework, making it impossible to trace interaction performance bottlenecks back to specific stages.

[0004] There is currently no effective solution to the problem that the testing methods for charging and discharging facilities in related technologies have performance blind spots and cannot be traced back to specific links in the entire chain. Summary of the Invention

[0005] The present invention provides a remote detection method for the interactive performance of charging and discharging facilities, which at least solves the problem that the detection methods for charging and discharging facilities in related technologies have performance blind spots and cannot be traced back to specific links in the entire chain.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides a remote detection method for the interactive performance of charging and discharging facilities, comprising: real-time acquisition of multi-source operational data, including at least vehicle-side, pile-side, grid-side, and platform-side data, via an edge controller deployed on the charging and discharging facility side and a fieldbus, and mapping each of the operational data to a unified time axis; issuing a standard test sequence to the charging and discharging facility via the cloud, synchronously recording actual response data of multiple stages, and mapping each of the actual response data to the unified time axis; wherein the stages include: a cloud command issuance stage, an edge controller receiving stage, a pile-side power execution stage, and a vehicle BMS response stage; based on the multi-source data... The source operation data and the actual response data are combined with a preset interactive performance evaluation index system to extract the measured values ​​of each evaluation index in the interactive performance evaluation index system; the measured values ​​are then integrated with the comprehensive weights of the corresponding evaluation indexes to calculate the comprehensive interactive performance score of the charging and discharging facility, thus obtaining a comprehensive evaluation result; wherein, the comprehensive weights are obtained by combining subjective weights based on expert experience and objective weights based on the dispersion of measured values ​​with their respective combination coefficients; the combination coefficients are obtained by constructing a game theory-based objective function and then using a genetic algorithm for iterative optimization after introducing constraints.

[0007] Preferably, the process involves using an edge controller deployed on the charging / discharging facility side to collect multi-source operational data in real time via a fieldbus, including data from at least the vehicle end, the charging pile end, the grid end, and the platform end, and mapping all of the operational data to a unified time axis. This includes: collecting multi-source operational data in real time via a fieldbus using an edge controller deployed on the charging / discharging facility side; wherein the multi-source operational data includes: battery state of charge and BMS state data from the vehicle end, output voltage and output current data from the charging pile end, power quality parameters from the grid end, and control commands and communication quality data from the platform end; preprocessing the multi-source operational data; wherein the preprocessing includes abnormal data cleaning; and mapping the multi-source operational data from the vehicle end, the charging pile end, the grid end, and the platform end to the unified time axis after aligning them by timestamp.

[0008] Preferably, a standard test sequence is sent to the charging and discharging facility via the cloud, and the actual response data of multiple stages are recorded simultaneously. The actual response data is then mapped to the unified timeline. This includes: sending a standard test sequence to the charging and discharging facility via the cloud; wherein the standard test sequence includes at least one of a step wave, a ramp wave, or a sine wave; during the sending of the standard test sequence, data points are marked at the cloud command sending stage, the edge controller receiving stage, the pile-end power execution stage, and the vehicle BMS response stage, recording the actual response data and corresponding timestamps for each stage; the recorded actual response data is mapped to the unified timeline, aligning the actual response data with the multi-source operating data in time sequence.

[0009] Preferably, before evaluating the interactive performance of the charging and discharging facility, the method includes: determining the evaluation indicators corresponding to the vehicle end, the pile end, the power grid side, and the platform end based on the multi-source operating data; classifying each evaluation indicator into a corresponding capability dimension according to its capability type to form the interactive performance evaluation indicator system; wherein, the capability dimension includes: observable, measurable, adjustable, and controllable.

[0010] Preferably, each of the evaluation indicators is divided into corresponding capability dimensions according to its capability type to form the interactive performance evaluation indicator system, including: dividing indicators reflecting the visibility of facility status into the observable dimension; wherein the observable dimension includes: charging facility installation status, charging facility operation status, charging power, and voltage distribution; dividing indicators reflecting the quantifiability of operating parameters into the measurable dimension; wherein the measurable dimension includes: load power consumption prediction, charging SOC status, charging pile power, and charging pile voltage and current; dividing indicators reflecting power regulation potential into the adjustable dimension; wherein the adjustable dimension includes: grid power real-time power, V2G discharge power, adjustable load, and adjustable total capacity; dividing indicators reflecting the execution capability of control commands into the controllable dimension; wherein the controllable dimension includes: load control plan, load control scheme, adjustable load, and adjustable total capacity.

[0011] Preferably, based on the multi-source operational data and the actual response data, and in conjunction with a preset interactive performance evaluation index system, the measured values ​​of each evaluation index in the interactive performance evaluation index system are extracted, including: extracting the measured values ​​corresponding to each evaluation index in the interactive performance evaluation index system according to the multi-source operational data and the actual response data; standardizing each measured value according to positive and negative indicators respectively; wherein, the positive and negative indicators are determined according to the consistency between the growth trend of the measured values ​​and the performance improvement trend.

[0012] Preferably, the comprehensive evaluation result is obtained by integrating the measured values ​​with the corresponding evaluation indexes and calculating the comprehensive score of the interactive performance of the charging and discharging facility. This includes: calculating the subjective and objective weights corresponding to each evaluation index based on fuzzy hierarchical analysis and information entropy theory; assuming the comprehensive weight is obtained by combining the subjective and objective weights with their respective combination coefficients; using a game theory-based objective function, with the goal of minimizing the deviation between the comprehensive weight and the subjective and objective weights, and combining constraints, using a genetic algorithm to iteratively optimize the objective function to obtain optimized combination coefficients; determining the final comprehensive weight based on the optimized combination coefficients, and then weighting and integrating the standardized measured values ​​of each evaluation index with the corresponding final comprehensive weight to calculate the comprehensive score of the interactive performance of the charging and discharging facility; and outputting the comprehensive evaluation result of the charging and discharging facility based on the comprehensive interactive performance score. The comprehensive evaluation result includes sub-item scores corresponding to each capability dimension, and a comprehensive health score obtained by weighted summation of the sub-item scores.

[0013] Preferably, the formula for calculating the final comprehensive weight includes: ; ; ; , ; ; ;in, This is the overall weight vector; This is the subjective weight vector; For objective weight vectors; , These are the two combination coefficients corresponding to the subjective weight vector and the objective weight vector, respectively; i This indicates the sequence number of the evaluation index in the interactive performance evaluation index system. n The total number of evaluation indicators; The first of the comprehensive weight vectors i The component is the nth component. i The comprehensive weight corresponding to each evaluation indicator; The first subjective weight vector i The component is the nth component. i The subjective weights corresponding to each evaluation indicator; For the first i The safety offset threshold corresponding to each evaluation indicator; , These are the two optimal combination coefficients output after iterative optimization using a genetic algorithm; This is the final comprehensive weight vector obtained after optimization by the genetic algorithm; This is the objective function constructed based on game theory.

[0014] A second aspect of the present invention provides an electronic device, comprising: a processor and a memory storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the aforementioned remote detection method for the interactive performance of a charging and discharging facility.

[0015] A third aspect of the present invention provides a non-transitory machine-readable medium storing computer instructions, characterized in that the computer instructions are used to cause the computer to execute the aforementioned remote detection method for the interactive performance of a charging and discharging facility.

[0016] A fourth aspect of the present invention provides a computer program product, including a computer program / instructions, characterized in that, when the computer program / instructions are executed by a processor, they implement the aforementioned remote detection method for the interactive performance of charging and discharging facilities.

[0017] Compared with the prior art, the above-described technical solution of the present invention has the following advantages: This invention provides a remote detection method for the interactive performance of charging and discharging facilities. It maps operational data from the vehicle, charging pile, power grid, and platform to the same timeline, and uses a standard test sequence from the cloud as an active stimulus, aligning the response data of the four stages—command issuance, edge reception, charging pile execution, and vehicle response—on the same timeline. This unified timeline provides a benchmark for cross-device correlation analysis, connecting isolated device data into a continuous interactive chain. This solves the performance blind spot problem caused by inconsistent references in related technologies, enabling comprehensive monitoring of the charging and discharging interaction process and timely detection of anomalies in any stage. By establishing an interactive performance evaluation index system covering the vehicle, charging pile, power grid, and platform, and extracting the measured values ​​of each index based on this system, the method combines subjective weights based on expert experience and objective weights based on the dispersion of measured data with their respective combination coefficients to obtain the comprehensive weight of each index. Finally, it calculates the comprehensive interactive performance score of the charging and discharging facility. The combined coefficients are not fixed values ​​set in advance, but are obtained by constructing a game theory-based objective function and combining it with constraints, and using a genetic algorithm for iterative optimization. This allows the comprehensive weights to achieve a scientific and reasonable balance between subjective and objective information, respecting the experience and knowledge of industry experts while avoiding biases caused by single subjective judgments. This makes the evaluation results more scientific and objective, and provides data support for the hierarchical and graded allocation of adjustable resources by power grid dispatch.

[0018] Furthermore, this invention breaks through the conventional simple linear combination weighting mode, creatively introducing a game theory optimization model and a genetic algorithm adaptive solution mechanism. By adding a safety offset constraint based on expert tolerance to the objective function and using a genetic algorithm for global optimization, the weight allocation can fully exploit the objective characteristics of massive measured data, while also being constrained by expert experience in extreme data anomalies, such as occasional communication delays or data packet loss. This method effectively avoids the algorithm getting trapped in local optima, solves the technical problem that traditional detection methods are prone to distortion of evaluation results under complex power grid conditions, and greatly improves the robustness and scientific rigor of remote detection and evaluation. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a remote detection method for the interactive performance of charging and discharging facilities according to an embodiment of the present invention.

[0021] Figure 2 This is an architecture diagram of a remote monitoring system for the full-chain interactive performance of charging and discharging facilities, according to an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of the process for comprehensive evaluation of interactive performance according to an embodiment of the present invention.

[0023] Figure 4 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0024] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0025] To address the issue that existing charging and discharging facility testing methods have performance blind spots and cannot trace the entire chain to specific stages, embodiments of the present invention provide a remote testing method for the interactive performance of charging and discharging facilities.

[0026] like Figures 1 to 3As shown, an embodiment of the present invention provides a remote detection method for the interactive performance of charging and discharging facilities, including the following steps S101 to S104.

[0027] Step S101: The edge controller deployed on the charging and discharging facility side collects multi-source operation data in real time via fieldbus, including at least vehicle end, pile end, grid side and platform end, and maps each operation data to a unified time axis.

[0028] Charging and discharging facilities refer to charging infrastructure with bidirectional charging and discharging functions in scenarios where electric vehicles interact with the power grid. Examples include V2G (Vehicle-to-Grid) charging piles and their associated power execution units, communication control units, and metering and monitoring units.

[0029] The edge controller comprises two layers: an energy router deployed at the site and a controller deployed at the charging pile. The energy router is deployed locally at the site as an edge computing node, while the controller communicates with the charging pile power module via a fieldbus.

[0030] Combination Figure 2 The vehicle side consists of electric vehicles, the charging pile side consists of V2G charging piles, the grid side includes the distribution network and grid simulation power supply, and the platform side consists of a server system deployed in the cloud, including hardware devices such as computing servers, storage servers and communication gateways, as well as databases and data processing software running on it.

[0031] Multi-source operational data can include vehicle-side data, charging pile-side data, grid-side data, and platform-side data. Specifically, vehicle-side data includes battery state of charge, battery temperature, battery voltage, battery current, battery degradation level, and the vehicle's current maximum charge / discharge capacity. Charging pile-side data includes the charging pile's output voltage, output current, output power, pile temperature, operating mode, fault status, and the actual power response value recorded at the charging pile. Grid-side data includes the voltage, current, power factor, load change rate, current harmonic distortion rate, and voltage harmonic distortion rate at the connection point. Platform-side data includes control command values ​​issued by the platform, command issuance timestamps, communication signal strength, data packet transmission delay, and packet loss rate.

[0032] The vehicle connects to the charging pile via a CAN (Controller Area Network) bus. The charging pile then connects to the edge controller via a fieldbus. BMS data from the vehicle is transmitted through the charging pile to the controller. The charging pile connects directly to the controller via a CAN bus. The controller sends power settings and collects electrical data such as voltage and current from the charging pile. The grid side connects to the electricity meter via an RS485 bus.

[0033] The controller reads electrical data from the charging pile and metering data from the electricity meter via the fieldbus, and simultaneously receives instructions from the platform via the communication network. The controller maps data from the vehicle, charging pile, power grid, and platform to the same timeline based on UTC (Coordinated Universal Time) according to their respective timestamps, performs protocol parsing, format conversion, and time alignment, and then uploads the data to the cloud.

[0034] Step S102 involves sending a standard test sequence to the charging and discharging facility via the cloud, synchronously recording the actual response data of multiple stages, and mapping each actual response data to a unified timeline. The stages include: cloud command sending stage, edge controller receiving stage, pile-end power execution stage, and vehicle BMS response stage.

[0035] Standard test sequences refer to standardized mathematical function-controlled waveforms generated in the cloud, including step waves, ramp waves, and sine waves. Distributing standardized test waveforms enables horizontal comparability of the interactive performance of facilities from different manufacturers and locations.

[0036] A standard test sequence is generated through a cloud-based aggregator scheduling simulation module. A cloud-edge data channel is established via a communication protocol, and the sequence is then distributed to edge-side facilities. During the distribution of the standard test sequence, data points are recorded at the cloud command distribution stage, the edge controller reception stage, the pile-end power execution stage, and the vehicle BMS response stage. The actual response data and corresponding timestamps for each stage are recorded, and the recorded data is mapped to a unified timeline.

[0037] Step S103: Based on multi-source operational data and actual response data, and combined with the preset interactive performance evaluation index system, extract the measured values ​​of each evaluation index in the interactive performance evaluation index system.

[0038] The interactive performance evaluation index system is a pre-established multi-dimensional evaluation standard used to determine whether the interactive capabilities of charging and discharging facilities meet the standards. This system covers four dimensions: vehicle-side, charging pile-side, grid-side, and platform-side, with several specific quantitative evaluation indicators under each dimension.

[0039] The interactive performance evaluation index system is constructed as follows: First, the objects to be evaluated are determined, including four aspects: vehicle battery status, charging pile electrical performance, power grid power quality, and platform communication quality. Then, key parameters reflecting the interactive capabilities of each object are selected as evaluation indicators, such as response speed, control accuracy, and communication latency. Finally, corresponding evaluation standards and calculation rules are set for each indicator.

[0040] Based on multi-source operational data and actual response data, how exactly do we extract the measured values ​​corresponding to each indicator?

[0041] Step S104: The comprehensive weights of each measured value and the corresponding evaluation index are integrated to calculate the comprehensive score of the interactive performance of the charging and discharging facility and obtain the comprehensive evaluation result. The comprehensive weight is obtained by combining the subjective weight based on expert experience and the objective weight based on the dispersion of the measured value with their respective combination coefficients. The combination coefficients are obtained by constructing a game theory-based objective function and iteratively optimizing it using a genetic algorithm after introducing constraints.

[0042] The overall weight of each evaluation indicator refers to a weight coefficient assigned to each indicator, which reflects the importance of that indicator in the overall evaluation of interactive performance. The overall weight is not determined from a single source, but rather is obtained by combining subjective and objective weights.

[0043] The subjective weights are determined based on expert experience. Specifically, domain experts compare the importance of indicators at each level—vehicle-side, pile-side, grid-side, and platform-side—pair by pairwise. Based on this, a judgment matrix is ​​constructed, and the subjective weight vector of each indicator is calculated, reflecting the experts' experiential judgment on the importance of each indicator.

[0044] Objective weights are determined based on the dispersion of each measured value. Specifically, by analyzing the variation of the measured values ​​of each evaluation indicator among different samples, information entropy is used to measure the degree of difference between the indicators—the greater the difference in measured values, the more information the indicator contains, the greater its contribution to differentiation in the evaluation, and the higher its corresponding objective weight, thus reflecting the objective laws inherent in the data itself.

[0045] The comprehensive weight in this embodiment of the invention is not a simple fixed-proportion superposition of the above subjective weight and objective weight, but is obtained by weighting the two separately through combination coefficients.

[0046] The combination coefficient is a parameter used to adjust the proportion of subjective and objective weights in the overall weight. Its value determines the degree to which the overall weight leans towards subjective experience or objective data. To ensure that the overall weight can reflect both subjective judgment and objective differences in a balanced way, the combination coefficient needs to be determined to find the optimal balance while taking into account the contributions of both.

[0047] Therefore, this embodiment introduces an objective function based on game theory. This function aims to minimize the overall deviation between the comprehensive weight and the subjective and objective weights. The basic idea is that if the combination coefficients cause the comprehensive weight to deviate excessively from either the subjective or objective weights, it indicates that the set of coefficients has failed to adequately consider information from either side, and the comprehensive weight is not representative enough. Therefore, by minimizing the deviation between the comprehensive weight and both the subjective and objective weights, a set of combination coefficients that achieves an optimal balance between the two can be found.

[0048] In the optimization process described above, constraints were also introduced. These constraints ensure that the deviation between the comprehensive weight of each evaluation indicator and its corresponding subjective weight does not exceed a preset safety offset threshold. Their purpose is to prevent extreme fluctuations in the measured values ​​of certain evaluation indicators from causing the objective weight to be excessively high or low, thus preventing the comprehensive weight from deviating excessively from the safety range set by experts based on power grid dispatching experience. Through the introduction of this constraint, the weight optimization results retain the objectivity of data-driven approaches while being reasonably regulated by expert prior knowledge regarding key safety indicators.

[0049] Since the objective function described above has constraints, conventional analytical solutions are difficult to apply directly. Therefore, this embodiment uses a genetic algorithm to iteratively optimize the combination coefficients. The genetic algorithm encodes candidate combination coefficients as individuals, using the degree of comprehensive satisfaction of the objective function and constraints as an evaluation index. Through evolutionary operations such as selection, crossover, and mutation, it gradually approaches the optimal combination coefficients in the solution space. This algorithm has good global search capabilities and adaptability to nonlinear constraints, effectively solving for the optimal values ​​of the combination coefficients while satisfying the constraints, thus ensuring the rationality and scientific nature of the comprehensive weights in the fusion of subjective and objective information. The comprehensive weights are obtained by fusing the subjective and objective weights. The fusion method involves linearly weighting the subjective and objective weights according to a preset fusion coefficient, ultimately obtaining a comprehensive weight vector for each evaluation index, ensuring that the weights incorporate both expert experience and objective data characteristics.

[0050] The method for calculating the comprehensive score by integrating each measured value with its corresponding comprehensive weight is as follows: For each evaluation indicator, its standardized measured value is multiplied by the comprehensive weight corresponding to that indicator to obtain the weighted score for that indicator; then, the weighted scores of all evaluation indicators are summed to obtain the comprehensive score for the interactive performance of the charging and discharging facility. The comprehensive evaluation result is generated based on this comprehensive score and is used to reflect the overall interactive performance level of the charging and discharging facility.

[0051] This invention provides a remote detection method for the interactive performance of charging and discharging facilities. It maps operational data from the vehicle, charging pile, power grid, and platform to the same timeline, and uses a standard test sequence from the cloud as an active stimulus. This aligns the response data of the four stages—command issuance, edge reception, charging pile execution, and vehicle response—on the same timeline. This unified timeline provides a benchmark for cross-device correlation analysis, connecting isolated device data into a continuous interactive chain. It solves the performance blind spot problem caused by inconsistent references in related technologies, enabling comprehensive monitoring of the charging and discharging interaction process and timely detection of anomalies in any stage.

[0052] Furthermore, this embodiment of the invention establishes an interactive performance evaluation index system covering the vehicle end, pile end, grid side, and platform end. Based on this system, it extracts the measured values ​​of each evaluation index. By combining subjective weights based on expert experience and objective weights based on the dispersion of measured data with their respective combination coefficients, it obtains the comprehensive weight of each index and finally calculates the comprehensive interactive performance score of the charging and discharging facility. These combination coefficients are not fixed values ​​pre-set by humans, but are obtained by constructing a game theory-based objective function, combining constraints, and using a genetic algorithm for iterative optimization. This ensures that the comprehensive weight achieves a scientific and reasonable balance between subjective and objective information, respecting the experience and knowledge of industry experts while avoiding biases caused by single subjective judgments. This makes the evaluation results more scientific and objective, providing data support for the grid dispatching of adjustable resources in a hierarchical and tiered manner.

[0053] Further, step S101 of this embodiment of the invention preferably includes: real-time acquisition of multi-source operating data, including at least vehicle-side, pile-side, grid-side, and platform-side data, via an edge controller deployed on the charging / discharging facility side and a fieldbus; wherein the multi-source operating data includes: battery state of charge and BMS (Battery Management System) status data of the vehicle-side, output voltage and output current data of the pile-side, power quality parameters of the grid-side, and control commands and communication quality data of the platform-side; preprocessing the multi-source operating data; wherein the preprocessing includes abnormal data cleaning; and mapping the multi-source operating data from the vehicle-side, pile-side, grid-side, and platform-side to a unified time axis after aligning by timestamps.

[0054] Specifically, the vehicle reports BMS data such as battery state of charge, battery temperature, voltage, current, degradation level, and maximum charge / discharge capacity to the charging pile via the CAN bus. The charging pile then transmits this data to the edge controller. The charging pile reports output voltage, current, power, pile temperature, operating mode, fault status, and actual power response value via the CAN bus. On the grid side, the power meter is connected via an RS485 bus, and the controller reads the voltage, current, power factor, load change rate, and harmonic distortion rate at the connection point. The platform sends control commands and command timestamps, and the controller records communication signal strength, transmission delay, and packet loss rate.

[0055] After receiving the above data, the controller uses its embedded data quality governance algorithm based on... The criteria involve anomaly cleaning, calculating the mean and standard deviation of each data dimension, and identifying and removing sampling points that deviate from the mean by more than three times the standard deviation.

[0056] To achieve alignment of multi-source data under a strict time base, the edge controller has a built-in high-precision real-time clock and is equipped with a GPS / BeiDou second pulse hardware time synchronization module, or it can deploy the IEEE 1588 precision time protocol over the network to synchronize with the cloud time server. The collected data from the vehicle, pile, and power grid sides are all hardware-stamped with a unified local time base after synchronization, achieving hard alignment of multi-source data on a unified time axis at the physical layer rather than the algorithm layer, no longer relying on the original, uncalibrated local clocks of each device.

[0057] The steps provided in this embodiment of the invention, by limiting the specific collection content of multi-source operational data and the time sequence alignment rules for anomaly cleaning, provide a data foundation with precise time alignment for subsequent interactive performance evaluation and fault tracing.

[0058] Furthermore, step S102 provided in this embodiment of the invention preferably includes: sending a standard test sequence to the charging and discharging facility via the cloud; wherein the standard test sequence includes at least one of a step wave, a ramp wave, or a sine wave; during the sending of the standard test sequence, data points are recorded at the cloud command sending stage, the edge controller receiving stage, the pile end power execution stage, and the vehicle BMS response stage, respectively, and the actual response data and corresponding timestamps of each stage are recorded; the recorded actual response data is mapped to a unified time axis so that the actual response data is aligned with the timing of the multi-source operation data.

[0059] Specifically, before issuing the standard test sequence, the edge controller must perform a front-end verification of the vehicle-side BMS real-time status via the CAN bus, including at least the current battery state of charge, the highest / lowest cell temperature, and the maximum allowable charge / discharge power limit of the BMS. If the current maximum adjustable power of the BMS is lower than the target amplitude of the test sequence, the cloud automatically triggers a step reduction processing strategy, proportionally adjusting the power step amplitude of the test sequence to within the BMS limit, and recording this 'limited step reduction' label. In subsequent performance evaluation, the actual response result is normalized to a per-unit value based on this label for evaluation, thereby effectively decoupling the battery's physical limits from device communication / execution failures.

[0060] Step waves are used to test response speed, ramp waves are used to test ramp rate, and sine waves are used to test dynamic tracking accuracy. One or more of these three waveforms can be selected and combined based on the test object and evaluation requirements.

[0061] When the standard test sequence is issued from the cloud, the issuance time is recorded in the cloud log. When the edge controller receives the downlink data packet, the reception time is recorded by the communication protocol stack. The completion time of the charging pile is determined by the controller reading the charging pile's output power in real time, with the time when the actual power enters the target value error band as the standard. The vehicle BMS response time is obtained by the controller parsing the response confirmation message returned by the BMS. The timestamps of the four stages are uniformly mapped to the same timeline based on UTC by the edge controller, so that the delay of each stage can be calculated and compared under a unified benchmark.

[0062] The above steps provided in this embodiment of the invention, by limiting the three standard test waveforms—step wave, ramp wave, and sine wave—enable all facilities to be evaluated under the same test benchmark, eliminating performance comparison deviations caused by inconsistent test conditions; at the same time, by plotting data from each stage and mapping it to a unified time axis, quantifiable raw time data is provided for calculating the independent time delay of each stage.

[0063] Furthermore, the method provided in this embodiment of the invention preferably further includes, before step S103: determining the evaluation indicators corresponding to the vehicle end, pile end, power grid side and platform end based on multi-source operation data; classifying each evaluation indicator into the corresponding capability dimension according to its capability type to form an interactive performance evaluation indicator system; wherein, the capability dimension includes: observable, measurable, adjustable and controllable.

[0064] Specifically, vehicle-side evaluation metrics may include real-time battery SOC, battery degradation level, data collection time (vehicle-to-grid), and charging demand. Pile-side evaluation metrics may include regulation response time, communication security, anti-islanding protection, and current stabilization accuracy. Grid-side evaluation metrics may include real-time load, power quality, current harmonics, and voltage harmonics. Platform-side evaluation metrics may include pile-to-platform communication latency, control terminal interaction cycle, regulation response rate, and regulation capacity.

[0065] These evaluation indicators are categorized into four capability dimensions based on their respective capability types: observable, measurable, adjustable, and controllable. Observability refers to whether the indicator can be directly acquired through sensors or communication, reflecting the transparency and visibility of the facility's status. Measurability refers to whether the indicator can be quantified, reflecting the numerical value of operating parameters. Adjustability refers to whether the indicator involves power adjustment range, reflecting the facility's flexible adjustment capabilities. Controllability refers to whether the indicator is related to the result of command execution, reflecting the effectiveness of control actions.

[0066] For example, real-time battery SOC and battery degradation are state parameters of the vehicle battery itself, unrelated to external control actions, and directly reflect whether the vehicle's state is observable; therefore, they are classified as observable. Charging demand refers to the current and voltage requirements requested by the vehicle from the charging station; these are quantifiable interaction parameters and are therefore classified as measurable. Adjustment response time reflects how quickly power changes from a command to the actual output; this reflects the facility's responsiveness to dispatch commands and is therefore classified as adjustable. Current stability accuracy reflects the deviation between the output current and the target value; this reflects the precision in executing control commands and is therefore classified as controllable.

[0067] Each evaluation indicator is assigned to a corresponding capability dimension to form a standardized evaluation system.

[0068] The method provided in this embodiment of the invention categorizes the various evaluation indicators extracted from multi-source operational data into four capability attributes: observable, measurable, adjustable, and controllable, forming a structured evaluation system that provides a standardized indicator framework for subsequent performance evaluation and weight calculation.

[0069] Furthermore, the method provided in this embodiment of the invention, which categorizes each evaluation index according to its capability type into corresponding capability dimensions to form an interactive performance evaluation index system, preferably includes: categorizing indicators reflecting the visibility of facility status into the observable dimension; wherein the observable dimension includes: charging facility installation status, charging facility operation status, charging power, and voltage distribution; categorizing indicators reflecting the quantifiability of operating parameters into the measurable dimension; the measurable dimension includes: load power consumption prediction, charging SOC status, charging pile power, and charging pile voltage and current; categorizing indicators reflecting power regulation potential into the adjustable dimension; the adjustable dimension includes: grid power real-time power, V2G discharge power, adjustable load, and adjustable total capacity; and categorizing indicators reflecting the execution capability of control commands into the controllable dimension; the controllable dimension includes: load control plan, load control scheme, adjustable load, and adjustable total capacity.

[0070] The specific indicators for each capability dimension are shown in Table 1.

[0071] Table 1. Comparison of Indicators for Each Capability Dimension of the Interactive Performance Evaluation Index System

[0072] It should be noted that both the adjustable and controllable dimensions in Table 1 include adjustable load and adjustable total capacity indicators, but the physical meaning and statistical scope of the two sets of indicators with the same name are different under different dimensions.

[0073] In the adjustable dimension, adjustable load and adjustable total capacity refer to the theoretically adjustable load potential and capacity upper limit calculated based on the current operating conditions (such as real-time grid power supply and V2G discharge power), reflecting the flexible adjustment capability of the facility. Their values ​​depend on physical constraints such as battery SOC and temperature.

[0074] In the controllable dimension, adjustable load and adjustable total capacity refer to the actual callable resources that have been included in the load control plan or scheme and are capable of receiving and responding to instructions issued by the platform. They reflect the achievable capacity at the control instruction execution level, and their values ​​are affected by factors such as communication quality and control terminal interaction cycle. The corresponding indicators in the two dimensions measure facility performance from the two levels of "potential assessment" and "execution confirmation," respectively, and play different evaluation functions in the comprehensive evaluation.

[0075] The steps provided in this embodiment of the invention specify the evaluation indicators under the four capability dimensions into collectable and quantifiable items, thereby clarifying the data source and collection object for the extraction of evaluation indicators and the acquisition of measured values.

[0076] Furthermore, step S103 provided in this embodiment of the invention preferably includes: extracting the measured values ​​corresponding to each evaluation index in the interactive performance evaluation index system based on multi-source operating data and actual response data; standardizing each measured value according to positive and negative indicators respectively; wherein, the positive and negative indicators are determined according to the consistency between the growth trend of the measured values ​​and the performance improvement trend.

[0077] Based on multi-source operational data and actual response data, the measured values ​​corresponding to each evaluation index in the interactive performance evaluation index system are extracted. Each measured value is then standardized according to positive and negative indicators. The positive and negative indicators are determined based on the consistency between the growth trend of the measured values ​​and the performance improvement trend. Based on expert experience and the dispersion of each measured value, the subjective and objective weights of each evaluation index are calculated, and these weights are then integrated to obtain the comprehensive weight of each evaluation index. Based on the standardized values ​​of each evaluation index and their corresponding comprehensive weights, the comprehensive interactive performance score of the charging and discharging facility is calculated.

[0078] Specifically, measured values ​​of various evaluation indicators are extracted from multi-source operational data and actual response data. These measured values ​​are then categorized into two types: positive indicators and negative indicators. Positive indicators are those where a larger measured value indicates better performance, such as available capacity and power quality. Negative indicators are those where a smaller measured value indicates better performance, such as communication delay and harmonic distortion rate.

[0079] The standardization process for each measured value is as follows: positive indicators are normalized using the formula of measured value minus minimum value divided by maximum value minus minimum value, and negative indicators are normalized using the formula of maximum value minus measured value divided by maximum value minus minimum value, so that all indicators are converted into dimensionless values ​​between 0 and 1.

[0080] The method provided in this embodiment of the invention eliminates the incomparability of evaluation indicators due to differences in dimensions and directions through standardization, ensuring that positive and negative indicators participate in subsequent weight fusion and comprehensive scoring calculation under a unified scale, so that the evaluation results are consistent in mathematical logic.

[0081] Furthermore, step S104 of this embodiment preferably includes: calculating the subjective and objective weights corresponding to each evaluation index based on the expert experience of fuzzy hierarchical analysis and the dispersion of each measured value according to information entropy theory; assuming that the comprehensive weight is obtained by combining the subjective and objective weights with their respective combination coefficients; based on the objective function of game theory, with the goal of minimizing the deviation between the comprehensive weight and the subjective and objective weights, and combined with the constraints, using a genetic algorithm to iteratively optimize the objective function to obtain the optimized combination coefficients; determining the final comprehensive weight based on the optimized combination coefficients, and fusing the subjective and objective weights to obtain the comprehensive weight of each evaluation index; calculating the comprehensive interactive performance score of the charging and discharging facility by weighted fusion of the measured values ​​of each evaluation index after standardization and the corresponding final comprehensive weight; and outputting the comprehensive evaluation result of the charging and discharging facility based on the comprehensive interactive performance score; wherein the comprehensive evaluation result includes the sub-item scores corresponding to each capability dimension, and the comprehensive health score obtained by weighted summation of the sub-item scores.

[0082] Specifically, in practical applications, if only simple linear weighting is used to integrate subjective weights (expert experience) and objective weights (data dispersion), it is easy for one weight to become too large and obscure the true physical meaning of the other weight. Meanwhile, compared with conventional simple combination weighting methods in existing technologies, this invention proposes a comprehensive weight optimization strategy based on a combination of game theory and genetic algorithm (GA).

[0083] Based on expert experience and the dispersion of each measured value, the subjective and objective weights of each evaluation indicator are calculated; the subjective and objective weights are then integrated to obtain the comprehensive weight of each evaluation indicator; based on the standardized measured values ​​of each evaluation indicator and their corresponding comprehensive weights, the comprehensive score of the interactive performance of the charging and discharging facility is calculated; based on the comprehensive score of the interactive performance, the comprehensive evaluation result of the charging and discharging facility is output; the comprehensive evaluation result includes the sub-scores corresponding to each capability dimension, as well as the comprehensive health score obtained by weighted summation of the sub-scores.

[0084] Specifically, the subjective weights employ fuzzy hierarchical analysis (FAHP). Experts use triangular fuzzy numbers to compare the pairwise importance of indicators at the same level. These triangular fuzzy numbers contain three values: a lower limit, a median, and an upper limit, representing the uncertainty range of the judgment. A fuzzy judgment matrix is ​​constructed using the median, and the consistency ratio is calculated. The matrix passes validation when the consistency ratio is less than 0.1. Then, the geometric mean of each row of indicators is calculated and normalized to obtain the fuzzy weights. These weights are then defuzzified and normalized again to output the subjective weight vector.

[0085] The calculated subjective weight vector Represented as: ,in, For the first i The subjective weight of each evaluation indicator, n This represents the total number of evaluation indicators.

[0086] Objective weights are calculated using the Entropy Weight Method (EWM). Information entropy is calculated based on the dispersion of the measured values ​​of each indicator. A smaller information entropy indicates a greater difference in the indicator among samples, a greater contribution to the evaluation, and a correspondingly higher objective weight. Specifically, the proportion of each sample value for each indicator is first calculated, then substituted into the entropy formula to obtain the information entropy. The difference coefficient is obtained by subtracting the information entropy from 1, and finally, the difference coefficients of each indicator are normalized to obtain the objective weight vector.

[0087] The calculated objective weight vector Represented as: ,in, For the first i Objective weights of each evaluation indicator.

[0088] Furthermore, the comprehensive weight is obtained by combining the subjective weight and the objective weight with their respective combination coefficients. The preferred combination method is a linear combination, which can be expressed as: .

[0089] in, This is the overall weight vector; This is the subjective weight vector; For objective weight vectors; , These are the two combination coefficients corresponding to the subjective weight vector and the objective weight vector, respectively.

[0090] The constructed objective function is preferably expressed as follows: .

[0091] in, This is the objective function constructed based on game theory.

[0092] The objective function described above, based on game theory, treats subjective and objective weights as two sides in a game. It seeks a Nash equilibrium solution acceptable to both sides by minimizing the deviation between the overall weight and these two weights. The physical meaning of this objective function is: when the overall weight leans towards subjective weights, the contribution of objective information is weakened; when the overall weight leans towards objective weights, the constraint of expert experience is ignored. Only when the deviation between the overall weight and both weights is minimized, i.e., when the overall weight achieves an optimal compromise between the two, is the combination coefficient optimal.

[0093] Meanwhile, considering the actual security requirements of power grid regulation, the final weights of certain key regulation indicators (such as response time and current stability accuracy) cannot be completely affected by objective data fluctuations and must be subject to security constraints based on expert experience. Therefore, this embodiment introduces nonlinear constraints into the optimization model, which are preferably expressed as follows: .

[0094] .

[0095] , .

[0096] in, The i-th component of the comprehensive weight vector is the comprehensive weight corresponding to the i-th evaluation index. The i-th component of the subjective weight vector is the subjective weight corresponding to the i-th evaluation index. is the expert tolerance offset threshold corresponding to the i-th evaluation index.

[0097] Due to the introduction of inequality constraints Traditional analytical matrix differentiation methods are difficult to directly solve for the global optimum. and Therefore, this embodiment uses a genetic algorithm (GA) for adaptive optimization, and the steps include: First, encoding and initialization are performed: an initial population is generated using real-number encoding, where each individual in the population represents a set of combination coefficients. .

[0098] Next, in order to minimize the objective function while satisfying the constraints, a fitness function with a penalty term is designed: .

[0099] In the formula, To prevent extremely small positive numbers with a denominator of zero (such as...) ); The penalty function is applied when an individual fails to meet the constraints. hour, Choose a very large positive number to force the individual to be eliminated in subsequent iterations.

[0100] Then, the roulette wheel selection method is used to retain individuals with high fitness; simulated binary crossover (SBX) and polynomial mutation operators are used to generate offspring populations to ensure population diversity and avoid getting trapped in local optima.

[0101] The genetic algorithm terminates when it reaches the preset maximum number of iterations, or when the optimal fitness of the population remains unchanged for multiple generations. The algorithm decodes and outputs the coefficients of the optimal individual. and .

[0102] Using the obtained optimal coefficients, the final comprehensive weight vector is calculated. Subsequently, through the formula Calculate the overall performance score of the charging and discharging facilities, among which, These are the measured values ​​of each indicator after standardization.

[0103] The above method constructs the objective through game theory and uses a genetic algorithm with a bias penalty term to solve for the optimal coefficients. It respects the expert experience of power grid dispatching while fully leveraging the objective characteristics of measured facility data. This effectively avoids the limitations of conventional fixed-weight allocation models when dealing with complex operating conditions and massive equipment differences, greatly improving the scientific rigor and accuracy of remote detection and evaluation. The method provided in this embodiment of the invention employs a two-step evaluation structure: first calculating the scores of each capability dimension, and then weighting and summing them to obtain a comprehensive health score. This ensures that the comprehensive evaluation result retains both fine-grained performance information of the facility under different capability dimensions and provides a macro-level evaluation of the overall health. The scores of each capability dimension can intuitively reflect the specific strengths and weaknesses of the facility in terms of "observability, measurability, adjustability, and controllability," avoiding the problem of a single total score masking performance imbalances between dimensions. This provides maintenance personnel with clear directions for targeted optimization of weak dimensions. The comprehensive health score provides an intuitive quantitative basis for power grid dispatching to quickly classify and stratify the use of large-scale adjustable resources.

[0104] In addition, this method includes a false alarm prevention mechanism to address response failures caused by battery physical characteristics. When the charging pile power execution stage fails to reach the target value within the timeout threshold, the system forcibly reads the latest BMS message cached by the edge controller. If the message shows that the current allowable charging and discharging power limit issued by the BMS is lower than the target value of the test sequence, the system automatically terminates the timeout judgment, generates a "vehicle-side physical capacity exceeded limit" alarm event, and removes the detection result from the "edge / charging pile communication and execution failure" statistics, thereby avoiding misreporting the inherent physical limitations of the battery as a failure in the entire chain of charging and discharging facility interaction performance.

[0105] Combination Figure 2 As shown, this embodiment of the invention provides a remote monitoring system for the interactive performance of the entire charging and discharging facility chain, used in conjunction with the monitoring method described in the above embodiments. The system adopts a four-level collaborative architecture of "cloud-pipe-edge-device," specifically including the grid side, vehicle side, charging pile side, edge control layer, and cloud platform layer.

[0106] The grid side is equipped with a grid simulation power supply and a grid-connected / off-grid switching switch. The vehicle side includes an electric vehicle and a vehicle simulator, which is interconnected with the V2G charging pile through a DC / AC interface simulator. The edge control layer consists of an energy router and a controller. The controller communicates with the V2G charging pile through a fieldbus. The energy router completes local data aggregation and preprocessing and then uploads the data to the cloud through a communication network. The cloud platform layer deploys a multi-source data acquisition and integration module, a vehicle-to-everything (V2X) interaction simulation module, and an aggregator scheduling simulation module, which respectively undertake the functions of data aggregation, virtual simulation, and standardized test sequence distribution, thus building a remote detection environment covering the entire chain.

[0107] Combination Figure 3 As shown in the embodiment of the present invention, the comprehensive evaluation process for interactive performance takes the measured values ​​of each evaluation indicator as input. After standardization, subjective weights are generated based on expert experience using FAHP (Fuzzy Analytic Hierarchy Process), while objective weights are generated based on the dispersion of the measured values ​​of the indicators using the entropy weight method. The two types of weights are then dynamically fused using an algorithm to obtain a comprehensive weight, which is then weighted and summed with the standardized measured values ​​to finally output a comprehensive interactive performance score and comprehensive evaluation result. Different standardization formulas are used for positive and negative indicators in the process to ensure that the indicators participate in subsequent calculations under a unified scale.

[0108] Embodiments of the present invention also provide a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0109] Embodiments of the present invention also provide a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the method of an embodiment of the present invention.

[0110] Embodiments of the present invention also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method of the embodiments of the present invention.

[0111] refer to Figure 4 The present invention will now be described in the form of a structural block diagram of an electronic device that can serve as an embodiment of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, 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, 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.

[0112] like Figure 4 As shown, the electronic device includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. RAM 403 can also store various programs and data required for the operation of the electronic device. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. An I / O interface (Input / Output Interface) 405 is also connected to bus 404.

[0113] Multiple components in the electronic device are connected to I / O interface 405, including: input unit 406, output unit 407, storage unit 408, and communication unit 409. Input unit 406 can be any type of device capable of inputting information into the electronic device. Input unit 406 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 407 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 408 may include, but is not limited to, disks and optical discs. Communication unit 409 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0114] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a CPU (Central Processing Unit), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention may be implemented as a computer program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 402 and / or communication unit 409. In some embodiments, the computing unit 401 may be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0115] Computer programs for implementing the methods of embodiments 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 or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. 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.

[0116] In the context of embodiments of the present invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. 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 of the foregoing.

[0117] It should be noted that the term "comprising" and its variations used in the embodiments of the present invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of the present invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0118] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are subject to strict compliance with relevant laws, regulations, and regulatory requirements in their collection, storage, use, processing, transmission, provision, and disclosure, and adhere to the principles of legality, legitimacy, necessity, and good faith. The acquisition of relevant information and data is premised on the user's explicit consent or other legitimate reasons, and a clear and convenient authorization management approach is provided to the user, allowing the user to independently choose to consent, withdraw consent, or refuse to provide relevant information. For functions that rely on user information, if the user does not authorize or withdraws authorization, the corresponding technical function cannot be implemented, and the technical solution of this invention is not applicable in this scenario.

[0119] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0120] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0121] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for remotely detecting the interactive performance of charging and discharging facilities, characterized in that, include: By deploying an edge controller on the charging and discharging facility side, multi-source operation data, including at least vehicle-side, pile-side, grid-side and platform-side data, is collected in real time via fieldbus, and the operation data is mapped to a unified time axis. The system sends standard test sequences to the charging and discharging facility via the cloud, synchronously records the actual response data of multiple stages, and maps each actual response data to the unified time axis; wherein, the stages include: cloud command sending stage, edge controller receiving stage, pile-end power execution stage, and vehicle BMS response stage; Based on the multi-source operational data and the actual response data, and combined with the preset interactive performance evaluation index system, the measured values ​​of each evaluation index in the interactive performance evaluation index system are extracted. The comprehensive weights of each measured value and its corresponding evaluation index are integrated to calculate the comprehensive score of the interactive performance of the charging and discharging facility, thereby obtaining a comprehensive evaluation result. The comprehensive weights are obtained by combining subjective weights based on expert experience and objective weights based on the dispersion of measured values ​​with their respective combination coefficients. The combination coefficients are obtained by constructing a game theory-based objective function and then using a genetic algorithm to iteratively optimize the solution after introducing constraints.

2. The remote detection method for the interactive performance of charging and discharging facilities according to claim 1, characterized in that, By deploying an edge controller on the charging and discharging facility side, multi-source operational data, including at least those from the vehicle end, the charging pile end, the power grid side, and the platform end, is collected in real time via a fieldbus, and all of the aforementioned operational data is mapped to a unified time axis, including: By deploying an edge controller on the charging and discharging facility side, multi-source operational data, including at least those from the vehicle end, the charging pile end, the power grid side, and the platform end, is collected in real time via fieldbus. The multi-source operational data includes: battery state of charge and BMS status data from the vehicle end, output voltage and output current data from the charging pile end, power quality parameters from the power grid side, and control commands and communication quality data from the platform end. The multi-source operational data is preprocessed; wherein, the preprocessing includes abnormal data cleaning; The multi-source operational data from the vehicle, pile, power grid, and platform are aligned by timestamps and mapped to the unified time axis.

3. The remote detection method for the interactive performance of charging and discharging facilities according to claim 1, characterized in that, Standard test sequences are distributed to the charging and discharging facility via the cloud, and actual response data from multiple stages are recorded synchronously. These actual response data are then mapped to a unified timeline, including: A standard test sequence is sent to the charging and discharging facility via the cloud; wherein the standard test sequence includes at least one of a step wave, a ramp wave, or a sine wave. During the standard test sequence distribution process, data points are recorded at the cloud command distribution stage, the edge controller receiving stage, the pile power execution stage, and the vehicle BMS response stage, respectively, and the actual response data and corresponding timestamps of each stage are recorded. The recorded actual response data is mapped to the unified time axis, so that the actual response data is aligned with the time sequence of the multi-source operation data.

4. The remote detection method for the interactive performance of charging and discharging facilities according to claim 1, characterized in that, Before evaluating the interactive performance of the charging and discharging facility, the method includes: Based on the multi-source operational data, the evaluation indicators corresponding to the vehicle end, the pile end, the power grid side, and the platform end are determined respectively. Each of the evaluation indicators is divided into corresponding capability dimensions according to its capability type, forming the interactive performance evaluation indicator system; wherein, the capability dimensions include: observable, measurable, adjustable, and controllable.

5. The remote detection method for the interactive performance of charging and discharging facilities according to claim 4, characterized in that, Each of the evaluation indicators is categorized into its corresponding capability dimension according to its capability type, forming the interaction performance evaluation indicator system, including: The indicators reflecting the visibility of facility status are divided into observable dimensions; wherein, the observable dimensions include: charging facility installation status, charging facility operation status, charging power, and voltage distribution; The indicators reflecting the quantifiability of operating parameters are divided into measurable dimensions; the measurable dimensions include: load power consumption prediction, charging SOC status, charging pile power, and charging pile voltage and current. The indicators reflecting power regulation potential are divided into adjustable dimensions; the adjustable dimensions include: grid-supplied real-time power, V2G discharge power, adjustable load, and adjustable total capacity; The indicators reflecting the ability to execute control commands are divided into controllable dimensions; the controllable dimensions include: load control plan, load control scheme, adjustable load, and adjustable total capacity.

6. The remote detection method for the interactive performance of charging and discharging facilities according to claim 1, characterized in that, Based on the multi-source operational data and the actual response data, and in conjunction with a preset interactive performance evaluation index system, the measured values ​​of each evaluation index in the interactive performance evaluation index system are extracted, including: Based on the multi-source operational data and the actual response data, extract the measured values ​​corresponding to each evaluation index in the interactive performance evaluation index system; Each of the measured values ​​is standardized according to positive and negative indicators respectively; wherein, the positive and negative indicators are determined based on the consistency between the growth trend of the measured values ​​and the performance improvement trend.

7. The remote detection method for the interactive performance of charging and discharging facilities according to claim 1, characterized in that, By integrating the measured values ​​with the corresponding evaluation indicators and their comprehensive weights, a comprehensive score for the interactive performance of the charging and discharging facility is calculated to obtain a comprehensive evaluation result, including: Based on fuzzy hierarchical analysis and information entropy theory, the subjective and objective weights corresponding to each evaluation index are calculated respectively. Assume that the overall weight is obtained by combining the subjective weight and the objective weight with their respective combination coefficients; Based on the concept of game theory, the objective function aims to minimize the deviation between the comprehensive weight and the subjective weight and the objective weight. Combined with the constraints, a genetic algorithm is used to iteratively optimize the objective function and obtain the optimized combination coefficients. The final comprehensive weight is determined based on the optimized combination coefficients, and the measured values ​​of each evaluation index after standardization are weighted and integrated with the corresponding final comprehensive weight to calculate the comprehensive score of the interactive performance of the charging and discharging facility. Based on the comprehensive score of the interactive performance, a comprehensive evaluation result of the charging and discharging facility is output; wherein, the comprehensive evaluation result includes the sub-scores corresponding to each capability dimension, and a comprehensive health score obtained by weighted summation of the sub-scores.

8. The remote detection method for the interactive performance of charging and discharging facilities according to claim 7, characterized in that, The formula for calculating the final comprehensive weight includes: ； ； ； ， ； ； ； in, This is the overall weight vector; For subjective weight vectors; For objective weight vectors; , These are the two combination coefficients corresponding to the subjective weight vector and the objective weight vector, respectively; i This indicates the sequence number of the evaluation index in the interactive performance evaluation index system. n The total number of evaluation indicators; The first of the comprehensive weight vectors i The component is the nth component. i The comprehensive weight corresponding to each evaluation indicator; The first subjective weight vector i The component is the nth component. i The subjective weights corresponding to each evaluation indicator; For the first i The safety offset threshold corresponding to each evaluation indicator; , These are the two optimal combination coefficients output after iterative optimization using a genetic algorithm; This is the final comprehensive weight vector obtained after optimization by the genetic algorithm; This is the objective function constructed based on game theory.

9. An electronic device, comprising: A processor and a memory storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the remote detection method for the interactive performance of a charging and discharging facility according to any one of claims 1 to 8.

10. A non-transitory machine-readable medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the remote detection method for the interactive performance of charging and discharging facilities according to any one of claims 1 to 8.

11. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the remote detection method for the interactive performance of charging and discharging facilities as described in any one of claims 1 to 8.

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