Charging station state verification and dynamic correction method, system, equipment and medium

Abstract

The invention provides a charging station state verification and dynamic correction method, system and device and a medium, and belongs to the technical field of electric vehicle charging, and the method comprises the steps: reading a configuration file; performing data acquisition on the charging station according to the parameters of the configuration file to obtain charging station data; performing state judgment according to the charging station data based on the state verification model to obtain a model state result; and when the model state result is inconsistent with the pile enterprise pushing state, dynamically correcting the state of the charging station. Whether the charging station is in the abnormal state or not is judged according to the collected data and the rule of the state verification model, the stations inconsistent with pile enterprise pushing are dynamically corrected, intelligent optimization of the charging station state is achieved, and therefore the accuracy of charging station state judgment, the user experience and the resource utilization efficiency are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, and in particular to a method, system, device and medium for verifying and dynamically correcting the status of a charging station. Background Technology

[0002] Currently, there are numerous and varied charging pile manufacturers connected to the charging aggregation platforms of car OEMs. Traditional technologies rely on a single data source (such as push notifications from charging pile manufacturers) to determine the status of charging stations, which can easily lead to misjudgments due to data delays or errors (such as a normal station being mistakenly marked as offline). This can result in electric vehicle users arriving at a charging station only to find that they cannot charge normally, or even that their vehicles do not have enough remaining battery power to reach the next charging station, causing significant losses and greatly reducing user trust. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes a method, system, device and medium for charging station status verification and dynamic correction.

[0004] In a first aspect, embodiments of the present invention provide a method for verifying and dynamically correcting the status of a charging station, including:

[0005] Read the configuration file;

[0006] Data is collected from the charging stations according to the parameters in the configuration file to obtain charging station data;

[0007] Based on the state verification model, the state is determined according to the charging station data to obtain the model state result;

[0008] When the model state results are inconsistent with the state pushed by the charging station company, the state of the charging station is dynamically corrected.

[0009] In some embodiments, reading the configuration file includes:

[0010] Initialize the parameters of the configuration file; wherein, the parameters include weight coefficient, status scoring threshold, site range distance, site historical usage time threshold, detection cycle, and pile enterprise code table;

[0011] The configuration file is read from the remote configuration repository.

[0012] In some embodiments, the step of collecting data from the charging station according to the parameters of the configuration file to obtain charging station data includes:

[0013] The detection cycle and the pile enterprise code table for timed monitoring are determined based on the parameters in the configuration file.

[0014] Based on the aforementioned detection cycle and the charging station enterprise coding table, multi-source data is collected from the charging stations to obtain initial charging station data.

[0015] The initial charging station data is preprocessed to obtain charging station data; wherein, the charging station data includes the charging station enterprise's push status, user feedback data, equipment status data, and environmental data.

[0016] In some embodiments, the state-based verification model performs state judgment based on the charging station data to obtain a model state result, including:

[0017] The weighting coefficients and status scoring thresholds are determined based on the parameters in the configuration file.

[0018] Based on the state verification model, a weighted score is calculated using the weight coefficients and charging station data to obtain the score result.

[0019] The scoring result is compared with the state scoring threshold to obtain the model state result.

[0020] In some embodiments, the method further includes:

[0021] The mean squared error is used as the loss function of the state verification model;

[0022] The weight coefficients are updated using gradient descent to minimize the loss function, resulting in the updated weight coefficients.

[0023] The updated weight coefficients are normalized, and the normalized weight coefficients are used for weighted scoring calculation.

[0024] In some embodiments, the method further includes:

[0025] The site range distance and site historical usage time threshold are determined based on the parameters in the configuration file.

[0026] After dynamic correction, the target object is given an early warning of abnormal site status based on the site range distance and the site historical usage time threshold;

[0027] Upon receiving the actual charging information upon arrival at the station from the user, the state verification model is optimized based on the actual charging information upon arrival at the station.

[0028] In some embodiments, the method further includes:

[0029] The updated station range distance is obtained based on vehicle charging information and user usage preference information;

[0030] The updated historical usage time threshold is obtained based on the time series analysis model and model fitting parameters;

[0031] The parameters of the configuration file are dynamically updated based on the updated site range distance and the updated historical usage time threshold.

[0032] Secondly, embodiments of the present invention provide a charging station status verification and dynamic correction system, including:

[0033] The file reading module is used to read configuration files;

[0034] The data acquisition module is used to collect data from the charging station according to the parameters in the configuration file to obtain charging station data;

[0035] The status judgment module is used to perform status judgment based on the charging station data according to the status verification model, and obtain the model status result.

[0036] The status correction module is used to dynamically correct the status of the charging station when the model status result is inconsistent with the status pushed by the charging station company.

[0037] Thirdly, embodiments of the present invention provide an electronic device, comprising:

[0038] One or more processors;

[0039] Memory, used to store one or more programs;

[0040] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods described above.

[0041] Fourthly, embodiments of the present invention provide a computer-readable medium on which a computer program is stored, the computer program being executed by a processor to implement the steps of any of the methods described above.

[0042] The charging station status verification and dynamic correction method provided by this invention includes: reading a configuration file; collecting data from the charging station according to the parameters of the configuration file to obtain charging station data; performing status judgment based on the charging station data using a status verification model to obtain a model status result; and dynamically correcting the charging station status when the model status result is inconsistent with the status pushed by the charging station company. This invention determines whether a charging station is in an abnormal state by using collected data and the rules of the status verification model, and dynamically corrects stations that are inconsistent with the status pushed by the charging station company, thereby achieving intelligent optimization of the charging station status and significantly improving the accuracy of charging station status judgment, user experience, and resource utilization efficiency. Attached Figure Description

[0043] Figure 1This is a flowchart illustrating a charging station status verification and dynamic correction method provided in an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of the overall process involved in the embodiments of the present invention;

[0045] Figure 3 A flowchart illustrating yet another embodiment of the charging station status verification and dynamic correction method provided in this invention;

[0046] Figure 4 This is a structural block diagram of a charging station status verification and dynamic correction system provided in an embodiment of the present invention;

[0047] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0048] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0049] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0050] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0051] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0052] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0053] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0054] Currently, automotive OEMs' charging platforms rely too heavily on a single data source, leading to asynchronous statuses at charging stations. This results in electric vehicle users receiving incorrect charging station status information, and in situations where electric vehicles cannot be recharged after arriving at a station.

[0055] In one related technology, an electric vehicle charging station early warning and monitoring system includes: a monitoring center, an environmental monitoring system, an interactive system, a cloud storage system, a power supply system, and a cloud platform system. The environmental monitoring system monitors the environment surrounding the charging station, monitors the charging station's operational status, and selects appropriate countermeasures based on the risk level of the early warning information. The environmental monitoring system can provide feedback on the charging station's on-site environmental information to the monitoring center, and the interactive system provides feedback to management personnel and electric vehicle users, facilitating management, improving the user experience, and ensuring the charging station's safety requirements. However, this technology is designed from the perspective of charging station operators and does not consider synchronously notifying car manufacturers of charging station status when multiple charging station providers are connected, nor does it consider early warning processing for potential users of the charging station or optimizing models based on user feedback.

[0056] In another related technology, a community charging aggregation platform includes an access module, a management module, an application module, a security module, and a transaction module. The access module aggregates B-end and C-end users, providing an access point. It supports interconnection, device-side adaptation protocol integration, and device-side aggregation platform protocol integration. The management module analyzes and monitors the operating and fault states of accessed devices using parameter information, enabling centralized platform management. The application module executes user business services, managing the entire lifecycle from access to operation and maintenance. The security module protects platform data security. The transaction module configures transaction strategies and executes transaction functions. This effectively aggregates various objects and services in community charging, improving management efficiency and resource utilization, and building a complete community charging ecosystem. However, this technology only implements charging aggregation functionality and does not consider handling misjudgments due to data delays or errors (e.g., a normal station being mistakenly marked as offline) when relying solely on a single data source (charging station company push). It also does not consider providing early warning notifications for abnormal charging station status based on user habits.

[0057] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides a method for verifying and dynamically correcting the status of charging stations. Figure 1 This is a flowchart illustrating a charging station status verification and dynamic correction method provided in an embodiment of the present invention.

[0058] As an embodiment of the present invention, such as Figure 1 As shown, the charging station status verification and dynamic correction method includes:

[0059] Step S1: Read the configuration file;

[0060] Step S2: Collect data from the charging stations according to the parameters in the configuration file to obtain charging station data;

[0061] Step S3: Based on the state verification model, determine the state according to the charging station data to obtain the model state result;

[0062] Step S4: When the model state result is inconsistent with the charging station enterprise's push state, the state of the charging station is dynamically corrected.

[0063] It should be noted that the execution subject in this embodiment can be an electronic device, which can be a computer device with data processing function, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment, the execution subject is a computer device in the cloud as an example for explanation.

[0064] Specifically, this embodiment uses multi-source data fusion for intelligent verification and dynamic correction of charging station status. It aims to solve technical challenges such as data uniformity in traditional communication between car manufacturers and charging station companies, leading to users being unable to charge at the station due to receiving incorrect station status information, and improving user experience and providing early warnings to potential users about station anomalies. The following describes the specific steps.

[0065] In some embodiments, reading the configuration file includes: initializing parameters of the configuration file; wherein the parameters include weight coefficients, status scoring thresholds, site range distances, site historical usage time thresholds, detection cycles, and pile enterprise coding tables; and reading the configuration file from a remote configuration repository.

[0066] Specifically, such as Figure 2 As shown, the initialization parameters are as follows: When initializing, the charging station enterprise code table, status scoring threshold, detection cycle, and weight coefficients w1, w2, w3, and w4 need to be configured. Regarding charging station abnormal warning notifications: the station range distance (in kilometers) and the time threshold parameter for users to use the station (the station's historical usage time threshold) can all be dynamically configured.

[0067] For example, cloud configurations can be dynamically distributed during initialization to dynamically set the overall threshold for monitoring sites. For instance, the configuration of charging station companies can be dynamically added or removed through the charging station company code table; for instance, if the status score threshold is set to 0.8, all sites with a model score lower than 0.8 can be marked as abnormal; for instance, the configuration of the detection period T can set the period for the cloud to periodically scan charging stations.

[0068] For example, the charging station abnormality warning notification configuration includes the following: the station range distance (in kilometers) and the station historical usage time threshold are configured during initialization and can be dynamically modified later based on vehicle charging information and user preferences.

[0069] It should be noted that regarding parameter selection: the charging station enterprise code table for periodic monitoring can be determined by the actual push status of different charging station enterprises. If a charging station enterprise experiences abnormal push notifications of charging station information, resulting in users being unable to charge at the station, it must be included in the monitored charging station enterprise code table under normal circumstances. Charging station enterprises that do not experience such issues are also recommended to be included in the monitoring code table. For the setting of weight coefficients w1, w2, w3, w4 and the status scoring threshold, an initial value will be set during initialization. It is recommended to configure these values ​​as the parameter values ​​used during the system testing phase. The weight coefficients w1, w2, w3, w4 and the status scoring threshold Th will be iterated based on real-time station adjustments and user feedback on charging status via the APP, learning from historical data. For the detection period T, it is recommended to configure it to be greater than or equal to the time period t during which the charging station enterprise normally pushes charging station status information. At the same time, it may cause multiple detections within a period t, resulting in unnecessary waste of server resources. Similarly, it is necessary to avoid the detection period being too long, which may lead to untimely dynamic correction and cause users to be unable to charge after arriving at the station. The user warning notification site range a (site range distance) and historical usage time threshold T0 can be dynamically set according to user usage habits and historical distance.

[0070] In some embodiments, data collection from charging stations is performed according to the parameters of the configuration file to obtain charging station data, including: determining the detection cycle and configuring a charging station enterprise code table for timed monitoring according to the parameters of the configuration file; collecting multi-source data from charging stations according to the detection cycle and the charging station enterprise code table to obtain initial charging station data; and preprocessing the initial charging station data to obtain charging station data; wherein the charging station data includes charging station enterprise push status, user feedback data, equipment status data, and environmental data.

[0071] Specifically, after reading the configuration file from the remote configuration repository, the cloud performs periodic scans and checks on charging stations that meet the conditions based on the parameters in the configuration file. After multi-source data collection and processing of such stations, a weighted score calculation and status judgment are performed through a status verification model.

[0072] For example, such as Figure 2 As shown, the cloud platform first performs multi-source data collection and preprocessing on charging stations based on the detection cycle T and the configured charging station enterprise code table for timed monitoring. The multi-source data collection can include data in the following four dimensions: charging station enterprise push status D0 (normal, abnormal), user feedback data D1 (collected through the brand APP to collect user feedback on successful / failed charging after arriving at the station), equipment status data D2 (obtained through IoT technology to acquire the charging station's operating status), and environmental data, i.e., information about the surrounding environment of the charging station D3 (such as weather and traffic conditions).

[0073] For example, data preprocessing may include the following two parts: data cleaning, i.e., removing invalid data (such as null values ​​and outliers); and data standardization (such as converting different data sources into a unified 0-1 scoring format). The preprocessed data is then used to calculate a weighted score using a state validation model.

[0074] In this embodiment, by using a multi-source data fusion method and a state verification model algorithm rule to determine the abnormal state of a charging station, the charging aggregation platform can use this rule to dynamically correct these stations that are inconsistent with the information pushed by the charging station companies, thereby achieving intelligent optimization of the charging station status.

[0075] In some embodiments, the state verification model is used to determine the state based on the charging station data to obtain the model state result, including: determining the weight coefficient and state scoring threshold according to the parameters of the configuration file; performing a weighted scoring calculation based on the weight coefficient and charging station data according to the state verification model to obtain a scoring result; and comparing the scoring result with the state scoring threshold to obtain the model state result.

[0076] Specifically, such as Figure 2 As shown, based on the charging station enterprise coding table {OID}, weight coefficients w1, w2, w3, and w4, and a detection period T, the weighted score Score of the charging station is calculated periodically. The system then checks whether the station's score matches the information pushed by the charging station enterprise based on the status score threshold Th. If they match, the process ends; otherwise, real-time correction is performed.

[0077] For example, a multi-source data fusion and status verification model is constructed by fusing data pushed by charging station companies, user feedback data, equipment status data, and environmental data. A weighted scoring mechanism (Score=w1*S1+w2*S2+w3*S3+w4*S4) is used to comprehensively judge the status of the charging station and improve the accuracy of the judgment.

[0078] In some embodiments, the method further includes: using the mean squared error as the loss function of the state verification model; updating the weight coefficients using gradient descent to minimize the loss function and obtain updated weight coefficients; normalizing the updated weight coefficients and using the normalized weight coefficients for weighted scoring calculation.

[0079] Specifically, how does the state validation model optimize the weight coefficients w1, w2, w3, and w4 based on historical data?

[0080] (1) Define the loss function: The mean squared error (MSE) can be used as the loss function to measure the deviation between the prediction results of the state validation model and the actual results:

[0081]

[0082] in, This represents the actual state (normal = 1, abnormal = 0). The model predicts the state (i.e., the normalized value of the score, ranging from [0, 1]); N is the number of historical data samples.

[0083] (2) Weight update formula: The gradient descent method can be used to update the weight coefficients w1, w2, w3, and w4, minimizing the loss function:

[0084]

[0085] Where α is the learning rate (e.g., 0.01), used to control the step size of weight updates; For the loss function with respect to weights The partial derivative of is calculated using the following formula:

[0086]

[0087] Among them, S j Rate the j-th data source (e.g., S1, S2, S3, S4).

[0088] (3) Weight normalization:

[0089]

[0090] The updated weights need to be normalized to ensure that w1+w2+w3+w4=1.

[0091] In some embodiments, when the model state result is inconsistent with the charging station enterprise's push state, the state of the charging station is dynamically corrected.

[0092] Specifically, for charging stations where the model's state judgment and the charging station company's push status are inconsistent, the cloud will dynamically correct the status and learn from historical data. After dynamic correction, for abnormal stations, all users whose vehicle distance or usage habits meet the conditions will be notified of the abnormal station status through the brand's APP. Users who receive the notification can avoid going to such stations to recharge their vehicles. Users can also provide feedback on the actual charging status through the APP after arriving at the station, further optimizing the model (state verification model).

[0093] Specifically, the dynamic correction mechanism: when the multi-source data is inconsistent with the data pushed by the pile enterprise, the status correction process is triggered in real time to ensure that the platform data is consistent with the actual operation. The weight coefficients w1, w2, w3, and w4 are optimized based on historical data to continuously improve the accuracy of the status verification model.

[0094] Specifically, the user feedback loop and early warning optimization: users provide feedback on the actual charging status through the APP, forming a data-driven continuous optimization mechanism. The early warning logic is dynamically adjusted based on user feedback to reduce false alarms and missed alarms and improve user experience.

[0095] Specifically, dynamic configuration and automated processing: It supports remote dynamic configuration of parameters (such as detection period T, offline date threshold Th, etc.) to automate the offline processing of charging stations, and dynamically adjusts early warning parameters (such as station range a, time threshold T0) in combination with user behavior data (such as historical charging habits, geographical location).

[0096] Understandably, traditional charging aggregation platforms lack conditional constraints on charging station status, relying entirely on information pushed by charging station companies. They cannot provide targeted anomaly warnings based on dynamic changes in station conditions. This embodiment employs a dynamically configured conditional filtering and warning notification mechanism, combining user charging history and geographical location information to provide users with predictive and targeted site anomaly warnings. This prevents users from arriving at charging stations only to find they cannot charge. Furthermore, after receiving a warning, users can provide feedback on their actual charging status via the app, further optimizing the model and improving user experience and trust in the charging platform.

[0097] It should be noted that the technical solution in this embodiment, through multi-source data fusion, dynamic correction, and user feedback closed loop, significantly improves the accuracy of charging station status judgment, user experience, and resource utilization efficiency, and has the following advantages:

[0098] 1. Improve the accuracy and real-time performance of charging station status assessment: By integrating multi-source data (charging station company push notifications, user feedback, equipment status, and environmental data), reduce the risk of misjudgment from a single data source (e.g., a normal station being mistakenly marked as offline). A dynamic correction mechanism updates the charging station status in real time, ensuring that platform data is consistent with actual operational conditions and preventing users from making wasted trips due to information lag.

[0099] 2. Optimize user experience and enhance user trust: The precise early warning mechanism only sends notifications to affected users, avoiding unnecessary warnings that may inconvenience them. A closed-loop user feedback mechanism allows users to participate in data optimization, increasing their sense of engagement and trust in the platform. This reduces instances where users are unable to charge their devices upon arrival, improving overall user satisfaction with the charging platform.

[0100] 3. Improve resource utilization efficiency and reduce operating costs: Reduce manual intervention and server resource waste through dynamic configuration and automated processing (e.g., avoid extra calculations caused by frequent scanning and misjudgment); continuously optimize models based on historical data and user feedback to reduce long-term maintenance costs; and reduce unnecessary user trips through accurate early warnings, indirectly reducing the operating pressure and service costs of charging stations.

[0101] The charging station status verification and dynamic correction method provided in this embodiment includes: reading a configuration file; collecting data from the charging station according to the parameters of the configuration file to obtain charging station data; performing status judgment based on the charging station data using a status verification model to obtain a model status result; and dynamically correcting the charging station status when the model status result is inconsistent with the status pushed by the charging station company. This embodiment determines whether the charging station is in an abnormal state by using collected data and the rules of the status verification model, and dynamically corrects stations that are inconsistent with the status pushed by the charging station company, thereby achieving intelligent optimization of the charging station status and significantly improving the accuracy of charging station status judgment, user experience, and resource utilization efficiency.

[0102] As another embodiment of the present invention, such as Figure 3 As shown, based on one embodiment, another embodiment of the charging station status verification and dynamic correction method of the present invention is proposed. The method further includes:

[0103] Step S5: Determine the site range distance and site historical usage time threshold based on the parameters in the configuration file;

[0104] Step S6: After dynamic correction, issue an early warning of abnormal site status to the target object based on the site range distance and the site historical usage time threshold;

[0105] Step S7: Upon receiving the actual charging information upon arrival from the user, optimize the state verification model based on the actual charging information upon arrival.

[0106] Specifically, the retraining and optimization process of the model (state verification model) is based on user feedback on the actual charging status via the app:

[0107] (1) Update the historical dataset using user feedback data. User feedback indicates the actual charging status (e.g., successful charging: y=1, failed charging: y=0); update the historical dataset by adding user feedback data for subsequent model training. Updated dataset:

[0108]

[0109] Among them, D old y represents the existing historical dataset; (S1, S2, S3, S4, y) represents the newly added data samples.

[0110] (2) Model retraining: using the updated dataset D new The retraining of the state validation model and optimization of the weight coefficients w1, w2, w3, and w4 training methods can refer to the specific process of optimizing the weight coefficients w1, w2, w3, and w4 based on historical data mentioned above, which will not be repeated here.

[0111] In some embodiments, the method further includes: obtaining an updated station range distance based on vehicle charging information and user usage preference information; obtaining an updated historical usage time threshold based on a time series analysis model and model fitting parameters; and dynamically updating the parameters of the configuration file based on the updated station range distance and the updated historical usage time threshold.

[0112] Specifically, the cloud dynamically updates the parameters of the calculation configuration file, including the notification site range a (site range distance) and the historical usage time threshold T0. The principle is to recalculate the range a (updated site range distance) based on vehicle charging information and user preferences.

[0113]

[0114] The vehicle-side system will calculate the final distance the user's vehicle traveled to the charging station using x. i The records reported to the cloud platform are N, which is the number of data points included in the weighted average calculation, and γ. i This represents the weighted value of the calculated data, where γ1=γ2=...=γ N When the result 'a' is calculated, it is the unweighted average of the N data points.

[0115] Based on the time series analysis model, the formula for calculating the historical usage time threshold T0 is as follows:

[0116]

[0117] Where, μ0, φ i ε0 are the model fitting parameters, determined through model training, and T0. (t-i) This represents the time threshold for the i-th time point in the dynamic configuration sequence.

[0118] The charging station status verification and dynamic correction method provided in this embodiment adopts a user feedback closed-loop and early warning optimization, dynamic configuration and automated processing. It supports remote dynamic configuration of parameters (such as detection period T, offline date threshold Th, etc.) to automate the offline processing of charging stations. It dynamically adjusts early warning parameters (such as station range a, time threshold T0) based on user behavior data (such as historical charging habits, geographical location). Users provide feedback on actual charging status through the APP, forming a data-driven continuous optimization mechanism. The early warning logic is dynamically adjusted according to user feedback to reduce false alarms and missed alarms and improve user experience.

[0119] Reference Figure 4 , Figure 4 This is a structural block diagram of an embodiment of the charging station status verification and dynamic correction system of the present invention. Figure 4 As shown, the charging station status verification and dynamic correction system includes:

[0120] File reading module 10 is used to read configuration files;

[0121] The data acquisition module 20 is used to collect data from the charging station according to the parameters of the configuration file to obtain charging station data;

[0122] The state judgment module 30 is used to make a state judgment based on the charging station data according to the state verification model, and obtain the model state result;

[0123] The status correction module 40 is used to dynamically correct the status of the charging station when the model status result is inconsistent with the status pushed by the charging station enterprise.

[0124] Specifically, the multi-source data fusion and status verification model: By fusing data pushed by charging station companies, user feedback data, equipment status data and environmental data from multiple sources, a charging station status verification model is constructed. A weighted scoring mechanism (Score=w1*S1+w2*S2+w3*S3+w4*S4) is used to comprehensively judge the status of the charging station and improve the accuracy of the judgment.

[0125] For example, the dynamic correction mechanism: when the multi-source data is inconsistent with the data pushed by the pile enterprise, the status correction process is triggered in real time to ensure that the platform data is consistent with the actual operation. The weight coefficients w1, w2, w3, and w4 are optimized based on historical data to continuously improve the accuracy of the model.

[0126] Specifically, the user feedback loop and early warning optimization: users provide feedback on the actual charging status through the APP, forming a data-driven continuous optimization mechanism. The early warning logic is dynamically adjusted based on user feedback to reduce false alarms and missed alarms and improve user experience.

[0127] For example, dynamic configuration and automated processing: It supports remote dynamic configuration of parameters (such as detection period T, offline date threshold Th, etc.) to automate the offline processing of charging stations, and dynamically adjusts warning parameters (such as station range a, time threshold T0) in combination with user behavior data (such as historical charging habits, geographical location).

[0128] The charging station status verification and dynamic correction system provided in this embodiment is an intelligent charging station status verification and dynamic correction system based on multi-source data fusion. It determines whether the charging station is in an abnormal state by collecting data and the rules of the status verification model, and dynamically corrects the stations that are inconsistent with the charging station companies' push, thereby realizing intelligent optimization of the charging station status and significantly improving the accuracy of charging station status judgment, user experience and resource utilization efficiency.

[0129] In addition, for technical details not described in detail in this embodiment of the charging station status verification and dynamic correction system, please refer to the charging station status verification and dynamic correction method provided in any embodiment of the present invention, which will not be repeated here.

[0130] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement any of the charging station status verification and dynamic correction methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0131] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0132] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0133] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0134] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the charging station state verification and dynamic correction methods described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0135] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described charging station status verification and dynamic correction method.

[0136] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0137] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0138] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0139] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0140] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0141] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0142] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0143] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0144] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0145] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for verifying and dynamically correcting the status of a charging station, characterized in that, include: Read the configuration file; Data is collected from the charging stations according to the parameters in the configuration file to obtain charging station data; Based on the state verification model, the state is determined according to the charging station data to obtain the model state result; When the model state results are inconsistent with the state pushed by the charging station company, the state of the charging station is dynamically corrected.

2. The method according to claim 1, characterized in that, The reading of the configuration file includes: Initialize the parameters of the configuration file; wherein, the parameters include weight coefficient, status scoring threshold, site range distance, site historical usage time threshold, detection cycle, and pile enterprise code table; The configuration file is read from the remote configuration repository.

3. The method according to claim 1, characterized in that, The step of collecting data from charging stations according to the parameters of the configuration file to obtain charging station data includes: The detection cycle and the pile enterprise code table for timed monitoring are determined based on the parameters in the configuration file. Based on the aforementioned detection cycle and the charging station enterprise coding table, multi-source data is collected from the charging stations to obtain initial charging station data. The initial charging station data is preprocessed to obtain charging station data; wherein, the charging station data includes the charging station enterprise's push status, user feedback data, equipment status data, and environmental data.

4. The method according to claim 1, characterized in that, The state-based verification model performs state judgment based on the charging station data to obtain the model state result, including: The weighting coefficients and status scoring thresholds are determined based on the parameters in the configuration file. Based on the state verification model, a weighted score is calculated using the weight coefficients and charging station data to obtain the score result. The scoring result is compared with the state scoring threshold to obtain the model state result.

5. The method according to claim 4, characterized in that, The method further includes: The mean squared error is used as the loss function of the state verification model; The weight coefficients are updated using gradient descent to minimize the loss function, resulting in the updated weight coefficients. The updated weight coefficients are normalized, and the normalized weight coefficients are used for weighted scoring calculation.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: The site range distance and site historical usage time threshold are determined based on the parameters in the configuration file. After dynamic correction, the target object is given an early warning of abnormal site status based on the site range distance and the site historical usage time threshold; Upon receiving the actual charging information upon arrival at the station from the user, the state verification model is optimized based on the actual charging information upon arrival at the station.

7. The method according to any one of claims 1 to 5, characterized in that, The method further includes: The updated station range distance is obtained based on vehicle charging information and user usage preference information; The updated historical usage time threshold is obtained based on the time series analysis model and model fitting parameters; The parameters of the configuration file are dynamically updated based on the updated site range distance and the updated historical usage time threshold.

8. A charging station status verification and dynamic correction system, characterized in that, include: The file reading module is used to read configuration files; The data acquisition module is used to collect data from the charging station according to the parameters in the configuration file to obtain charging station data; The status judgment module is used to perform status judgment based on the charging station data according to the status verification model, and obtain the model status result. The status correction module is used to dynamically correct the status of the charging station when the model status result is inconsistent with the status pushed by the charging station company.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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