Large-scale charging pile network layering remote verification method, system, equipment and medium

Abstract

The invention relates to the technical field of electric digital data processing, in particular to a large-scale charging pile network layered remote verification method, system, equipment and medium, and aims to complete unified management and task configuration and realize cross-site and cross-region centralized access and standardized processing by acquiring and summarizing charging pile operation data; through receiving the operation state reported by the node and carrying out screening and multi-level transmission, rapid processing and instruction interaction are realized on the edge side, the central pressure is reduced, and data transmission under limited communication is guaranteed; dynamic updating is realized through state reporting and analysis and execution of a control instruction in combination with local monitoring, and remote regulation and control response is improved; according to the method, credibility evaluation of multi-source data is carried out, an abnormal evolution model is constructed in combination with historical data, an operation state is predicted and compared with an actual state, advanced identification of pseudo-online, data drift and an abnormal trend is realized, a basis is provided for remote maintenance and risk disposal, and the operation stability and management level of a charging network are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric digital data processing, and in particular to a large-scale charging pile network hierarchical remote verification method, system, device and medium. BACKGROUND

[0002] With the continuous growth of the number of new energy vehicles, charging piles, as a key supporting infrastructure, are being constructed and deployed on a large scale in various scenarios such as cities, parks and highway service areas, showing the characteristics of extensive coverage and dense distribution. With the continuous expansion of the network scale, the operation state of the charging piles presents a trend of diversification and complexity, with significant differences in device types, communication methods and operating environments, and increasing demand for remote management and operation and maintenance.

[0003] However, the existing charging pile operation and maintenance mode mostly relies on manual inspection and passive response, which is difficult to discover potential risks in a timely manner, and the existing remote management system mostly adopts a centralized architecture, only having basic state reporting and control functions, and being difficult to cope with the demand for high concurrency, asynchronous communication and multi-protocol adaptation in a super large-scale device network. When multiple devices have state drift, pseudo-online or information loss, etc., there is a lack of effective data verification and consistency confirmation mechanism, which is easy to cause state misjudgment and fault omission, and is difficult to meet the reliability and intelligent management demand of large-scale, distributed charging network. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a large-scale charging pile network hierarchical remote verification method and system, which solves the problems in the prior art that it is difficult to efficiently process multi-source heterogeneous device data in a super large-scale charging pile network environment, it is unable to effectively identify abnormal conditions such as state drift and pseudo-online, and there is a lack of data consistency verification and credibility evaluation mechanism, and realizes hierarchical remote state verification, abnormal trend prediction and intelligent operation and maintenance scheduling, thereby improving the operation reliability and management efficiency of the charging network.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a large-scale charging pile network hierarchical remote verification method, comprising:

[0008] Obtaining and summarizing charging pile operation data to complete unified data management and task configuration;

[0009] Receiving and aggregating the operation state information reported by the nodes, performing preliminary screening and processing, and completing multi-level data interaction and instruction transmission;

[0010] Report running state information, and analyze and execute the received control instructions, while updating the state in combination with local monitoring information;

[0011] Trustworthiness evaluation is performed on data from different sources, an abnormal evolution model is established, the running state is predicted and compared, and analysis basis is generated.

[0012] As a preferred scheme of the large-scale charging pile network hierarchical remote verification method, the method comprises the following steps:

[0013] Based on the centralized management process, the running data of the charging piles from different sites and regions is received;

[0014] The running data is structured and classified to form a unified data management portal;

[0015] According to the set management strategy, the data management portal is associated with the task configuration process;

[0016] The task configuration process is synchronized to the corresponding execution end.

[0017] As a preferred scheme of the large-scale charging pile network hierarchical remote verification method, the method comprises the following steps:

[0018] Based on the hierarchical aggregation process, the running state information reported by the nodes in the jurisdiction is received;

[0019] The received running state information is feature extracted and preliminarily screened to identify abnormal and priority processing state information;

[0020] The preliminarily screened running state information is transferred and processed to match the corresponding instruction transmission path;

[0021] Data and instructions are transmitted between multiple levels.

[0022] As a preferred scheme of the large-scale charging pile network hierarchical remote verification method, the method comprises the following steps:

[0023] Triggering active reporting of the running state;

[0024] Packing and sending the running state data to the upper processing end;

[0025] Analyzing the received control instructions and performing corresponding operations;

[0026] Collect local operating parameters and record monitoring results to update status information.

[0027] The beneficial effects of this preferred technical solution are as follows: by actively reporting the operating status, the real-time transmission of key information is ensured; the status data is packaged and sent to improve transmission efficiency and integrity; by combining the parsing and execution of control commands, a rapid response to remote control is achieved; at the same time, a local monitoring information update mechanism is introduced to make remote control and local sensing complement each other, ensuring that the status information is consistent with the actual working conditions, thereby improving the reliability and controllability of the charging pile network in complex operating environments.

[0028] As a preferred embodiment of the hierarchical remote verification method for large-scale charging pile networks described in this invention, the step of collecting local operating parameters and recording monitoring results to update status information includes:

[0029] Real-time collection of key operating parameters of charging piles;

[0030] Key operating parameters include voltage, current, and monitoring values ​​related to the current operating status of the equipment.

[0031] The collected operating parameters are formatted and recorded; and monitoring log entries containing the collection time, parameter type, and value are generated.

[0032] When it is detected that the running parameters cannot be obtained normally or that the parameters fluctuate abnormally, a corresponding local alarm message is generated and marked.

[0033] The system compares the recorded monitoring data with the currently stored operational status information, updates items with discrepancies, and retains historical records.

[0034] As a preferred embodiment of the hierarchical remote verification method for large-scale charging pile networks described in this invention, the steps of assessing the trustworthiness of data from different sources, establishing an anomaly evolution model, predicting and comparing operational states, and generating analytical basis include:

[0035] Extract scoring factors from data from different levels;

[0036] The scoring factors include data consistency score, state fluctuation stability index, and feedback response rate;

[0037] The corresponding data trust value is calculated based on the rating factors, and the trust value is periodically updated and trend analyzed.

[0038] Extract abnormal evolution paths based on historical operational data and construct a knowledge graph of the relationship between state and evolution;

[0039] According to the knowledge graph, an abnormal evolution model is generated, and a state transition matrix is formed to represent the transition probability between states.

[0040] The abnormal evolution model is used to predict the running state of the equipment, and prediction state information is obtained.

[0041] The prediction state information is compared with the actually collected running state information, the deviation score and the deviation index are calculated, and the comparison result is recorded.

[0042] The beneficial effects of the preferred technical solution are: by evaluating the trustworthiness of data from different sources, low-trustworthiness information can be effectively eliminated in the multi-level and multi-site data fusion process, and the reliability of the overall analysis is improved; combined with the state fluctuation stability index and the feedback response rate, comprehensive judgment of the running data quality and real-time performance is realized; by establishing an abnormal evolution model and introducing a state transition matrix, not only the change law of the running state over time can be revealed, but also potential abnormalities can be predicted in advance; in the comparison process between the prediction result and the actual running state, the deviation score and the deviation index are formed, which further supports abnormal detection and risk warning, and improves the accuracy and foresight of large-scale charging pile network remote verification.

[0043] As a preferred scheme of the large-scale charging pile network hierarchical remote verification method, wherein: the corresponding data trust value is calculated according to the scoring factor, and the trust value is periodically updated and trend analyzed, comprising:

[0044] The trust degree calculator is called, the scoring factor is operated according to the pre-designed calculation formula, and the data trust value is obtained, which is expressed as:

[0045]

[0046] Wherein, alpha is a logic coefficient, beta is a stability coefficient, gamma is a feedback coefficient, C is a data consistency score, S is a state fluctuation stability index, and F is a feedback response rate.

[0047] The calculated data trust value and the historical trust value are stored and associated;

[0048] The data trust value is updated within a set time period, and the updated data trust value is analyzed for change trend;

[0049] The trend analysis result is bound to the corresponding equipment identifier.

[0050] In the second aspect, the application provides a large-scale charging pile network hierarchical remote verification system, comprising:

[0051] The cloud platform management module acquires and aggregates charging pile running data, completes unified data management and task configuration;

[0052] Edge gateway processing module, receiving and converging the running state information reported by the nodes, performing preliminary screening and processing, completing multi-level data interaction and instruction transmission;

[0053] Terminal control response module, reporting the running state information, and analyzing and executing the received control instructions, while updating the state in combination with the local monitoring information;

[0054] Data trust prediction module, performing trust evaluation on data from different sources, establishing an abnormal evolution model, predicting and comparing the running state, and generating analysis basis.

[0055] In a third aspect, the present application provides an electronic device, comprising:

[0056] a memory and a processor;

[0057] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions, which realize the steps of the large-scale charging pile network layered remote verification method.

[0058] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer executable instructions, which realize the steps of the large-scale charging pile network layered remote verification method when executed by a processor.

[0059] Compared with the prior art, the present application has the following beneficial effects: the present application realizes centralized access and standardized processing of running information across sites and regions by obtaining and summarizing charging pile running data and performing unified management and task configuration, establishes a stable data foundation for subsequent instruction scheduling and abnormal monitoring; by receiving and converging the running state information reported by the nodes, in combination with preliminary screening and multi-level transmission, the present application realizes rapid processing and distribution of the state of large-scale equipment on the edge side, reduces the data pressure of the center system, and maintains timely transmission of data and instructions when the communication condition is limited; by performing trust evaluation on data from different sources and establishing an abnormal evolution model, the present application combines the running state change with historical records and state transition rules, and can identify in advance in the early stage of false online, data drift or abnormal trend of the charging pile, providing support for remote maintenance and risk disposal; as a whole, the present application forms a layered remote verification system composed of a cloud platform, edge nodes and terminal devices, so that the charging network can continuously monitor the running state, discover problems in advance and take timely measures to reduce manual inspection, and improve the operation stability and management level of the large-scale charging network. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0061] Figure 1 The schematic diagram of the overall process of the large-scale charging pile network hierarchical remote verification method according to an embodiment of the present application.

[0062] Figure 2 The effect comparison diagram of the large-scale charging pile network hierarchical remote verification method according to an embodiment of the present application and the ordinary method on the pseudo online identification rate of the charging pile.

[0063] Figure 3 The schematic diagram of the structural framework of the large-scale charging pile network hierarchical remote verification system according to an embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings in the specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort should be within the scope of protection of the present application.

[0065] Embodiment 1, refer to Figure 1 According to an embodiment of the present application, a large-scale charging pile network hierarchical remote verification method is provided, comprising:

[0066] S1: obtaining and summarizing the charging pile operation data, completing unified data management and task configuration;

[0067] S2: receiving and aggregating the operation state information reported by the nodes, performing preliminary screening and processing, completing multi-level data interaction and instruction transmission;

[0068] S3: reporting the operation state information, and analyzing and executing the received control instructions, while updating the state in combination with the local monitoring information;

[0069] S4: performing trustworthiness evaluation on the data from different sources, establishing an abnormal evolution model, performing prediction and comparative analysis on the operation state, and generating analysis basis.

[0070] It should be noted that in the current large-scale charging pile network, there are many devices, wide distribution, heterogeneous communication protocols, and frequent state changes, which lead to data delay, state drift, pseudo-online, and information loss in remote management and operation, making it difficult to accurately grasp and confirm the consistency of the device state. Most existing systems use centralized architecture and only have basic state reporting and control capabilities. In a high-concurrency and multi-source heterogeneous environment, there is a lack of effective hierarchical processing and trusted verification mechanisms, and the identification of potential abnormal trends is insufficient, thereby affecting the stability and controllability of network operation.

[0071] Therefore, in view of the above-mentioned problems of insufficient distributed data management, limited state abnormality identification capability, and lack of prediction and early warning mechanism, through steps S1-S4, a complete process consisting of data acquisition and management, hierarchical aggregation and interaction, state reporting and instruction execution, and trust evaluation and prediction analysis is constructed, forming a unified data management portal and multi-level information interaction link to ensure that operation information can be smoothly transmitted between different levels; through trust evaluation and abnormal evolution modeling, quantitative analysis and trend prediction of the running state are realized; and through comparison and analysis of the predicted and actual results, clear data basis is provided for subsequent processing, thereby forming a hierarchical remote verification scheme for large-scale charging pile networks in the method system.

[0072] Embodiment 2 is an embodiment of the present application, which provides a hierarchical remote verification method for a large-scale charging pile network based on the above-mentioned embodiment.

[0073] In the present application, the charging pile operation data is acquired and aggregated in step S1, and unified data management and task configuration are completed, including:

[0074] A1: Based on the centralized management process, receiving charging pile operation data from different sites and regions;

[0075] A2: Structurally processing and classifying the operation data to form a unified data management portal;

[0076] A3: According to the set management strategy, associating the data management portal with the task configuration process;

[0077] A4: Synchronizing the task configuration process to the corresponding execution end.

[0078] Specifically, A1-A4 include receiving charging pile operation data from different sites and regions based on a centralized management process, completing compatible processing of different sources and different protocol data in the access process, thereby realizing unified gathering of multi-source operation information and ensuring entry consistency of subsequent data processing; organizing the received operation data in a structured processing and classification manner to form a unified data management entry, so that it can be directly called and distributed in the subsequent task configuration and execution process; according to the set management strategy, the data management entry is associated with the task configuration process, so that the task configuration can be directly matched with the corresponding data, ensuring the correspondence between the task content and the required data; the configured task is transmitted to the corresponding execution end through the synchronization process, ensuring that the execution end can accurately receive and execute the task instruction, and realizing effective connection between the management end and the execution end.

[0079] In an optional embodiment, the data management and task configuration in step S1 can also be realized by introducing a hierarchical cache and batch processing mechanism; when receiving charging pile operation data from different sites and regions, real-time data is first cached in a hierarchical cache area, and is divided into batches according to site priority, alarm level and task urgency; in the batch processing link, the operation data of the same batch is packaged and transmitted into the structured processing process, and the batch number and priority label are marked in the data management entry, so that the subsequent task configuration can preferentially issue execution instructions for high-priority batches, thereby adapting to the scheduling needs of large-scale networks in high-concurrency and alarm concentration burst scenarios.

[0080] In another optional embodiment, the data management and task configuration in step S1 can also be realized by combining node self-checking and dynamic task adjustment mechanisms; after the task configuration process is associated with the data management entry, the execution end node first performs self-checking to generate a current device availability and network state report; the management end dynamically adjusts the original task configuration according to the report, for example, when the node network state is unstable, it preferentially issues local executable diagnosis and data collection tasks and delays large data transmission tasks; when the node state returns to normal, the delayed tasks are automatically rescheduled, realizing adaptive optimization of task execution paths and data scheduling strategies under different operating conditions.

[0081] In the embodiment of the present application, in step S1, the data management portal is associated with the task configuration process according to the set management strategy, including: under the framework of centralized management, the cloud binds the data management portal formed by structured processing and classification with the corresponding task configuration process according to the preset business strategy and scheduling rules; the business strategy and scheduling rules cover task priority sorting, execution frequency setting, quick dispatch mechanism of abnormal tasks, etc., to ensure the consistency and controllability of the data receiving, classification, task allocation and execution process; the task configuration process after completion of binding is synchronized to the corresponding execution end through the management platform, realizing the orderly scheduling of the task execution path.

[0082] In an optional embodiment, the management strategy in step S1 can also be realized by dynamic priority adjustment based on the running state of the site; when high-frequency alarms or key task congestion occur in some areas of charging piles, the cloud dynamically improves the priority of related tasks in the area according to real-time monitoring results, while reducing the execution weight of tasks in sites with lighter load, to ensure that abnormal situations are handled in time and the balance of task allocation is maintained in the overall scheduling.

[0083] In another optional embodiment, the management strategy in step S1 can also be realized by a task batch configuration mechanism combined with a time window; in daily operation, the cloud sets multiple execution time windows according to different business scenarios, such as batch dispatching of regular detection tasks during night low load, and only dispatching necessary real-time monitoring and abnormal repair tasks during daytime high load, so as to improve the execution efficiency of the management strategy and reduce the additional burden of the network and terminal without affecting the normal charging of users.

[0084] In the embodiment of the present application, in step S2, the running state information reported by the receiving and converging node is received and converged, and preliminary screening and processing are performed, completing multi-level data interaction and instruction delivery, including:

[0085] B1: receiving the running state information reported by the nodes in the jurisdiction based on the hierarchical convergence process;

[0086] B2: performing feature extraction and preliminary screening on the received running state information, identifying abnormal and priority processing state information;

[0087] B3: transferring the running state information after preliminary screening, matching the corresponding instruction delivery path;

[0088] B4: delivering data and instructions between multiple levels.

[0089] Specifically, B1-B4 include receiving the running state information reported by the nodes in the jurisdiction through the hierarchical aggregation process, performing feature extraction and preliminary screening on the running state information reported by the nodes, identifying abnormal states and information that needs to be processed in priority; performing transfer processing on the screened running state information, and matching the corresponding instruction delivery path to complete the interaction and delivery of data and instructions between multiple levels.

[0090] In an optional implementation, the preliminary screening and processing in step S2 can also be performed by combining the time sequence characteristics of the node running state, using a set time window to perform continuous data comparison, distinguishing short-term fluctuations from long-term abnormalities, to avoid misjudgment due to transient fluctuations and improve the accuracy of the screening result.

[0091] In another optional implementation, the preliminary screening and processing in step S2 can also be performed by introducing a task priority strategy in the screening link, grading the abnormal state information of different categories according to the degree of urgency, and preferentially delivering high-priority tasks during transfer processing to ensure the timeliness and effectiveness of the abnormal information in multi-level delivery.

[0092] In the embodiments of the present application, the running state information reported by the nodes in the jurisdiction is received based on the hierarchical aggregation process in step S2, including: receiving the running state information reported by the nodes in the jurisdiction under the hierarchical aggregation process according to the regional hierarchical division and management architecture, for subsequent screening, processing and multi-level interaction.

[0093] In an optional implementation, the hierarchical aggregation process in step S2 can also set a local cache and preprocessing module at each level node to first perform deduplication and format unification on the running state information reported by the nodes, and then upload it to the upper level, to reduce the occupation of the transmission link by redundant data and improve the efficiency of hierarchical aggregation.

[0094] In another optional implementation, the hierarchical aggregation process in step S2 can also introduce a hierarchical identification and priority classification mechanism in the aggregation process, identify and group the running state information into three categories: abnormal, early warning and normal, and then deliver it to the upper layer level by level, to realize the preferential delivery and rapid response of important information.

[0095] In the embodiments of the present application, the running state information is reported and the received control instructions are parsed and executed in step S3, while the local monitoring information is updated, including:

[0096] C1: triggering active reporting of the running state;

[0097] C2: packaging and sending the running state data to the upper processing end;

[0098] C3: analyze the received control instruction and perform the corresponding operation;

[0099] C4: collect local running parameters and record monitoring results to update the state information.

[0100] It should be noted that in step S3, the active reporting of the running state is triggered, so that the key information can be transmitted in time; during the process of packaging and sending the running state data to the upper processing end, the efficiency and integrity of the transmission are guaranteed; after receiving the control instruction, analysis and execution are performed, realizing the rapid response of remote control; by combining the real-time collection of local running parameters and the generation of monitoring logs, not only can the collection time, parameter type and value be recorded in a standardized manner, but also when the running parameters cannot be normally obtained or abnormal fluctuations occur, alarm information can be generated and marked, thereby improving the identifiability of abnormal states; further, by comparing the recorded monitoring data with the stored running state information, the difference items are updated and the history is kept, so that remote control and local sensing are complementary to each other, and the consistency of the state information and the actual working condition is guaranteed.

[0101] Specifically, C4 includes real-time collection of key running parameters of the charging pile; formatting the collected running parameters; and generating a monitoring log entry containing the collection time, parameter type and value; when it is detected that the running parameters cannot be normally obtained or the parameters abnormally fluctuate, the corresponding local alarm information is generated and marked; the recorded monitoring data is compared with the currently stored running state information, the items with differences are updated, and the history is kept;

[0102] Among them, the key running parameters include voltage, current reflecting the current working state of the device, and monitoring values related to the running state;

[0103] C1-C4 includes the active reporting capability of the running state required by the node side; the locally collected running parameters and state results are packaged and sent to the upper processing end; after receiving the control instruction issued by the cloud or the upper platform, the node needs to complete the instruction analysis and perform the corresponding operation; the node side dynamically updates the running state in combination with the local monitoring information during the execution process, so as to ensure the real-time and accuracy of the state information.

[0104] In an optional embodiment, the analysis and execution in step S3 can also be realized by introducing an instruction legality verification mechanism in the control instruction analysis link; specifically, after receiving the control instruction issued by the upper platform, the node first verifies the legality of the instruction content and authenticates the source of the instruction, such as comparing the instruction format, timestamp and digital signature, to ensure that the instruction has not been tampered with and the source is credible. If the instruction verification is passed, the corresponding operation is continued; if the verification fails, local alarm is triggered and execution is refused, thereby improving the security and reliability of remote control.

[0105] In another optional embodiment, the parsing and execution in step S3 can also be implemented by introducing a hierarchical confirmation mechanism at the instruction execution stage; specifically, for instructions involving sensitive control operations such as voltage and current adjustment, the node side will not execute directly upon receiving, but first perform simulation rehearsal or safety threshold comparison in combination with local monitoring parameters, confirm that the execution result will not cause abnormal running state, and then formally execute the control action; if the simulation result or comparison result exceeds the set threshold, an alarm is triggered first and manual intervention is waited for. This way can effectively avoid misoperation caused by abnormal instructions or sudden working conditions, and improve the controllability and robustness of the system in complex running environment.

[0106] In the embodiments of the present application, the trustworthiness of data from different sources is evaluated in step S4, an abnormal evolution model is established, the running state is predicted and compared, and analysis basis is generated, including:

[0107] D1: score factors are extracted for data from different levels;

[0108] D2: calculate the trust value of the corresponding data according to the score factors, and periodically update and analyze the trend of the trust value;

[0109] D3: extract abnormal evolution paths based on historical running data, and construct a knowledge graph of state and evolution relationship;

[0110] D4: generate an abnormal evolution model according to the knowledge graph, and form a state transition matrix to represent the transition probability between states;

[0111] D5: use the abnormal evolution model to predict the running state of the equipment and obtain predicted state information;

[0112] D6: compare the predicted state information with the actually collected running state information, calculate the deviation score and deviation index, and record the comparison result.

[0113] It should be noted that in step S4, by extracting the score factors and calculating the trust value of the data from different sources, low credibility information can be effectively eliminated in the multi-level and multi-site data fusion process, and the reliability of the analysis is improved; in periodic updating and trend analysis, combined with data consistency score, state fluctuation stability index and feedback response rate, the comprehensive judgment of operation data quality and real-time is realized, and the feedback result reflects the instruction execution situation, ensuring the dynamic and comprehensive of trust evaluation; on this basis, the historical operation data is used to construct the abnormal evolution path and knowledge graph, and the state transition matrix is generated, and the transition probability between different states is quantified in mathematical form, which not only reveals the evolution law of the operation state, but also provides a basis for modeling and prediction of potential abnormalities; further, through the abnormal evolution model combined with trust factors and historical states, the prediction result with prediction index is obtained, realizing the quantitative expression of prediction reliability; finally, the predicted state is compared with the actual operation state, the deviation score and deviation index are calculated, and the threshold value is set to determine when the continuous reliable prediction is realized, the early warning of abnormality is realized, thereby improving the accuracy and foresight of large-scale charging pile network remote verification.

[0114] Specifically, the score factors in D1 include data consistency score C, state fluctuation stability index S and feedback response rate F. Compared with the last reported state, C=1 when the change is reasonable, and C=0 when the change is unreasonable. The smaller the state fluctuation, the larger S is. S ranges from 0 to 1. F is the ratio of the number of successful responses to the number of issued instructions.

[0115] In D2, the trust degree calculator is called to operate the score factors according to the pre-designed calculation formula to obtain the data trust value, which is represented as:

[0116]

[0117] Wherein, a is a logic coefficient, β is a stability coefficient, γ is a feedback coefficient, C is a data consistency score, S is a state fluctuation stability index, and F is a feedback response rate.

[0118] In D4, the abnormal evolution model is generated according to the knowledge graph, and the state transition matrix is formed to represent the transition probability between states, which is represented as:

[0119]

[0120] Wherein, P i→j represents the transition probability from the i-th state to the j-th state, N i→j represents the number of observations from the i-th state to the j-th state in the historical data.

[0121] In D5, the abnormal evolution model is used to predict the device operation state, and the predicted state information is represented as:

[0122]

[0123] wherein m is the number of prediction reference paths, T i represents the device trust value at the i-th time of reporting data, Z(i) represents the state at the i-th time of reporting data, Y represents the state, and Q(Y) represents the prediction index;

[0124] In D6, the predicted state information is compared with the actually collected running state information, the deviation score and the deviation index are calculated, and the comparison result is represented as:

[0125]

[0126] wherein D represents the deviation score, N is the number of dimensions, w i represents the weight of the i-th dimension, Y(i) represents the predicted state of the i-th dimension, and R(i) represents the actual state of the i-th dimension;

[0127] wherein the deviation score is calculated according to Pd = 1-e -λ·D and the prediction deviation index Pd is calculated;

[0128] wherein λ is a sensitivity adjustment coefficient;

[0129] When the deviation index Pd is less than the threshold value, the prediction information is regarded as reliable prediction, and when there are three consecutive reliable predictions, the abnormal information of the subsequent prediction is fed back.

[0130] In an optional embodiment, the trust degree evaluation in step S4 can also be realized by introducing a multi-source cross-checking mechanism, that is, while calculating the trust value of different levels of data, some core running parameters (such as voltage, current and response delay) are selected to be cross-compared between different nodes, and when the data trends of adjacent nodes or similar nodes are consistent, the trust value weight is improved; when the data of a certain node deviates obviously from the overall trend, the weight is reduced or marked as abnormal, so as to further improve the robustness and accuracy of trust evaluation in a large-scale node environment.

[0131] In another optional embodiment, the trust degree evaluation in step S4 can also be realized by dynamic correction combined with a time decay factor, that is, a time-based decay parameter is introduced when calculating the trust value, and for the node data that has not been reported for a long time or has a large feedback delay, the trust value decreases with time, so as to reflect the importance of information freshness; at the same time, in the subsequent abnormal evolution model prediction, the data with high timeliness is preferentially used, the fitting degree of the prediction result to the real-time running state is improved, and the misjudgment caused by old data is avoided.

[0132] In summary, by collecting charging pile operation parameters, constructing a virtual model and comparing with the real operation state, combining hierarchical aggregation and active reporting mechanism, the application realizes unified management of multiple levels and multiple nodes; through instruction analysis and local monitoring update during operation, the real-time and accuracy of state information are ensured; further introducing trust evaluation and abnormal evolution model, the operation state is predicted and deviation comparison is carried out, supporting abnormal identification and risk warning; finally, the analysis result is fed back to the control end and the virtual model is updated, forming a complete closed loop of data acquisition, transmission, prediction and control. The application improves the operation reliability and intelligent level of large-scale charging pile network as a whole.

[0133] Embodiment 3, refer to Figure 2 For an embodiment of the application, a hierarchical remote verification method for large-scale charging pile network is provided. In order to verify the beneficial effects of the application, economic benefit calculation and simulation experiment are used for scientific demonstration.

[0134] 100 charging piles are tested, the pseudo online identification rate of each charging pile is calculated, and the comparison chart as shown in Figure 2 is obtained, and the specific experimental test steps are as follows:

[0135] 100 charging piles are selected, numbered from 1 to 100, and are installed and connected.

[0136] System configuration is carried out, wherein the traditional system adopts a detection system based on static rules, such as data fluctuation detection and threshold judgment; the system of the application adopts data trust scoring, cross-layer data comparison and abnormal evolution modeling technology.

[0137] Each device reports its state (such as voltage, current, communication state, etc.) during the test period, and simulates device failure (such as power failure, temperature overload, etc., but the charging pile still reports normal state as a fake); each charging pile reports 100 data per day, including: device ID, current state, health index (simulated data), fault type (simulated data), actual state and reported state.

[0138] Each data is manually labeled, including normal: device works normally; pseudo online: device has a fault, but the reported state is normal.

[0139] The data record is as follows:

[0140] Device ID: charging pile number;

[0141] Fault type: such as "voltage too high";

[0142] State: reported state (such as "normal");

[0143] Manual annotation: whether it is a pseudo online device.

[0144] The charging piles are divided into two groups:

[0145] The experimental group (the system of the patent): the layered remote verification system of the patent is enabled for detection; the control group (the traditional system): the traditional detection system based on simple rules (such as voltage fluctuation detection, time delay detection, etc.) is enabled.

[0146] The running experiment includes: the first stage: running for 1 week, and the data is reported by hour; the second stage: collecting the identification results of all pseudo online devices, including: the experimental group (the system of the patent): identifying pseudo online devices through cross-layer data scoring, abnormal evolution modeling, trust prediction and other methods; the control group (the traditional system): detecting the device state through traditional rules and threshold judgment.

[0147] The accuracy of identifying pseudo online devices of each group of systems is calculated; the identification rates of pseudo online devices of the two systems are compared; the differences in the identification rates of pseudo online devices of each group of systems are shown by charts, and the specific data includes the identification rate of the traditional system and the identification rate of the system of the patent.

[0148] Embodiment 4, the above is a schematic scheme of a layered remote verification method for a large-scale charging pile network. It should be noted that the technical scheme of the layered remote verification system for the large-scale charging pile network belongs to the same concept as the technical scheme of the layered remote verification method for the large-scale charging pile network described above. The technical scheme of the layered remote verification system for the large-scale charging pile network in this embodiment is not described in detail, and can be referred to the description of the technical scheme of the layered remote verification method for the large-scale charging pile network.

[0149] Referring to Figure 3 , the embodiment also provides a layered remote verification system for a large-scale charging pile network, comprising:

[0150] A cloud platform management module acquires and aggregates charging pile operation data, and completes unified data management and task configuration;

[0151] Specifically, it includes a policy configuration unit, a centralized analysis unit and an operation and maintenance scheduling unit; the policy configuration unit is used to set configuration parameters such as verification policy and task frequency; the centralized analysis unit is used to accept device state data uploaded by the edge gateway and perform unified analysis; the operation and maintenance scheduling unit automatically generates operation and maintenance strategies based on the analysis results and sends remote instructions;

[0152] The policy configuration unit includes a policy setting processor, a parameter manager and a policy synchronization processor; the policy setting processor is used to configure verification policy information; the parameter manager is used to group manage policy parameters of different sites; the policy synchronization processor is used to synchronize the set policy to the corresponding edge gateway module;

[0153] The centralized analysis unit includes a data aggregation processor, a multi-dimensional anomaly identifier, and an analysis result outputter; the data aggregation processor aggregates data from the gateway according to the ID and time sequence; the multi-dimensional anomaly identifier identifies abnormal problems based on a statistical model; and the analysis result outputter is used to package the identification result into a standard abnormal report and output it;

[0154] The operation and maintenance scheduling unit includes a task generation processor, an instruction scheduling controller, and an execution record manager; the task generation processor creates corresponding control tasks according to the analysis result; the instruction scheduling controller schedules instruction sequences based on the control tasks and issues them; and the execution record manager is used to record the execution feedback, time point, and status code of each instruction;

[0155] The edge gateway processing module receives and aggregates the running state information reported by the nodes, performs preliminary screening and processing, and completes multi-level data interaction and instruction delivery.

[0156] Specifically, it includes a device state acquisition unit, a preliminary screening judgment unit, and a transfer control unit; the device state acquisition unit is used to periodically collect state information from charging piles in the jurisdiction; the preliminary screening judgment unit is used to preliminarily screen and make a simple judgment on the collected data; and the transfer control unit is used to transfer and process data and instructions.

[0157] The device state acquisition unit includes a state acquisition controller, a protocol adaptation processor, and a timestamp marker; the state acquisition controller is used to set the acquisition frequency and acquisition field of each device; the protocol adaptation processor is used to realize communication protocol analysis and conversion with different terminal charging piles; and the timestamp marker is used to add accurate timestamp information to each acquisition data.

[0158] The preliminary screening judgment unit includes a fast feature extractor, an abnormal rule matcher, and a preliminary screening marker processor; the fast feature extractor is used to extract basic judgment features; the abnormal rule matcher is used to quickly judge whether it is a preliminary abnormality according to the interface strategy rule; and the preliminary screening marker processor is used to mark priority information for suspected abnormal data.

[0159] The transfer control unit includes a data cache processor, an instruction router, and a forwarding state monitor; the data cache processor is used to cache reported data; the instruction routing processor is used to accurately route the received cloud instructions to the target device; and the forwarding state monitor is used to monitor the transfer situation of data and instructions.

[0160] The terminal control response module reports the running state information, analyzes and executes the received control instructions, and updates the state in combination with the local monitoring information.

[0161] Specifically, it includes a state self-reporting unit, an instruction execution unit, and a local monitoring unit; the state self-reporting unit is used to actively report the running state to the edge gateway; the instruction execution unit is used to receive and execute instructions; the local monitoring unit is used to monitor the key parameters of the charging pile in real time;

[0162] The state self-reporting unit includes a state reporting trigger, a reporting data packer, and a data sending processor; the state reporting trigger obtains the state data of the device based on the self-reporting mechanism; the reporting data packer is used to pack the state data into a standard message structure; and the data sending processor is used to send the packed data to the edge gateway processing module.

[0163] The instruction execution unit includes an instruction analysis processor, a function call processor, and an execution result feedback device; the instruction analysis processor is used to analyze the control instructions from the gateway; the function call processor calls the local functions of the charging pile based on the control instructions; and the execution result feedback device is used to feed back the execution state to the edge gateway processing module.

[0164] The local monitoring unit includes a parameter sampling processor, a local anomaly detector, and a monitoring log recorder; the parameter sampling processor is used to collect the running parameters of the device in real time; the local anomaly detector is used to issue an alarm information when normal sampling is not possible; and the monitoring log recorder is used to record each monitoring data and abnormal information.

[0165] The data trust prediction module evaluates the trust degree of data from different sources, establishes an abnormal evolution model, predicts and compares the running state, and generates analysis basis.

[0166] Specifically, it includes a cross-layer data scoring unit, an abnormal evolution modeling unit, and a state prediction comparison unit; the cross-layer data scoring unit is used to score the trust degree of data of each charging pile and gateway; the abnormal evolution modeling unit is used to create an abnormal model of the charging pile; and the state prediction comparison unit compares the subsequent state information based on the abnormal model with the actual situation.

[0167] The cross-layer data scoring unit includes a scoring factor extractor, a trust degree calculator, and a scoring update manager; the scoring factor extractor is used to extract scoring factors from data; the trust degree calculator is used to dynamically calculate the trust value of data; and the scoring update manager is used to periodically update and trend analyze the score of the device.

[0168] The abnormal evolution modeling unit includes a historical anomaly analyzer, an abnormal atlas constructor, and a model output processor; the historical anomaly analyzer extracts an abnormal evolution path based on historical data; the abnormal atlas constructor is used to construct a knowledge graph between the state and the evolution relationship; and the model output processor is used to output an abnormal evolution model.

[0169] The state prediction comparison unit comprises a state prediction processor, a comparison calculation processor and a deviation marking processor; the state prediction processor predicts the device state based on the abnormal evolution model; the comparison calculation processor is configured to compare the predicted information with the subsequent actual information; and the deviation marking processor is configured to mark the deviation degree of each prediction.

[0170] The embodiment also provides an electronic device suitable for the case of hierarchical remote verification of a large-scale charging pile network, comprising a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the method for hierarchical remote verification of a large-scale charging pile network proposed in the above embodiment.

[0171] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to implement the method for hierarchical remote verification of a large-scale charging pile network proposed in the above embodiment.

[0172] The storage medium proposed in the embodiment and the method for hierarchical remote verification of a large-scale charging pile network proposed in the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0173] From the above description about the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary universal hardware, and of course can also be realized by hardware. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, such as a floppy disk, a ROM, a RAM, a FLASH, a hard disk or an optical disk, and includes a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute the methods of various embodiments of the present application.

[0174] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A hierarchical remote verification method for large-scale charging pile networks, characterized in that, include: Acquire and aggregate charging pile operation data to complete unified data management and task configuration; It receives and aggregates the operational status information reported by nodes, performs preliminary screening and processing, and completes multi-level data interaction and instruction transmission; It reports operational status information, parses and executes received control commands, and updates the status based on local monitoring information. Trustworthiness assessments are conducted on data from different sources, anomaly evolution models are established, operational status is predicted and compared, and analytical basis is generated.

2. The large-scale charging pile network hierarchical remote verification method as described in claim 1, characterized in that, The process of acquiring and aggregating charging pile operation data to complete unified data management and task configuration includes: Based on a centralized management process, it receives charging pile operation data from different sites and regions; The operational data is structured and categorized to form a unified data management entry point; According to the established management strategy, the data management entry point is linked to the task configuration process; Synchronize the task configuration process to the corresponding execution end.

3. The large-scale charging pile network hierarchical remote verification method as described in claim 2, characterized in that, The receiving and aggregation nodes report operational status information, which undergoes preliminary screening and processing to complete multi-level data interaction and command transmission, including: Based on the hierarchical aggregation process, it receives operational status information reported by nodes within the jurisdiction; The received operational status information is subjected to feature extraction and preliminary screening to identify abnormal and priority-processing status information; The initially screened running status information is transferred and processed to match the corresponding instruction transmission path; Data and instructions are transferred between multiple levels.

4. The large-scale charging pile network hierarchical remote verification method as described in claim 3, characterized in that, The process of reporting operational status information, parsing and executing received control commands, and updating the status in conjunction with local monitoring information includes: Trigger proactive reporting of runtime status; Package the running status data and send it to the upper-level processing terminal; Parse the received control commands and execute the corresponding operations; Collect local operating parameters and record monitoring results to update status information.

5. The large-scale charging pile network hierarchical remote verification method as described in claim 4, characterized in that, The process of collecting local operating parameters and recording monitoring results to update status information includes: Real-time collection of key operating parameters of charging piles; Key operating parameters include voltage, current, and monitoring values ​​related to the current operating status of the equipment. The collected operating parameters are formatted and recorded; and monitoring log entries containing the collection time, parameter type, and value are generated. When it is detected that the running parameters cannot be obtained normally or that the parameters fluctuate abnormally, a corresponding local alarm message is generated and marked. The system compares the recorded monitoring data with the currently stored operational status information, updates items with discrepancies, and retains historical records.

6. The large-scale charging pile network hierarchical remote verification method as described in claim 5, characterized in that, The process of assessing the trustworthiness of data from different sources, establishing an anomaly evolution model, predicting and comparing operational states, and generating analytical basis includes: Extract scoring factors from data from different levels; The scoring factors include data consistency score, state fluctuation stability index, and feedback response rate; The corresponding data trust value is calculated based on the rating factors, and the trust value is periodically updated and trend analyzed. Extract abnormal evolution paths based on historical operational data and construct a knowledge graph of the relationship between state and evolution; An abnormal evolution model is generated based on the knowledge graph, and a state transition matrix is ​​formed to represent the transition probability between states; An abnormal evolution model is used to predict the operating status of equipment and obtain predicted status information; The predicted status information is compared with the actual collected operating status information, the deviation score and deviation index are calculated, and the comparison results are recorded.

7. The large-scale charging pile network hierarchical remote verification method as described in claim 6, characterized in that, The step of calculating the corresponding data trust value based on the rating factors, and periodically updating and trend analyzing the trust value, includes: The trust calculator is invoked to calculate the rating factors according to a preset formula, resulting in a data trust value, which is represented as follows: Where α is the logic coefficient, β is the stability coefficient, γ is the feedback coefficient, C is the data consistency score, S is the state fluctuation stability index, and F is the feedback response rate. The calculated data trust value is stored and associated with historical trust values; Update the data trust value within a set time period and analyze the trend of the updated data trust value. The trend analysis results are linked to the corresponding device identifiers.

8. A large-scale charging pile network hierarchical remote verification system, employing the large-scale charging pile network hierarchical remote verification method as described in any one of claims 1-7, characterized in that, include: The cloud platform management module acquires and aggregates charging pile operation data, and completes unified data management and task configuration. The edge gateway processing module receives and aggregates the operational status information reported by the nodes, performs preliminary screening and processing, and completes multi-level data interaction and command transmission. The terminal control response module reports operating status information, parses and executes received control commands, and updates the status based on local monitoring information. The data trust prediction module assesses the trustworthiness of data from different sources, establishes anomaly evolution models, predicts and compares operational status, and generates analytical basis.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the large-scale charging pile network hierarchical remote verification method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the large-scale charging pile network hierarchical remote verification method as described in any one of claims 1 to 7.

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