Remote verification method, system and equipment for charging pile based on digital twinning and medium

Abstract

The invention relates to the technical field of electric digital data processing, in particular to a charging pile remote verification method, system and equipment based on digital twinning and a medium, which are used for uniformly acquiring and processing various operating parameters and state information in the operating process of a charging pile to form standardized input; digital twin modeling is carried out, a virtual model corresponding to a real charging pile operation mechanism is constructed, and the state of the hardware logic and software protocol level is synchronously modeled; through real-time synchronization, alignment and mapping of time and variables are carried out on collected data and a virtual model, and timeliness and accuracy of virtual-real comparison are guaranteed; a state verification mechanism is adopted, operation deviation is calculated, potential faults are identified in combination with multi-period trends, and graded alarm information is generated; remote control is utilized, a control instruction is issued after abnormity is detected, reliable transmission is ensured by combining a secure channel, signaling compression and a receipt confirmation mechanism, feedback updating is completed through state verification and log archiving, and whole-process remote verification of the charging pile is achieved.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and in particular to a method, system, device and medium for remote verification of charging piles based on digital twins. Background Technology

[0002] With the rapid increase in the number of new energy vehicles, charging piles, as an important supporting infrastructure, are being built and deployed on an ever-expanding scale. Charging piles play a crucial role in ensuring daily vehicle charging and supporting the energy transition in transportation. At the same time, operation and maintenance management methods are gradually developing towards intelligence and remote operation to adapt to the needs of distributed charging networks and diversified application scenarios.

[0003] However, charging piles are prone to hidden faults such as equipment aging, operating parameter drift, and communication anomalies during long-term operation. Traditional methods relying on manual inspections and periodic maintenance are inefficient, slow to respond, and unable to detect potential anomalies in a timely manner, posing significant safety risks. Existing digital twin-based applications are mostly limited to status display and historical data analysis, lacking comprehensive capabilities for refined status comparison, anomaly prediction, and remote control. Meanwhile, traditional monitoring systems are insufficient in data synchronization accuracy, model reliability maintenance, and control risk assessment, failing to meet the demands for high reliability and intelligent operation and maintenance. Therefore, there is an urgent need for a solution that integrates real-time data acquisition, dynamic model comparison, and intelligent control strategies to achieve remote online verification and efficient closed-loop management of charging pile status. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a remote verification method and system for charging piles based on digital twins, which solves the problems in the prior art such as the lack of refined status comparison, anomaly prediction and remote control capabilities, insufficient data synchronization accuracy, imperfect model credibility management and insufficient control risk assessment in the operation status monitoring of charging piles. It realizes the real-time acquisition of charging pile operation parameters, dynamic comparison of virtual and real status, intelligent prediction of potential faults and safe closed-loop management of remote control.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a remote verification method for charging piles based on digital twins, comprising:

[0008] By collecting data, we can obtain various operating parameters and status information of the charging piles during operation and form a unified data input.

[0009] Digital twin modeling is performed to construct a virtual model corresponding to the actual operation mechanism of the charging pile, and the virtual and real states are compared.

[0010] Based on real-time synchronization, the collected data is matched with the virtual model and aligned with time.

[0011] A status verification mechanism is adopted to calculate the deviation and identify abnormal situations based on the comparison results of virtual and real status.

[0012] Using remote control, control commands are issued based on the verification results, and the virtual model status is updated based on feedback information.

[0013] As a preferred embodiment of the remote verification method for charging piles based on digital twins described in this invention, the step of acquiring multiple types of operating parameters and status information during the operation of the charging pile through data collection to form a unified data input includes:

[0014] Acquire detection information of charging piles during operation and complete the collection of data from different sources;

[0015] It supports the communication protocol of charging piles, parses and processes operational data, and ensures the consistency of data format.

[0016] Data caching is used to preprocess and initially cache the collected data.

[0017] As a preferred embodiment of the remote verification method for charging piles based on digital twins described in this invention, the step of performing digital twin modeling and constructing a virtual model corresponding to the actual operating mechanism of the charging pile includes:

[0018] Establish physical-level modeling to simulate the hardware operation logic of charging piles;

[0019] Build a logical-level model to reproduce the protocol response and control flow at the software level;

[0020] Based on model parameter management, the model parameters are dynamically maintained and adjusted.

[0021] As a preferred embodiment of the remote verification method for charging piles based on digital twins described in this invention, the step of corresponding and aligning the collected data with the virtual model in real time includes:

[0022] Time-align the collected data and unify the data timestamps;

[0023] The collected data is mapped and transformed to correspond with the variables of the virtual model;

[0024] Dynamic synchronization is used to periodically trigger state updates of the virtual model and input data.

[0025] As a preferred embodiment of the remote verification method for charging piles based on digital twins described in this invention, the step of employing a state verification mechanism to calculate the deviation and identify abnormal situations based on the comparison results of virtual and real states includes:

[0026] By comparing the equipment operation data with the output data of the virtual model, the state difference at the current moment is calculated to obtain the deviation result;

[0027] By combining deviation data from multiple time periods, comparing trends, identifying operational status, and comparing it with known fault modes;

[0028] Alarm information is generated based on the verification results, and corresponding alarm levels are triggered according to different degrees of anomaly.

[0029] The beneficial effects of this preferred technical solution are as follows: by comparing the equipment operation data with the virtual model output data and calculating the state deviation, the difference between the equipment and the model can be quantified in real time; by combining the deviation data of multiple time periods for trend analysis and comparing it with known fault modes, potential problems can be identified in advance before the fault is fully manifested; based on the verification results, alarm information of different levels can be generated, and abnormal situations can be classified and processed, thereby realizing the step-by-step monitoring and abnormal prompts of the charging pile operation status, ensuring that abnormal situations can be detected in a timely manner and responded to in a graded manner.

[0030] As a preferred embodiment of the remote verification method for charging piles based on digital twins described in this invention, the step of using remote control to issue control commands based on the verification results and updating the virtual model state based on feedback information includes:

[0031] Configure control commands, establish a command template library, set conditional triggers, match control commands according to fault type, and conduct control risk assessment;

[0032] A secure channel is established through communication management, signaling compression is performed, and acknowledgment is issued to complete the distribution of control commands;

[0033] Feedback processing is performed, the virtual model is self-updated based on the control results through status verification, and the control behavior and feedback results are logged and archived.

[0034] The beneficial effects of this preferred technical solution are as follows: By configuring control commands and establishing a command template library, corresponding control operations can be quickly matched under different fault types, and potential operational risks can be reduced by combining control risk assessment; a secure channel is built with communication management, and the security and reliability of command transmission are ensured by combining signaling compression processing and acknowledgment mechanism; during the feedback processing, the validity of the control results is ensured by status verification, and the virtual model is self-updated based on the feedback information. At the same time, the control behavior and feedback results are logged and archived, providing complete data support for subsequent analysis and management, thereby realizing the standardization, traceability and dynamic adjustment of the remote control process.

[0035] As a preferred embodiment of the remote verification method for charging piles based on digital twins described in this invention, the steps of configuring control commands, establishing a command template library, setting conditional triggers, matching control commands according to fault types, and performing control risk assessment are expressed as follows:

[0036] R c =α·P f +β·|ΔC|

[0037] Where ΔC is the expected difference in impact of control operations on core indicators, α is the safety factor, β is the correction factor, and R... c This indicates the control risk value.

[0038] Secondly, the present invention provides a remote verification system for charging piles based on digital twins, comprising:

[0039] The data acquisition module acquires various operating parameters and status information of the charging pile during its operation, forming a unified data input.

[0040] The digital twin modeling module performs digital twin modeling, constructs a virtual model corresponding to the actual charging pile operation mechanism, and compares the virtual and real states.

[0041] The real-time synchronization module matches and aligns the collected data with the virtual model in real time.

[0042] The status verification module uses a status verification mechanism to calculate the deviation and identify abnormal situations based on the comparison results of virtual and real status.

[0043] The remote control module uses remote control to issue control commands based on the verification results and updates the virtual model status based on feedback information.

[0044] Thirdly, the present invention provides an electronic device, comprising:

[0045] Memory and processor;

[0046] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the remote verification method for charging piles based on digital twins.

[0047] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the digital twin-based remote verification method for charging piles.

[0048] Compared with existing technologies, the beneficial effects of this invention are as follows: Through data acquisition, multiple operating parameters and status information during the charging pile operation are uniformly acquired and processed, ensuring that data from different sources and formats can be completely collected and standardized for input; through digital twin modeling, a virtual model corresponding to the actual charging pile operating mechanism is constructed, enabling synchronized modeling of hardware logic and software protocol levels, avoiding inconsistencies between the model and the actual equipment state; through real-time synchronization, the collected data and the virtual model are aligned and mapped at the time and variable levels, ensuring the timeliness and accuracy of virtual-real comparison; through status verification, operational deviations can be calculated in real time, and potential faults can be identified and graded alarm information generated by combining deviation trends over multiple time periods, enabling timely detection and alerts for anomalies; through remote control, control commands are issued after an anomaly is detected, ensuring the reliability of command transmission through secure channels, signaling compression, and acknowledgment mechanisms, and confirming execution status and saving records through status verification and log archiving; this invention realizes remote verification of the entire charging pile process from data acquisition, modeling, synchronization, verification to control, providing efficient and reliable intelligent operation and maintenance capabilities in distributed charging networks, improving operational safety and management standardization, and reducing manual inspection costs. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the overall process of a remote verification method for charging piles based on digital twins according to an embodiment of the present invention.

[0051] Figure 2 This is a comparison chart showing the effectiveness of the remote verification method for charging piles based on digital twins according to an embodiment of the present invention with traditional methods in terms of deviation and fault risk.

[0052] Figure 3This is an overall structural diagram of a remote verification system for charging piles based on digital twins, according to an embodiment of the present invention. Detailed Implementation

[0053] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0054] Example 1, referring to Figure 1 As one embodiment of the present invention, a remote verification method for charging piles based on digital twins is provided, comprising:

[0055] S1: Through data collection, obtain various operating parameters and status information of the charging pile during operation to form a unified data input;

[0056] S2: Perform digital twin modeling, construct a virtual model corresponding to the actual charging pile operation mechanism, and compare the virtual and real states;

[0057] S3: Based on real-time synchronization, the collected data is matched with the virtual model and aligned with the time.

[0058] S4: Employ a status verification mechanism to calculate the deviation and identify abnormal situations based on the comparison results of virtual and real states;

[0059] S5: Utilize remote control to issue control commands based on the verification results and update the virtual model status based on feedback information.

[0060] It should be noted that charging piles are susceptible to various factors during long-term operation, such as equipment aging, drifting operating parameters, and communication anomalies, leading to hidden faults. Relying solely on traditional manual inspections and periodic maintenance is not only inefficient and slow to respond, but also makes it difficult to detect potential anomalies in a timely manner, posing significant safety hazards. Furthermore, existing digital twin-based applications largely focus on status display and historical data analysis, lacking comprehensive capabilities such as refined status comparison, anomaly prediction, and remote control. They also have shortcomings in data synchronization accuracy, model reliability maintenance, and control risk assessment, failing to meet the actual needs of distributed charging networks for high reliability and intelligent operation and maintenance.

[0061] Therefore, to address the aforementioned issues of inaccurate real-time monitoring, untimely anomaly identification, and imperfect control methods, a complete technical process covering data acquisition, digital twin modeling, real-time synchronization, status verification, and remote control is constructed through steps S1-S5: In S1, multiple operating parameters and status information during the charging pile's operation are acquired through data acquisition, forming a unified data input; in S2, digital twin modeling is performed to construct a virtual model corresponding to the actual charging pile's operating mechanism and conduct a comparison between the virtual and real states; in S3, relying on real-time synchronization, the acquired data is matched and time-aligned with the virtual model; in S4, a status verification mechanism is adopted to calculate deviations and identify abnormal situations based on the virtual-real state comparison results; in S5, remote control is used to issue control commands based on the verification results and update the virtual model's state in conjunction with execution feedback information, thereby achieving remote online verification and efficient control processing of the charging pile's operating status.

[0062] Example 2 is an embodiment of the present invention. Based on the above embodiment, a remote verification method for charging piles based on digital twins is provided.

[0063] In this embodiment of the application, step S1 involves acquiring various operating parameters and status information of the charging pile during its operation through data collection, forming a unified data input, including:

[0064] A1: Obtain the detection information of the charging pile during operation and complete the collection of data from different sources;

[0065] A2: Supports the communication protocol of charging piles, parses and processes operational data, and ensures the consistency of data format;

[0066] A3: Utilize data caching to preprocess and initially cache the collected data.

[0067] Specifically, A1 to A3 include a sensor access unit for connecting with sensors to obtain sensor detection information, a protocol parsing unit for supporting communication protocols with charging piles and uploading data packets, and a data caching unit for preprocessing and initial caching of collected data.

[0068] In an optional implementation, the data acquisition in step S1 can also be achieved by introducing a multi-protocol interface processing mechanism during the sensor access process, so as to realize the compatible access of different types of sensors, and automatically identify and bind the corresponding sensors when the charging pile deployment environment changes, so as to ensure the integrity and adaptability of the collected information.

[0069] In another optional implementation, the data acquisition in step S1 can also be combined with edge buffering during the data caching process to cache short-cycle acquired data locally, and to perform preprocessing and tagging operations first in scenarios with large data fluctuations or unstable networks, so as to ensure the continuity and reliability of data for subsequent uploading and modeling.

[0070] In this embodiment of the application, step S2 involves digital twin modeling to construct a virtual model corresponding to the actual charging pile operation mechanism, and comparing the virtual and real states, including:

[0071] B1: Establish physical-level modeling to simulate the hardware operation logic of charging piles;

[0072] B2: Construct a logical-level model to reproduce the protocol response and control flow at the software level;

[0073] B3: Based on model parameter management, dynamically maintain and adjust model parameters.

[0074] It should be noted that by using digital twin modeling to establish a hardware operation logic model at the physical level, the actual operation process of the charging pile can be accurately mapped in the virtual environment, thereby enabling the verification and analysis of the hardware operation logic without relying on on-site operation. At the logic level, a model of protocol response and control flow is constructed, enabling the virtual model to realistically reproduce the software interaction and control mechanism, improving the ability to identify communication anomalies and control logic defects in virtual-real comparison. At the parameter management level, the model parameters are dynamically maintained and adjusted, ensuring that the virtual model remains consistent with changes in the actual operating state of the charging pile, guaranteeing the effectiveness and reliability of the model during long-term operation.

[0075] Specifically, physical layer modeling in B1 refers to establishing a corresponding hardware operation logic model to simulate the hardware operation logic of the charging pile; logical layer modeling in B2 refers to constructing a logical model to reproduce the protocol response and control flow at the software layer; and model parameter management in B3 refers to dynamically maintaining and adjusting the model parameters during the modeling process.

[0076] In an optional implementation, the digital twin modeling in step S2 can also introduce the status monitoring data of key components into the physical layer modeling, and load real-time data such as voltage, current and temperature rise collected by sensors into the hardware logic model, so that the virtual model can not only reflect the structural logic, but also dynamically reflect the health status of the core components, thereby improving the accuracy of virtual-real comparison.

[0077] In another optional implementation, the digital twin modeling in step S2 can also dynamically correct the protocol response process by combining the execution records of remote control commands in the modeling at the logical level, so that the virtual model can adapt to the actual situation on site in real time in terms of interaction logic, avoid deviation between the virtual and real models due to software upgrades or protocol changes, and thus ensure the consistency of the virtual model with the software interaction logic in long-term operation.

[0078] In this embodiment of the application, step S2 involves managing and dynamically maintaining and adjusting the model parameters, including maintaining and updating the model parameters used during the modeling process so that the modeling parameters can be adjusted according to the changes in the operating status of the charging pile, thereby ensuring the effectiveness of the virtual model during use.

[0079] In an optional implementation, the model parameter management in step S2 can also correct the model parameters by combining historical comparison errors. When there is a continuous deviation between the virtual model output and the collected data, the parameter weights are automatically adjusted to ensure that the model calculation results are closer to the operating state of the physical equipment.

[0080] In another optional implementation, the model parameter management in step S2 can also be achieved by setting a parameter version record, marking the time and content of each modification to the model parameters, and backtracking or restoring to the previous version when needed, so as to quickly adjust and restore the stability of the model when running for a long period of time or when the parameters are not updated properly.

[0081] In this embodiment of the application, step S3 involves matching and aligning the collected data with the virtual model in real time, including:

[0082] C1: Time-align the collected data and unify the data timestamps;

[0083] C2: Match and transform the collected data with the variables of the virtual model;

[0084] C3: Utilize dynamic synchronization to periodically trigger state updates of the virtual model and input data.

[0085] It should be noted that real-time synchronization, by calibrating the collected data by time, ensures that data from different sources are aligned under the same time reference, thereby avoiding comparison errors caused by time deviations. By establishing a mapping relationship between the collected operational data and the corresponding variables of the virtual model and performing format conversion, it ensures that the data can be correctly identified and processed by the virtual model. By setting a synchronization cycle to trigger the virtual model update and inputting real-time data into the virtual model, it ensures that the virtual model maintains consistency with the actual charging pile's operating status, thereby achieving high-precision alignment and dynamic consistency between the virtual and reality.

[0086] Specifically, C1, time alignment of the collected data, refers to time calibration of the collected data to ensure that data from different sources are aligned under the same time reference; C2, mapping and converting the collected data with the variables of the virtual model, refers to establishing a mapping relationship between the collected running data and the corresponding variables of the virtual model, and performing necessary format conversions; C3, using dynamic synchronization to periodically trigger the state update of the virtual model and input data, refers to triggering the update of the virtual model by setting a synchronization period, inputting real-time data into the virtual model, and completing state synchronization.

[0087] In an optional implementation, real-time synchronization in step S3 can also be achieved by introducing redundant time sources for comparison and correction. For example, the timestamps of the collected data can be cross-calibrated using both the local clock and the network time signal. Even with fluctuations in the communication network or clock drift within the device, data from different sources can still be aligned under a unified time reference, thus improving the reliability of time synchronization.

[0088] In another optional implementation, real-time synchronization in step S3 can also be achieved by dynamically adjusting the variable mapping relationship. That is, during the operation of the virtual model, according to the differences in the charging pile's operating status and protocol version, different formats of data input are adapted, and the newly collected data structure is updated and mapped in real time with the corresponding variables in the virtual model, thereby ensuring that the virtual model can continuously and accurately respond to the latest collected operating data.

[0089] In this embodiment of the application, step S3 utilizes dynamic synchronization to periodically trigger the state update of the virtual model and input data, including: periodically triggering the state update of the virtual model and inputting the collected data to ensure that the virtual model is consistent with the real running state.

[0090] In an optional implementation, dynamic synchronization in step S3 can also be achieved by introducing an event triggering mechanism. That is, when an abnormal event or a sudden change in key parameters is detected in the collected data, the virtual model's state update and data input are immediately triggered to ensure that the virtual model can respond quickly in case of emergencies and maintain real-time consistency with the actual operating state.

[0091] In another optional implementation, dynamic synchronization in step S3 can also be achieved through a hierarchical synchronization strategy. That is, based on the importance and update frequency of the charging pile operation data, the data is divided into high-frequency key parameters and low-frequency auxiliary parameters. The high-frequency key parameters are updated in a short cycle, while the low-frequency auxiliary parameters are updated in a long cycle, thereby reducing the computational and communication burden of the system while ensuring the real-time performance of the virtual model.

[0092] In this embodiment of the application, step S4 employs a state verification mechanism to calculate the deviation and identify abnormal situations based on the comparison results of virtual and real states, including:

[0093] D1: Compare the equipment operation data with the virtual model output data, calculate the state difference at the current moment, and obtain the deviation result;

[0094] D2: Combine deviation data from multiple time periods, compare trends, identify operational status, and compare with known fault modes;

[0095] D3: Generate alarm information based on the verification results and trigger the corresponding alarm level according to different degrees of anomaly.

[0096] It should be noted that by adopting a status verification mechanism in step S4, the charging pile operation data is compared with the output of the digital twin model and the real-time deviation results are calculated, so that the difference between the equipment and the virtual model can be quantitatively represented. By combining the deviation data of multiple time periods for trend statistics and fault mode comparison, potential hidden dangers can be identified in advance before the fault is fully manifested, enhancing the sensitivity of anomaly identification. Based on the verification results, graded alarm information is generated, which can classify abnormal situations into different levels such as minor, moderate and severe, and realize step-by-step monitoring and graded response of the operation status. Thus, a complete monitoring link from real-time detection, trend prediction to alarm classification is built in complex operation scenarios, effectively improving the accuracy and reliability of charging pile operation status monitoring.

[0097] Specifically, D1 includes comparing the collected charging pile operation data with the output of the digital twin model to calculate the real-time difference value, which is used as the deviation result; and calculating the state deviation δ at time t. t , is represented as:

[0098]

[0099] Where n represents the number of monitoring parameters, This represents the value of the i-th entity parameter. This represents the value of the i-th model parameter;

[0100] D2 includes statistical and trend analysis of deviation data from multiple time periods, comparing the operating status with preset failure modes to identify abnormal situations; and calculating the failure probability P for the next cycle. f , is represented as:

[0101]

[0102] Where T represents the number of past reference periods, λ is the response sensitivity, and w j δ represents the weight of the j-th period. t-jThe state deviation over time period tj;

[0103] D3 includes generating corresponding alarm prompts based on the results of deviation analysis, distinguishing the degree of abnormality, and triggering alarm information of different levels.

[0104] In an optional implementation, the status verification mechanism in step S4 can also be implemented by introducing a multi-source comparison verification method. That is, when comparing the device operation data and the virtual model output data, redundant monitoring data (such as current, voltage and temperature) from different acquisition channels are combined at the same time to compare the stability of the differences, avoid false deviation results caused by single channel error, and thus improve the reliability of deviation calculation.

[0105] In another optional implementation, the status verification mechanism in step S4 can also be implemented by introducing a hierarchical alarm method. That is, when generating alarm information, in addition to classifying the real-time deviation by threshold, the rate of change of the historical deviation trend is also combined to trigger a warning level prompt for the situation where the trend deteriorates but has not yet exceeded the threshold, so that maintenance personnel can take intervention measures in advance before the potential fault amplifies.

[0106] In this embodiment of the application, step S5 utilizes remote control to issue control commands based on the verification results and updates the virtual model state based on feedback information, including:

[0107] E1: Configure control commands, establish a command template library, set conditional triggers, match control commands according to fault type, and perform control risk assessment;

[0108] E2: Establishes a secure channel through communication management, performs signaling compression processing, and issues acknowledgments to complete the distribution of control commands;

[0109] E3: Perform feedback processing, verify the status, update the virtual model based on the control results, and archive the control behavior and feedback results in logs.

[0110] It should be noted that by configuring control commands and establishing a command template library, corresponding control operations can be quickly matched under different fault types. Combined with a control risk assessment mechanism, potential risks are quantified, and when the risk value exceeds a threshold, a secondary alarm or manual approval process is triggered, thereby reducing the impact of misoperation. A secure communication channel is established through communication management, and control commands are compressed and acknowledged, ensuring the security and reliability of control command transmission during issuance. In the feedback processing stage, status verification ensures the validity of control execution results, and the verification results are used to drive the self-updating of the virtual model, maintaining consistency with the actual operating state. Simultaneously, control behavior and feedback results are logged and archived, providing complete data support for subsequent anomaly tracing, operational analysis, and strategy optimization, thus achieving standardized, traceable, and dynamically adjustable remote control capabilities.

[0111] Specifically, E1 includes configuring instructions by matching corresponding instructions based on different types of operational anomalies using a pre-established control instruction template library, assessing the risk level, and setting conditional triggering mechanisms; represented as:

[0112] R c =α·P f +β·|ΔC|

[0113] Where ΔC is the expected difference in impact of control operations on core indicators, α is the safety factor, β is the correction factor, and R... c Indicates the control risk value;

[0114] R c The larger the value, the higher the risk control level, triggering a secondary alarm or manual approval process.

[0115] E2 includes establishing a secure communication channel through communication management during the control command issuance process, compressing the command signaling, and obtaining a confirmation receipt after transmission to ensure the reliability of command transmission.

[0116] E3 includes performing status verification on the feedback results after executing control, updating the operating status of the digital twin model based on the verification results, and logging and archiving the control process and feedback information.

[0117] In an optional implementation, remote control in step S5 can also be achieved by introducing a hierarchical instruction approval mechanism. That is, based on the establishment of an instruction template library, control instructions are divided into three levels: ordinary, important, and critical, according to the fault type and risk assessment results. Instructions of different levels correspond to different approval and execution processes. Among them, critical level instructions can only be issued after manual confirmation or secondary verification when triggered, so as to ensure control security in high-risk scenarios.

[0118] In another optional implementation, remote control in step S5 can also be achieved by combining redundant communication links. That is, in the communication management process, not only is a single secure channel established, but also two communication links, a primary one and a backup one, are constructed at the same time. When signal attenuation, delay or packet loss is detected in the primary channel, the system automatically switches to the backup channel, thereby ensuring the continuity and reliability of the control command issuance and feedback transmission process.

[0119] In summary, this invention proposes a digital twin method for charging pile operation management, forming a closed-loop process consisting of data acquisition, model parameter management, real-time synchronization, status verification, and remote control. By dynamically maintaining and adjusting model parameters during operation, the consistency between the virtual model and the actual equipment status is ensured. Real-time synchronization and deviation verification enable quantitative comparison and trend analysis of the operating status, allowing for identification and tiered alarms before anomalies fully manifest. The remote control mechanism, combined with command configuration, secure communication, and a feedback self-updating mechanism, ensures the reliability and traceability of control operations. Overall, this invention enables accurate monitoring, anomaly warning, and control of charging pile operation status under complex conditions, improving the safety and stability of the system.

[0120] Example 3, referring to Figure 2 As an embodiment of the present invention, a remote verification method for charging piles based on digital twins is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0121] Three charging stations were set up and connected to corresponding sensors for data collection; traditional monitoring methods were also configured, including regular manual inspections and data recording.

[0122] The configuration system must ensure that the digital twin modeling module, real-time synchronization module, deviation detection and remote control module are working properly.

[0123] Data acquisition and fault simulation: Collect equipment operating status data every 5 minutes, including parameters such as current, voltage, and temperature; simulate voltage abnormality, overheating, and communication failure of the charging pile at the 20th, 40th, and 55th minutes, respectively.

[0124] Operation and verification: Compare the responses of the two methods when an anomaly is detected. The traditional method relies on manual inspection, while the patented system automatically triggers fault warnings, calculates fault risks, and performs remote control (such as power outages, restarts, etc.).

[0125] Data logging: Record the time when each method detects a fault, as well as the changes in abnormal deviations.

[0126] Record fault handling time: the time difference between the discovery of the fault and its repair.

[0127] The differences between traditional methods and the system of this invention in terms of deviation detection response speed, fault prediction accuracy, and repair time were compared, and various performance indicators were calculated and relevant data charts were drawn, such as... Figure 2 As shown.

[0128] Example 4 illustrates a schematic scheme for a remote verification method for charging piles based on digital twins. It should be noted that the technical solution of this system for remote verification of charging piles based on digital twins is based on the same concept as the aforementioned method for remote verification of charging piles based on digital twins. Details not described in detail in the technical solution of the system for remote verification of charging piles based on digital twins in this embodiment can be found in the description of the aforementioned method for remote verification of charging piles based on digital twins.

[0129] Reference Figure 3 This embodiment also provides a remote verification system for charging piles based on digital twins, including: a data acquisition module, a digital twin modeling module, a real-time synchronization module, a status verification module, and a remote control module;

[0130] The data acquisition module acquires various operating parameters and status information of the charging pile during its operation, forming a unified data input.

[0131] Specifically, it includes a sensor access unit, a protocol parsing unit, and a data caching unit; the sensor access unit is used to connect with the sensor to obtain the sensor's detection information; the protocol parsing unit is used to support the communication protocol with the charging pile and upload data packets; the data caching unit is used to preprocess and initially cache the collected data;

[0132] The sensor access unit includes a multi-protocol interface processor, a device binding processor, and a sampling frequency adaptive processor. The multi-protocol interface processor enables compatible access to different types of sensors. The device binding processor automatically identifies and binds sensors to designated charging pile devices. The sampling frequency adaptive processor automatically adjusts the sampling frequency based on data fluctuations.

[0133] The protocol parsing unit includes a protocol template database, a data decoding processor, and an exception handling processor. The protocol template database stores template data for various mainstream charging pile communication protocols. The data decoding processor performs structured decoding on the communication data. The exception handling processor reconstructs the data or initiates a retransmission when receiving an exception packet.

[0134] The data caching unit includes an edge buffer processor, a data preprocessor, and a data tag processor; the edge buffer processor is used to implement short-cycle data caching locally; the data preprocessor is used to filter, unify units, and remove anomalies from the data; and the data tag processor is used to attach tag information to each group of data.

[0135] The digital twin modeling module performs digital twin modeling, constructs a virtual model corresponding to the actual charging pile operation mechanism, and compares the virtual and real states.

[0136] Specifically, it includes a physical modeling unit, a logical modeling unit, and a model parameter management unit; the physical modeling unit is used to simulate the hardware operation logic of the charging pile; the logical modeling unit is used to simulate the protocol response and control process at the software level; and the model parameter management unit is used to dynamically maintain the model parameters.

[0137] The physical modeling unit includes a hardware behavior simulator, a power consumption characteristic simulator, and a state machine builder. The hardware behavior simulator is used to recreate the electrical information operation curve of the hardware. The power consumption characteristic simulator is used to fit the dynamic changes of the device's power consumption during the charging stage. The state machine builder is used to establish the multi-state transition logic diagram of the real hardware.

[0138] The logic modeling unit includes a protocol behavior simulator, an event-driven converter, and a model test processor. The protocol behavior simulator is used to reproduce the response logic of control commands in the charging process. The event-driven converter drives the model to enter different states according to the input data. The model test processor is used for virtual simulation and playback comparison with actual data.

[0139] The model parameter management unit includes a parameter version controller, a model optimization processor, and a credibility assessment processor. The parameter version controller records the time and content of each model parameter adjustment. The model optimization processor corrects parameter weights by combining historical comparisons of errors. The credibility assessment processor evaluates the credibility level of the model's current performance.

[0140] The real-time synchronization module matches and aligns the collected data with the virtual model in real time.

[0141] Specifically, it includes a time alignment unit, a data mapping unit, and a dynamic synchronization unit; the event alignment unit is used to unify data timestamps; the data mapping unit is used to map and convert collected data to the acute fields of model variables; and the dynamic synchronization unit is used to periodically trigger model state updates and data input.

[0142] The time alignment unit includes an NTR synchronization processor and a time drift detector; the NTR synchronization processor is used to synchronize the system time of the device and the server; the time drift detector is used to detect and correct clock drift in the device.

[0143] The data mapping unit includes a field adaptation processor, a unit conversion processor, and a perception mapping processor; the field adaptation processor is used to match different device fields to model parameters; the unit conversion processor is used to adaptively convert the units of the data; and the perception mapping processor dynamically adjusts the data mapping strategy according to the charging stage.

[0144] The dynamic synchronization unit includes a model trigger, a lookup and update processor, and a synchronization error detector; the model trigger is used to periodically push data and trigger model refresh; the lookup and update processor is used to synchronize only changed fields; and the synchronization error detector is used to detect synchronization failures.

[0145] The status verification module uses a status verification mechanism to calculate the deviation and identify abnormal situations based on the comparison results of virtual and real status.

[0146] Specifically, it includes a deviation detection unit, a fault prediction unit, and an alarm generation unit; the deviation detection unit is used to calculate the current state deviation; the fault prediction unit identifies potential faults based on the deviation trend; and the alarm generation unit triggers graded alarms based on the verification results.

[0147] The deviation detection unit includes a real-time deviation calculator, a dynamic threshold adjuster, and a data alignment processor. The real-time deviation calculator is used to compare the deviation values ​​between entity and model data. The dynamic threshold adjuster automatically adjusts the deviation judgment threshold according to different working modes. The data alignment processor is used to ensure the consistency of the compared data in the time dimension.

[0148] The fault prediction unit includes a trend recognition processor, a pattern library comparator, and a state prediction processor. The trend recognition processor is used to identify gradually worsening fault trends by combining multi-time period deviation data. The pattern library comparator is used to compare with a built-in library of known fault behavior patterns. The state prediction processor is used to predict whether the device will fail in the future.

[0149] The alarm generation unit includes a level determination processor, a multi-channel push processor, and a handling suggestion generator; the level determination processor is used to classify anomalies into different levels; the multi-channel push processor is used to support alarm notifications in various ways; and the handling suggestion generator automatically recommends handling solutions based on the fault type.

[0150] The remote control module uses remote control to issue control commands based on the verification results and updates the virtual model status based on feedback information.

[0151] Specifically, it includes an instruction configuration unit, a communication management unit, and a closed-loop feedback unit; the instruction configuration unit is used to define all control instructions; the communication management unit is used to ensure that instructions are sent to the terminal through a secure channel; and the closed-loop feedback unit is used to monitor control results and update the state of the digital twin model.

[0152] The instruction configuration unit includes an instruction template library, condition triggers, and a control risk assessor. The instruction template library stores all control instruction information. The condition triggers automatically match control instructions based on the fault type. The control risk assessor analyzes the impact of the current control operation on the equipment.

[0153] The communication management unit includes a secure channel builder, a signaling compression processor, and a receipt confirmation processor; the secure channel builder is used to support encrypted transmission; the signaling compression processor is used to improve control response speed under low bandwidth conditions; and the receipt confirmation processor is used to ensure that the response after the command is issued is accepted and executed.

[0154] The closed-loop feedback unit includes a state verification processor, a model self-uploader, and a log archive processor; the state verification processor is used to determine whether the control operation is effective; the model self-uploader adjusts the current state of the virtual model according to the control result; and the log archive processor is used to form a closed-loop archive of each control action and feedback result.

[0155] This embodiment also provides an electronic device suitable for remote verification of charging piles based on digital twins, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the remote verification method for charging piles based on digital twins as proposed in the above embodiment.

[0156] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the remote verification method for charging piles based on digital twins as proposed in the above embodiments.

[0157] The storage medium proposed in this embodiment and the remote verification method for charging piles based on digital twins proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0158] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A remote verification method for charging piles based on digital twins, characterized in that, include: By collecting data, we can obtain various operating parameters and status information of the charging piles during operation and form a unified data input. Digital twin modeling is performed to construct a virtual model corresponding to the actual operation mechanism of the charging pile, and the virtual and real states are compared. Based on real-time synchronization, the collected data is matched with the virtual model and aligned with time. A status verification mechanism is adopted to calculate the deviation and identify abnormal situations based on the comparison results of virtual and real status. Using remote control, control commands are issued based on the verification results, and the virtual model status is updated based on feedback information.

2. The remote verification method for charging piles based on digital twins as described in claim 1, characterized in that, The process involves acquiring various operating parameters and status information of the charging pile during operation through data collection, forming a unified data input, including: Acquire detection information of charging piles during operation and complete the collection of data from different sources; It supports the communication protocol of charging piles, parses and processes operational data, and ensures the consistency of data format. Data caching is used to preprocess and initially cache the collected data.

3. The remote verification method for charging piles based on digital twins as described in claim 2, characterized in that, The process of performing digital twin modeling, constructing a virtual model corresponding to the actual operating mechanism of charging piles, includes: Establish physical-level modeling to simulate the hardware operation logic of charging piles; Build a logical-level model to reproduce the protocol response and control flow at the software level; Based on model parameter management, the model parameters are dynamically maintained and adjusted.

4. The remote verification method for charging piles based on digital twins as described in claim 3, characterized in that, The step of matching and aligning the collected data with the virtual model in real time includes: Time-align the collected data and unify the data timestamps; The collected data is mapped and transformed to correspond with the variables of the virtual model; Dynamic synchronization is used to periodically trigger state updates of the virtual model and input data.

5. The remote verification method for charging piles based on digital twins as described in claim 4, characterized in that, The state verification mechanism, which calculates the deviation and identifies abnormal situations based on the comparison results of virtual and real states, includes: By comparing the equipment operation data with the output data of the virtual model, the state difference at the current moment is calculated to obtain the deviation result; By combining deviation data from multiple time periods, comparing trends, identifying operational status, and comparing it with known fault modes; Alarm information is generated based on the verification results, and corresponding alarm levels are triggered according to different degrees of anomaly.

6. The remote verification method for charging piles based on digital twins as described in claim 5, characterized in that, The method of using remote control to issue control commands based on verification results and update the virtual model state based on feedback information includes: Configure control commands, establish a command template library, set conditional triggers, match control commands according to fault type, and conduct control risk assessment; A secure channel is established through communication management, signaling compression is performed, and acknowledgment is issued to complete the distribution of control commands; Feedback processing is performed, the virtual model is self-updated based on the control results through status verification, and the control behavior and feedback results are logged and archived.

7. The remote verification method for charging piles based on digital twins as described in claim 6, characterized in that, The configuration of control commands, the establishment of a command template library, the setting of conditional triggers, the matching of control commands according to fault type, and the performance of control risk assessment are represented as follows: R c =α·P f +β·|ΔC| Where ΔC is the expected difference in impact of control operations on core indicators, α is the safety factor, β is the correction factor, and R... c This indicates the control risk value.

8. A remote verification system for charging piles based on digital twins, employing the remote verification method for charging piles based on digital twins as described in any one of claims 1-7, characterized in that, include: The data acquisition module acquires various operating parameters and status information of the charging pile during its operation, forming a unified data input. The digital twin modeling module performs digital twin modeling, constructs a virtual model corresponding to the actual charging pile operation mechanism, and compares the virtual and real states. The real-time synchronization module matches and aligns the collected data with the virtual model in real time. The status verification module uses a status verification mechanism to calculate the deviation and identify abnormal situations based on the comparison results of virtual and real status. The remote control module uses remote control to issue control commands based on the verification results and updates the virtual model status based on feedback information.

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 remote verification method for charging piles based on digital twins as described in 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 remote verification method for charging piles based on digital twins as described in any one of claims 1 to 7.

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