Electric vehicle interconnection and intercommunication shared charging operation method, device and storage medium

Abstract

The application relates to the field of new energy automobile power supply, and provides an electric automobile interconnection and intercommunication shared charging operation method, equipment and a storage medium, the method comprising the following steps: realizing heterogeneous system interconnection of a multi-source charging platform and a supervision platform through a standardized interface protocol, and establishing a cross-platform data channel; constructing a dynamic user authentication system based on the cross-platform data channel, adopting a multi-modal identity authentication mode to complete electric automobile user registration and permission distribution to obtain a user permission level, and feeding back an authentication result to the supervision platform; according to the user permission level, performing intelligent charging pile matching based on a real-time multi-dimensional charging resource graph, and generating a dynamic service instruction containing a navigation path and charging parameters; in a charging service execution process, creating a two-way supervision data flow through the cross-platform data channel, performing real-time whole life cycle supervision on a charging process, and dynamically adjusting a service strategy based on a feedback instruction of the supervision platform. The technical scheme of the application can cooperate with electric automobile charging.

Description

Electric vehicle interconnection and shared charging operation methods, equipment and storage media Technical Field

[0001] This application relates to the field of power supply for new energy vehicles, and in particular to a method, equipment and storage medium for operating interconnected and shared charging of electric vehicles. Background Technology

[0002] With the accelerated global energy structure transformation, electric vehicles, as an important carrier of clean energy applications, have made the intelligent and networked operation of their charging infrastructure a core requirement for industry development. Currently, charging operators mostly use platform-based management systems to achieve charging pile access and user services. However, due to differences in technical standards among different platforms, the lack of diverse user authentication methods, and insufficient dynamic scheduling capabilities for charging resources, cross-platform collaboration efficiency is low, making it difficult to meet the real-time charging needs of a large number of electric vehicle users.

[0003] In existing technologies, charging operation platforms typically employ a single-platform closed architecture, static user permission management, offline resource matching strategies, and one-way regulatory data reporting. However, these existing technologies suffer from numerous significant drawbacks, including poor multi-platform interoperability, a disconnect between user authentication and permission management, insufficient dynamism in resource matching, and delayed regulatory feedback. Summary of the Invention

[0004] This application provides a method, device, and storage medium for operating interconnected and shared charging of electric vehicles, which can achieve coordinated optimization of stable cross-platform interconnection, intelligent scheduling, and closed-loop supervision during electric vehicle charging.

[0005] On the one hand, this application provides a method for operating interconnected and shared charging of electric vehicles, the method comprising:

[0006] By using standardized interface protocols, heterogeneous systems such as multi-source charging platforms and regulatory platforms can be interconnected, establishing cross-platform data channels.

[0007] A dynamic user authentication system is built based on the cross-platform data channel. Multimodal authentication is used to complete the registration and permission allocation of electric vehicle users to obtain user permission levels, and the authentication results are fed back to the regulatory platform.

[0008] Based on the user's permission level, intelligent charging pile matching is performed based on a real-time multi-dimensional charging resource map to generate dynamic service instructions containing navigation paths and charging parameters.

[0009] During the execution of the charging service, a two-way regulatory data stream is created through the cross-platform data channel to perform real-time full lifecycle monitoring of the charging process, and the service strategy is dynamically adjusted based on the feedback instructions from the regulatory platform.

[0010] On the other hand, this application provides an electric vehicle interconnection and shared charging operation device, the device comprising:

[0011] The channel establishment module is used to interconnect heterogeneous systems between the multi-source charging platform and the regulatory platform through a standardized interface protocol, and to establish a cross-platform data channel.

[0012] The authentication module is used to build a dynamic user authentication system based on the cross-platform data channel, use multimodal authentication to complete electric vehicle user registration and permission allocation to obtain user permission levels, and feed back the authentication results to the regulatory platform.

[0013] The instruction generation module is used to perform intelligent charging pile matching based on the real-time multi-dimensional charging resource map according to the user's permission level, and generate dynamic service instructions containing navigation paths and charging parameters.

[0014] The monitoring module is used to create a two-way monitoring data stream through the cross-platform data channel during the charging service execution process, perform real-time monitoring of the entire lifecycle of the charging process, and dynamically adjust the service strategy based on the feedback instructions from the monitoring platform.

[0015] Thirdly, this application provides an electronic device, the device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the technical solution of the above-described electric vehicle interconnection and shared charging operation method.

[0016] Fourthly, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the technical solution of the above-described electric vehicle interconnection and shared charging operation method.

[0017] As can be seen from the technical solution provided in this application, on the one hand, by establishing a cross-platform data channel through a standardized interface protocol and verifying and optimizing the interface version compatibility and transmission stability, the protocol differences between different charging platforms and regulatory systems are effectively solved, ensuring efficient interoperability of multi-source data. Furthermore, by implementing multimodal identity verification based on the cross-platform data channel and dynamically associating user registration with permission allocation, not only is real-time adaptation of electric vehicle user permission levels achieved, but the platform's security protection capabilities are also enhanced. On the other hand, by integrating dynamic parameters such as geographical location, charging pile status, and grid load to construct a charging resource map, and combining this with electric vehicle user permissions to generate collaborative optimization instructions containing navigation paths and charging parameters, the accuracy of resource matching is significantly improved. Thirdly, by uploading charging process data in real time through bidirectional regulatory data streams and receiving regulatory instruction feedback, dynamic adjustments to charging strategies and rapid responses to abnormal events can be achieved, ensuring the compliance and security of charging services. In summary, the technical solution of this application enables collaborative optimization of stable cross-platform interconnection, intelligent scheduling, and closed-loop supervision during electric vehicle charging. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 is a flowchart of the electric vehicle interconnection and shared charging operation method provided in an embodiment of this application;

[0020] Figure 2 is a structural schematic diagram of the electric vehicle interconnection and shared charging operation device provided in an embodiment of this application;

[0021] Figure 3 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] In this specification, adjectives such as "first" and "second" are used only to distinguish one element or action from another, without necessarily requiring or implying any actual such relationship or order. Where circumstances permit, reference to an element or component or step (etc.) should not be construed as being limited to only one of the elements, components, or steps, but may be one or more of the elements, components, or steps, etc.

[0024] For ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn to actual scale.

[0025] With the accelerated global energy structure transformation, electric vehicles, as an important carrier of clean energy applications, have made the intelligent and networked operation of their charging infrastructure a core requirement for industry development. Currently, charging operators mostly use platform-based management systems to achieve charging pile access and user services. However, due to differences in technical standards among different platforms, the lack of diverse user authentication methods, and insufficient dynamic scheduling capabilities for charging resources, cross-platform collaboration efficiency is low, making it difficult to meet the real-time charging needs of a large number of electric vehicle users.

[0026] In existing technologies, charging operation platforms typically adopt the following solutions: 1) Single-platform closed architecture: Each charging operator builds and operates its own platform, with inconsistent interface protocols, leading to compatibility issues in cross-platform data interaction; 2) Static user permission management: User authentication relies on traditional account and password systems, lacking dynamic permission adjustment mechanisms, making it difficult to prevent identity theft; 3) Offline resource matching strategy: Charging pile recommendations rely on static database queries, failing to integrate multi-dimensional dynamic parameters such as traffic conditions and grid load in real time, resulting in recommendations deviating from actual user needs; 4) One-way regulatory data reporting: Regulatory data upload and strategy execution are separated, making it impossible to achieve closed-loop supervision of the entire charging process lifecycle.

[0027] However, the aforementioned existing technologies have the following significant drawbacks: 1) Poor multi-platform interoperability: When heterogeneous systems are connected, data transmission is unstable due to protocol differences, affecting the continuity of cross-platform services; 2) Disconnection between user authentication and permission management: Static authentication methods cannot dynamically associate parameters such as user credit and vehicle status, posing security risks; 3) Insufficient dynamic resource matching: Offline generated charging instructions are difficult to respond to real-time environmental parameters such as changes in road conditions and fluctuations in electricity prices in a timely manner; 4) Delayed regulatory feedback: The one-way data reporting mechanism leads to a lag in the adjustment of regulatory strategies, making it impossible to effectively prevent charging safety accidents.

[0028] To address the aforementioned problems in the prior art, this application proposes a method for operating interconnected and shared charging of electric vehicles, the flowchart of which is shown in Figure 1. The method mainly includes steps S101 to S104, detailed below:

[0029] Step S101: Achieve heterogeneous system interconnection between the multi-source charging platform and the regulatory platform through standardized interface protocols, and establish a cross-platform data channel.

[0030] On the one hand, different charging platforms and regulatory platforms (e.g., public security, traffic management, and power departments) employ heterogeneous technical architectures, and standardized protocols are the foundation for eliminating semantic ambiguity and achieving data interoperability. In other words, without a unified protocol, the failure rate of cross-platform command parsing will significantly increase. On the other hand, when charging platforms are upgraded, the interfaces of new and old versions may have added or removed fields or semantic changes, and version verification can effectively prevent data parsing errors caused by version differences. Therefore, in order to solve the compatibility problems caused by platform and version iterations and thus achieve industry interconnection, this application can achieve heterogeneous system interconnection between multi-source charging platforms and regulatory platforms through standardized interface protocols, establishing a cross-platform data channel. It should be noted that since general transmission protocols (e.g., TCP) cannot meet the customized stability requirements of charging scenarios, and the log function needs to be deeply coupled with the protocol parsing layer, it cannot be completely replaced by external tools. Therefore, in the process of establishing a cross-platform data channel, the above embodiments can optimize transmission stability, that is, ensure transmission reliability through mechanisms such as heartbeat detection, data retransmission, and / or flow control, and generate system docking logs for subsequent fault tracing and compliance auditing.

[0031] As one embodiment of this application, the heterogeneous system interconnection between the multi-source charging platform and the regulatory platform is achieved through a standardized interface protocol, and the establishment of a cross-platform data channel can be achieved through steps S1011 to S1013, as detailed below:

[0032] Step S1011: When performing protocol conversion tests on each access platform, use deep packet inspection technology to identify differences in interface specifications.

[0033] The access platforms mentioned here include the multi-source charging platform and the monitoring platform described in the aforementioned embodiments. Specifically, when performing protocol conversion tests on each access platform, deep packet inspection technology is used to identify differences in interface specifications, which can be achieved through the following steps S10111 to S10115:

[0034] Step S10111: Initialize the simulation test environment for each access platform.

[0035] Specifically, this includes setting up a simulated test environment that includes the target charging platform and the regulatory platform, deploying protocol probe nodes, loading the standard protocol template library, establishing a standard field mapping table, and finally configuring probe packet capture rules and setting trigger conditions, such as starting deep parsing when a non-standard field is detected.

[0036] Step S10112: Capture protocol interaction data.

[0037] The specific implementation of step S10112 can be as follows: send a standardized set of test instructions for charging start, stop and status query to each access platform, capture the platform response data stream with the probe, classify and store the original messages according to session ID, perform man-in-the-middle decryption on the encrypted messages, and obtain the plaintext payload.

[0038] Step S10113: Analyze the protocol structure of each access platform layer by layer.

[0039] The specific implementation of step S10113 can be as follows: extract the transport layer features of the message (including TCP port number, SSL fingerprint, etc.), identify the basic protocol type, then parse the application layer protocol structure, and finally output the structured parsing results such as field names, data types, and position offsets. It should be noted that for the HTTP protocol, the above parsing of the application layer protocol structure mainly includes disassembling the Header and Body, extracting the RESTful API path and parameters, etc. For the MQTT protocol, it mainly involves parsing the Topic level and Payload binary structure. For proprietary protocols, the protocol structure is parsed by reverse-engineering the magic number and length identifier in the message header.

[0040] Step S10114: Analyze the correlation of differences.

[0041] The specific implementation of step S10114 can be to compare the parsing results of the protocol structure of each access platform with the standard template field by field to generate a difference matrix. Then, semantic tracing is performed on the difference fields in the difference matrix, including tracing the original message interaction process, determining the business scenario of the difference fields (such as charging power control) and the dependency relationship between the parsed fields, etc.

[0042] Step S10115: Assess the impact of the difference.

[0043] Here, the assessment of the impact of differences mainly involves classifying the difference levels based on the business scenario. Then, a difference analysis report is generated, and recommended handling strategies (including conversion, ignoring, and alerting, etc.) are labeled. Finally, key difference points are input into the protocol conversion middleware configuration system to trigger the generation of adaptation rules. In the above embodiment, the difference levels mainly include blocking-level differences that cause core function failures (e.g., missing charging command codes), compatibility-level differences that require conversion logic (e.g., inconsistent time formats), and extension-level differences for platform-specific functions (e.g., extended fields for scheduled charging), etc.

[0044] Step S1012: Construct a protocol conversion middleware based on the analysis results of interface specification differences.

[0045] In this embodiment, the protocol conversion middleware mainly includes a business logic allocation layer and a data encapsulation layer. The business logic adaptation layer is used to convert between control instruction sets of different platforms, while the data encapsulation layer is used to unify data packet formats and encryption standards. As one embodiment of this application, constructing the business logic adaptation layer based on the analysis results of interface specification differences can be as follows: performing binary instruction set mapping conversion on charging pile control instructions, retaining the core opcode and rewriting the parameter bit structure; implementing AES-GCM encryption conversion on the electric vehicle user identity field to generate an encrypted data unit containing a version identifier; and dynamically loading the central bank's exchange rate API to obtain the real-time benchmark exchange rate for currency conversion during billing data conversion. The construction of the business logic adaptation layer based on the analysis results of interface specification differences can be achieved through the following steps S10121 to S10124:

[0046] Step S10121: Generate field mapping rules.

[0047] The process of generating field mapping rules specifically involves reading the field mapping relationships from the difference analysis report, defining a conversion function library including type conversion, semantic conversion, and structural conversion, and finally generating field mapping configuration files in formats such as XML and JSON.

[0048] Step S10122: Construct a dynamic packaging pipeline.

[0049] The construction of a dynamic encapsulation pipeline mainly includes two steps: designing the data packet assembly pipeline and implementing the atomicity of the conversion logic. The design of the data packet assembly pipeline mainly involves receiving raw platform data at the access layer, triggering field mapping rules, performing type / semantic / structural conversions at the conversion layer, assembling data packets according to standard formats at the encapsulation layer, and so on. Implementing the atomicity of the conversion logic mainly involves encapsulating each field conversion into an independent microservice and establishing a conversion dependency graph. For example, the conversion of station_id should take precedence over charge_power.

[0050] Step S10123: Protect the data packet.

[0051] Here, protecting data packets mainly involves encrypting and protecting their integrity. Specifically, this includes: based on the encryption differences identified during the protocol testing phase, if the platform uses AES-CBC, the encapsulation layer is uniformly upgraded to AES-GCM, while HMAC-SHA256 signatures are added to unencrypted fields; intercepting the platform's key exchange process at the encapsulation layer and injecting the standard negotiation protocol; embedding key version identifiers in the data header, etc.

[0052] Step S10124: Verify and optimize the data flow.

[0053] The verification and optimization of the data stream mainly includes: injecting test data streams to verify the correctness of the encapsulation, then performing performance tuning by enabling cache conversion for high-frequency fields (such as charging status) and using zero-copy technology to reduce memory copying overhead, and finally outputting the final encapsulation layer configuration parameters and synchronizing them to the protocol conversion middleware.

[0054] Step S1013: Establish a dual-channel redundant transmission link based on the protocol conversion middleware.

[0055] In this embodiment, the dual-channel redundant transmission link mainly refers to the main channel and the backup channel. The main channel uses the WebSocket protocol to maintain a long connection and transmit real-time control commands, while the backup channel is configured with the MQTT protocol to retransmit offline data and ensure business continuity.

[0056] As can be seen from steps S1011 to S1013 of the above embodiments, on the one hand, by constructing a protocol conversion middleware (data encapsulation layer + business logic adaptation layer) and adopting a dual-channel redundant transmission mechanism (WebSocket + MQTT), the communication interruption problem caused by the differences in protocols across multiple platforms is effectively solved; on the other hand, by unifying the data format and encryption standards of different platforms through the data encapsulation layer, parsing errors are avoided, and by realizing the accurate mapping of the instruction set through the business logic adaptation layer, the reliable execution of cross-platform control instructions is ensured. The dual-channel design automatically switches to the backup channel when the main channel is abnormal, ensuring the real-time performance and continuity of charging control instructions and preventing charging interruptions or safety accidents caused by network fluctuations.

[0057] Step S102: Based on the established cross-platform data channel, construct a dynamic user authentication system, use multimodal authentication to complete electric vehicle user registration and permission allocation to obtain user permission levels, and feed the authentication results back to the regulatory platform.

[0058] Considering that a single verification method cannot meet the identity verification requirements of high-security scenarios (e.g., high-voltage operation of charging piles), and that static permission allocation cannot adapt to real-time risk changes, and that the regulatory platform also needs to monitor the legitimacy of user identities and the scope of their operating permissions in real time, a dynamic user authentication system can be constructed based on the established cross-platform data channel after establishing a cross-platform data channel. This system uses multimodal authentication to complete electric vehicle user registration and permission allocation to obtain user permission levels, and the authentication results are fed back to the regulatory platform. Specifically, as an embodiment of this application, the construction of a dynamic user authentication system based on the established cross-platform data channel, and the use of multimodal authentication to complete electric vehicle user registration and permission allocation to obtain user permission levels, can be achieved through steps S1021 to S1023, as detailed below:

[0059] Step S1021: Generate a biometric template through liveness detection and document OCR recognition, and verify it online with the public security database.

[0060] Step S1022: Dynamically generate user permission tokens based on the verification results from online verification.

[0061] Here, the user permission token includes: a charging power limit based on the electric vehicle user's credit rating, a whitelist of available time periods associated with grid load characteristics, and payment method permissions adapted to vehicle battery parameters, etc. The electric vehicle user's credit rating can be calculated by extracting the on-time performance rate from the user's historical charging records. Obtain credit ratings from third-party credit reporting platforms. Based on the on-time performance rate Credit ratings provided by third-party credit reporting platforms Calculate the dynamic power limit ,in, Standard power supply for the regional power grid.

[0062] Step S1023: Bind the user permission token to the cross-platform data channel. When a cross-platform operation is detected, the token parameters are parsed through the business logic adaptation layer to trigger the distributed authentication service for real-time authentication.

[0063] Specifically, binding the user permission token to the cross-platform data channel, and when a cross-platform operation is detected, parsing the token parameters through the business logic adaptation layer to trigger the distributed authentication service for real-time authentication can be achieved through the following steps S10231 to S10233:

[0064] Step S10231: Bind the user permission token to the cross-platform data channel.

[0065] Specifically, this could involve: mapping and registering the URI of the user permission token across the platform's data channel ID; adding a token fingerprint to the channel metadata to establish a binding relationship table; then injecting the token metadata during the WebSocket long connection handshake phase; implementing channel cascading control by configuring the subscription relationship between the MQTT topic tree and the token permissions; and finally, starting a heartbeat monitoring mechanism, such as verifying the token's validity period and channel activity status every 60 seconds, automatically renewing soon-to-expire user permission tokens, and so on.

[0066] Step S10232: Detect and analyze cross-platform operation.

[0067] Specifically, the detection and parsing of cross-platform operations mainly includes identifying request traffic characteristics, extracting user permission token parameters, and analyzing the context. Identifying request traffic characteristics is mainly achieved by capturing cross-platform API calls in the data channel and parsing the X-Platform-Transfer identifier in the HTTP Header. Extracting user permission token parameters is mainly achieved by disassembling the encrypted token through the business logic adaptation layer, such as verifying the validity of the digital signature, the decryption power limit, the time period whitelist, and other core parameters, as well as parsing the blockchain hash to verify data integrity. As for analyzing the context, it can be achieved by matching the current operation scenario with the spatiotemporal constraints of the token permissions (e.g., whether the current time is within the available time period) and detecting the consistency between the device fingerprint and the initial authentication device.

[0068] Step S10233: Perform distributed real-time authentication.

[0069] When performing distributed real-time authentication, the authentication service cluster is first triggered, then multi-dimensional authentication is performed, and finally, dynamic policy instructions are returned, including full permission pass, partial permission pass, or authentication failure, etc. Among them, the authentication service cluster can be triggered by selecting the nearest authentication node based on the token URI hash value and sending a lightweight authentication request packet. The multi-dimensional authentication includes power authentication by comparing the requested charging power with the token's upper limit, time period authentication by verifying whether the operation time is within the available whitelist, payment authentication by verifying whether the payment method is in the permitted list, and device authentication by confirming the association between the operation terminal and the authentication terminal, etc.

[0070] Step S103: Based on the user's permission level, perform intelligent charging pile matching based on the real-time multi-dimensional charging resource map, and generate dynamic service instructions containing navigation paths and charging parameters.

[0071] Considering that traditional GPS distance-based recommendation algorithms ignore key parameters such as electricity price and road conditions, leading to user decision-making bias, and that multi-dimensional data such as charging pile status (e.g., idle or occupied), electricity price, and traffic conditions dynamically change, they need to be aggregated into a spatiotemporally correlated map to support accurate recommendations; on the other hand, user permissions (e.g., maximum charging power) directly affect the range of available charging piles. If permission constraints are ignored, charging piles that users cannot use may be recommended, and static instructions cannot cope with dynamic environmental changes. Therefore, intelligent charging pile matching can be performed based on a real-time multi-dimensional charging resource map according to the user's permission level, generating dynamic service instructions containing navigation paths and charging parameters. As an embodiment of this application, intelligent charging pile matching based on a real-time multi-dimensional charging resource map according to the user's permission level, generating dynamic service instructions containing navigation paths and charging parameters, can be implemented through steps S1031 to S1034, as detailed below:

[0072] Step S1031: Aggregate charging pile status data from multiple platforms in real time to construct a real-time multi-dimensional charging resource map that includes geographical location, interface type, and real-time electricity price.

[0073] Specifically, the charging pile data stream, including geographical location data, status parameters, and device attributes, can be retrieved and standardized in real time through a cross-platform data channel. Then, a spatiotemporal data fusion model can be constructed by encoding the charging pile location into grid blocks and establishing an R-tree index to accelerate regional queries. Finally, real-time layer refresh can be triggered by status change data (e.g., a charging pile changing from occupied to idle) to establish an incremental update pipeline. Abnormal data can be processed by retaining location information for offline pile data and taking the optimal status value from data from multiple platforms at the same location.

[0074] Step S1032: Dynamically calculate the charging demand urgency index based on the user's current vehicle SOC value, battery capacity, and destination information.

[0075] The vehicle's SOC value, or State of Charge (SOC) of an electric vehicle, represents the current percentage of battery charge remaining. Specifically, based on the user's current vehicle SOC value, battery capacity, and destination information, the dynamic calculation of the charging demand urgency index can be achieved as follows: The vehicle's SOC value, battery health, and rated battery capacity are collected in real-time via the onboard OBD interface. When the user inputs destination coordinates and a preset arrival time, vehicle status parameters are obtained. A basic urgency level is generated by estimating the remaining driving range and calculating the required travel distance. Finally, the urgency level weight is dynamically adjusted by modifying time sensitivity and battery protection.

[0076] Step S1033: Based on the urgency index of charging demand and the real-time multi-dimensional charging resource map, a multi-objective optimization algorithm is used to generate a recommended sequence of charging stations.

[0077] In the embodiments of this application, the optimization objective in the multi-objective optimization algorithm includes a weighted evaluation of time cost, economic cost, and battery health deterioration.

[0078] Step S1034: Generate a dynamic navigation route using the path planning engine and calculate the optimal combination of charging parameters using the charging strategy optimizer.

[0079] In this embodiment of the application, the path planning engine's working method includes constructing a charging time estimation model and performing the following steps:

[0080] a) Establish a vehicle routing problem model with time windows and integrate the real-time reachability radius output by the charging time prediction model.

[0081] Specifically, the real-time reach radius The calculation method is as follows:

[0082] in, For charging efficiency, for Available charging power at all times For driving power consumption function, For real-time vehicle speed, This refers to the battery capacity.

[0083] b) Generate a dynamic road resistance function based on the real-time reachability radius, wherein the road resistance weight of the dynamic road resistance function is inversely proportional to the estimated charging time.

[0084] c) The ant colony optimization algorithm is used to find the optimal path, and its pheromone update rule is as follows: , ,

[0085] in, This is the pheromone evaporation coefficient, and its value is positively correlated with the time sensitivity output of the charging time prediction model. The pheromone baseline intensity (or pheromone total constant) represents the baseline value of the total amount of pheromone released by a single ant per unit path. For path The travel time is calculated in real time using a dynamic road resistance function. It is a small constant used to prevent division by zero errors.

[0086] As can be seen from the above embodiments, by integrating dynamic parameters such as charging pile status, traffic conditions, and power grid load, a spatiotemporally correlated resource heat map is constructed. Simultaneously executing navigation path planning and charging parameter calculation avoids the separation of path selection and charging strategy in traditional solutions. Furthermore, by dynamically updating service instructions based on changes in the resource map, it can respond to fluctuations in charging pile status and changes in electric vehicle user demand, thereby significantly improving the efficiency of charging resource matching, reducing the time and cost for electric vehicle users to find charging piles, and balancing the distribution of regional power grid load.

[0087] Step S104: During the charging service execution process, a two-way regulatory data stream is created through a cross-platform data channel to perform real-time full lifecycle monitoring of the charging process, and the service strategy is dynamically adjusted based on the feedback instructions from the regulatory platform.

[0088] On the one hand, unidirectional data flow cannot achieve bidirectional control, and the monitoring platform also needs to receive charging data (such as power and temperature) in real time and issue control instructions. On the other hand, the charging process needs to cover the entire process of startup preparation, charging execution, and abnormal termination to avoid blind spots in supervision. Staged supervision may also miss violations of startup parameters or abnormal termination. Therefore, after performing intelligent charging pile matching based on real-time multi-dimensional charging resource map according to user permission level and generating dynamic service instructions containing navigation path and charging parameters, a bidirectional monitoring data flow can be created through cross-platform data channel during the charging service execution process to perform real-time full lifecycle supervision of the charging process and dynamically adjust the service strategy based on the feedback instructions from the monitoring platform. Specifically, as an embodiment of this application, during the charging service execution process, the creation of a bidirectional monitoring data flow through cross-platform data channel to perform real-time full lifecycle supervision of the charging process and dynamically adjust the service strategy based on the feedback instructions from the monitoring platform can be achieved through steps S1041 to S1043, which are described in detail below:

[0089] Step S1041: When charging starts, send a filing data packet containing the electric vehicle user's identity hash value, charging pile code and plan parameters to the regulatory platform, and mark the data packet as the regulatory baseline.

[0090] Step S1042: Upload multi-dimensional monitoring data according to the preset sampling period during the charging process.

[0091] The aforementioned multi-dimensional regulatory data includes real-time charging curve data, user operation logs, and device status monitoring data, etc. Among them, real-time charging curve data is used to synchronize the charging pile code in the filing data package and associate the second-level sampling values ​​of voltage, current, and temperature. User operation logs record the timestamps and operation content of all interaction events and trigger policy conflict detection. Device status monitoring data is used to collect charging module temperature and insulation impedance parameters and perform deviation analysis with the regulatory baseline.

[0092] In the above embodiments, the implementation of policy conflict detection can be as follows: Establish a policy priority matrix, define the effectiveness levels of different regulatory instructions, wherein the effectiveness level is correlated with the deviation threshold of the regulatory baseline; detect the compatibility between user reservation parameters and new policy requirements, and when a conflict exists: a) for adjustable parameters, call the collaborative optimization instruction set to generate a compensation scheme and push confirmation to the electric vehicle user; b) for non-adjustable parameters, trigger the service termination process and refund the prepaid fees, while updating the regulatory baseline; record the policy change impact assessment report and feed the assessment results back to the real-time multi-dimensional charging resource map update module. The method of calling the collaborative optimization instruction set to generate a compensation scheme can be implemented as follows: calculate the expected cost difference under the old and new policies. The generated face value is (in, The system will compensate users with vouchers (based on a preset coefficient) and write the compensation data to the user's permission token; analyze available alternative charging stations, call the route planning engine to replan the navigation route, and estimate the time difference. ;when Greater than the preset duration At the same time, the service redemption interface with partner merchants will be activated, providing free access to the lounge during the charging waiting period, and the service redemption records will be synchronized to the regulatory data stream.

[0093] Step S1043: After receiving the policy update instruction issued by the regulatory platform, perform policy conflict detection and generate a service parameter adjustment queue.

[0094] The priority of the service parameter adjustment queue is dynamically determined by the deviation analysis results of the deviation analysis between the charging module temperature, insulation impedance parameters and the regulatory baseline.

[0095] To form a closed loop of "monitoring-isolation-repair-optimization," shorten fault recovery time, and avoid system-wide paralysis caused by a single charging device malfunction, this embodiment of the application can also implement multi-level anomaly monitoring throughout the entire charging cycle. Specifically, throughout the entire charging cycle, the charging pile controller collects equipment anomaly codes in real time, classifies them into communication faults, hardware faults, and safety alarms, and marks the classification results as anomaly event tags. Based on the anomaly event tags, a differentiated processing procedure is initiated: for communication faults, link self-checks and channel switching are enabled, and the switching record is synchronized to the cross-platform data channel; for hardware faults, the device is marked as unavailable and a maintenance work order is dispatched, while the real-time multi-dimensional charging resource map is updated; for safety alarms, emergency stop protection is triggered and the fire supervision platform is notified, generating an emergency event log and sending it back to the bidirectional supervision data stream; a user-side anomaly report is generated, including fault impact analysis and solution time estimation, and the report data is input into the module for optimizing dynamic service instructions. During the implementation of the above multi-level anomaly monitoring, when an abnormal charging gun temperature of the charging pile is detected, the following processing steps are executed: a) Real-time monitoring of the charging gun temperature. ,when ( When the preset warning threshold is reached: The output power is reduced at a preset rate gradient (e.g., 0.5℃ / s), and the cooling fan is activated simultaneously; the temperature data is synchronized to the monitoring data stream; b) If the temperature continues to rise after the power reduction gradient and ( (for safety thresholds): immediately cut off the power and lock the charging interface; generate hardware fault codes to update the real-time multi-dimensional charging resource map; c) trigger the early warning linkage mechanism: broadcast early warning signals to surrounding charging piles, trigger preventive testing of similar devices; associate early warning events with abnormal event tags and generate cross-platform security logs.

[0096] To transform dispersed electric vehicle charging loads into dispatchable resources, improve grid resilience, and reduce reliance on fossil fuel peak shaving, the method in the above embodiments further includes: receiving grid dispatch instructions from a bidirectional regulatory data stream in real time, parsing the demand response coefficient and time-of-use pricing incentive signals; calculating adjustable capacity margin based on the acquired vehicle battery status, and feeding the adjustable capacity margin back to the power adjustment module; the power adjustment module dynamically adjusting the charging pile output power curve based on the adjustable capacity margin; and writing the adjustment result of the charging pile output power curve into the bidirectional regulatory data stream and triggering a billing strategy update. In the above embodiments, if the demand response coefficient is denoted as... The current battery state of charge is The rated total capacity of the battery is Then the capacity margin can be adjusted. It can be represented as The power adjustment module dynamically adjusts the charging pile's output power curve based on the adjustable capacity margin, which can be: for high-priority vehicles, maintaining the lowest rate. (in, (Maximum allowable reverse power), synchronized to a real-time multi-dimensional charging resource map; for adjustable loads, based on the value of the time-of-use pricing incentive signal. Calculate V2G reverse power supply

[0097] ,like ,in, To trigger the V2G electricity price incentive threshold, The power release coefficient, This represents the maximum permissible reverse power.

[0098] It should be noted that, in order to address the risks of key leakage, misuse of privacy data, and operational repudiation in traditional solutions, and to provide a trusted foundation for multi-platform collaboration, the method of the above embodiments of this application further includes a data security enhancement system running through steps S101 to S104, executing the following collaborative protection steps S61 to S63: Step S61: During the cross-platform data channel data transmission process in step S1, a one-time encryption key is generated using quantum random numbers to perform end-to-end encryption on control commands; the encrypted data packet is appended with a digital signature and synchronized to the blockchain node in step S63; Step S62: During the authentication of electric vehicle users in the dynamic user authentication system, Paillier semi-homomorphic encryption is applied to the biometric template, and the encryption result of Paillier semi-homomorphic encryption serves as the basis for generating user permission tokens; Step S63: The timestamp hash of key operations is recorded in the bidirectional monitoring data stream and hashed to the blockchain to form an audit chain, and the encryption key rotation strategy in step S61 is dynamically updated based on the blockchain evidence storage result. Furthermore, the aforementioned data security enhancement system also includes: employing fully homomorphic encryption for dynamic service instructions regarding charging parameters, enabling third-party billing systems to execute the following in encrypted form: 1) Fee settlement, i.e., fee... ,in, Indicates the amount of electricity charged for electric vehicles Encryption, 1) Indicate the rate; 2) After the settlement result is decrypted, it is written into the two-way regulatory data stream; 3) The key rotation mechanism is linked with the power grid dispatching instructions: when the change in the power grid demand response coefficient is detected to exceed the preset threshold (e.g., 10%), the key rotation is triggered in advance; 4) The new key hash value is synchronized to the authorized node through the cross-platform data channel.

[0099] As illustrated in Figure 1 above, the electric vehicle interconnection and shared charging operation method achieves several key benefits. Firstly, by establishing a cross-platform data channel through standardized interface protocols and verifying and optimizing interface version compatibility and transmission stability, the protocol differences between different charging platforms and regulatory systems are effectively resolved, ensuring efficient interoperability of multi-source data. Furthermore, implementing multimodal identity verification based on the cross-platform data channel dynamically links user registration and permission allocation, achieving real-time adaptation of electric vehicle user permission levels and enhancing platform security. Secondly, by integrating dynamic parameters such as geographical location, charging pile status, and grid load to construct a charging resource map, and combining this with electric vehicle user permissions to generate collaborative optimization instructions containing navigation paths and charging parameters, resource matching accuracy is significantly improved. Thirdly, by uploading charging process data in real-time through bidirectional regulatory data streams and receiving regulatory instruction feedback, dynamic adjustments to charging strategies and rapid responses to abnormal events are achieved, ensuring the compliance and security of charging services. In summary, the technical solution of this application enables collaborative optimization of stable cross-platform interconnection, intelligent scheduling, and closed-loop supervision during electric vehicle charging.

[0100] Please refer to Figure 2, which illustrates an electric vehicle interconnection and shared charging operation device provided in this application embodiment. This device may include a channel establishment module 201, an authentication module 202, an instruction generation module 203, and a monitoring module 204, as detailed below:

[0101] Channel establishment module 201 is used to interconnect heterogeneous systems between the multi-source charging platform and the regulatory platform through a standardized interface protocol, and establish a cross-platform data channel.

[0102] Authentication module 202 is used to build a dynamic user authentication system based on cross-platform data channels, use multimodal authentication methods to complete electric vehicle user registration and permission allocation to obtain user permission levels, and feed back the authentication results to the regulatory platform;

[0103] The instruction generation module 203 is used to perform intelligent charging pile matching based on the real-time multi-dimensional charging resource map according to the user's permission level, and generate dynamic service instructions containing navigation paths and charging parameters.

[0104] The monitoring module 204 is used to create a two-way monitoring data stream through a cross-platform data channel during the execution of the charging service, to perform real-time monitoring of the entire lifecycle of the charging process, and to dynamically adjust the service strategy based on the feedback instructions from the monitoring platform.

[0105] Optionally, the electric vehicle interconnection and shared charging operation device illustrated in Figure 2 may further include a classification module, a differentiation processing module, and an anomaly generation module, wherein:

[0106] The classification module is used to collect equipment abnormal codes in real time through the charging pile controller throughout the entire charging cycle, classify them into communication failures, hardware failures and safety alarms, and mark the classification results as abnormal event tags.

[0107] The differentiated processing module is used to initiate differentiated processing procedures based on abnormal event tags: for communication failures, it enables link self-testing and channel switching, and synchronizes the switching record to the cross-platform data channel; for hardware failures, it marks the device as unavailable and dispatches a maintenance work order, while updating the real-time multi-dimensional charging resource map; for safety alarms, it triggers emergency stop protection and notifies the fire supervision platform, generating an emergency event log and sending it back to the bidirectional supervision data stream.

[0108] The anomaly generation module is used to generate user-side anomaly reports, including fault impact analysis and solution time estimates, and input the report data into the module that optimizes the dynamic service instructions.

[0109] Optionally, the electric vehicle interconnection and shared charging operation device illustrated in Figure 2 may further include a parsing module, a calculation module, an adjustment module, and an update module, wherein:

[0110] The parsing module is used to receive grid dispatch instructions from the bidirectional regulatory data stream in real time and parse the demand response coefficient and time-of-use pricing incentive signals.

[0111] The calculation module is used to calculate the adjustable capacity margin based on the acquired vehicle battery status and feed the adjustable capacity margin back to the power adjustment module.

[0112] The adjustment module is used to dynamically adjust the output power curve of the charging pile based on the adjustable capacity margin by the power adjustment module.

[0113] The update module is used to write the adjustment results of the charging pile output power curve into the bidirectional regulatory data stream and trigger the billing strategy update.

[0114] As illustrated in Figure 2 above, the electric vehicle interconnection and shared charging operation device achieves several key benefits. Firstly, by establishing a cross-platform data channel through standardized interface protocols and verifying and optimizing interface version compatibility and transmission stability, the protocol differences between different charging platforms and regulatory systems are effectively resolved, ensuring efficient interoperability of multi-source data. Furthermore, the implementation of multimodal identity verification based on the cross-platform data channel dynamically links user registration and permission allocation, achieving real-time adaptation of electric vehicle user permission levels and enhancing platform security. Secondly, by integrating dynamic parameters such as geographical location, charging pile status, and grid load to construct a charging resource map, and combining this with electric vehicle user permissions to generate collaborative optimization instructions containing navigation paths and charging parameters, resource matching accuracy is significantly improved. Thirdly, by uploading charging process data in real-time through bidirectional regulatory data streams and receiving regulatory instruction feedback, dynamic adjustments to charging strategies and rapid responses to abnormal events are achieved, ensuring the compliance and safety of charging services. In summary, the technical solution of this application enables collaborative optimization of stable cross-platform interconnection, intelligent scheduling, and closed-loop supervision during electric vehicle charging.

[0115] Figure 3 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in Figure 3, the electronic device 3 of this embodiment mainly includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a program for an electric vehicle interconnection and shared charging operation method. When the processor 30 executes the computer program 32, it implements the steps in the above-described electric vehicle interconnection and shared charging operation method embodiment, such as steps S101 to S104 shown in Figure 1. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-described device embodiments, such as the functions of the channel establishment module 201, authentication module 202, instruction generation module 203, and monitoring module 204 shown in Figure 2.

[0116] For example, the computer program 32 of the electric vehicle interconnection and shared charging operation method mainly includes: establishing a cross-platform data channel by realizing the heterogeneous system interconnection between the multi-source charging platform and the regulatory platform through a standardized interface protocol; constructing a dynamic user authentication system based on the cross-platform data channel, using multimodal authentication to complete electric vehicle user registration and permission allocation to obtain user permission levels, and feeding back the authentication results to the regulatory platform; performing intelligent charging pile matching based on a real-time multi-dimensional charging resource map according to the user permission level, and generating dynamic service instructions containing navigation paths and charging parameters; during the execution of the charging service, creating a bidirectional regulatory data flow through the cross-platform data channel, performing real-time full lifecycle supervision of the charging process, and dynamically adjusting the service strategy based on the feedback instructions from the regulatory platform. The computer program 32 can be divided into one or more modules / units, one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 32 in the electronic device 3. For example, computer program 32 can be divided into the functions of channel establishment module 201, authentication module 202, instruction generation module 203, and supervision module 204 (a module in the virtual device). The specific functions of each module are as follows: Channel establishment module 201 is used to realize the heterogeneous system interconnection between the multi-source charging platform and the supervision platform through a standardized interface protocol, and establish a cross-platform data channel; Authentication module 202 is used to build a dynamic user authentication system based on the cross-platform data channel, use multimodal authentication to complete electric vehicle user registration and permission allocation to obtain user permission levels, and feed back the authentication results to the supervision platform; Instruction generation module 203 is used to perform intelligent charging pile matching based on the real-time multi-dimensional charging resource map according to the user permission level, and generate dynamic service instructions containing navigation paths and charging parameters; Supervision module 204 is used to create a two-way supervision data stream through the cross-platform data channel during the charging service execution process, perform real-time full life cycle supervision of the charging process, and dynamically adjust the service strategy based on the feedback instructions from the supervision platform.

[0117] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that FIG3 is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0118] The processor 30 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0119] The memory 31 can be an internal storage unit of the electronic device 3, such as a hard disk or RAM. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 31 can include both internal and external storage units of the electronic device 3. The memory 31 is used to store computer programs and other programs and data required by the electronic device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed. That is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above-described device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0121] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0122] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0123] In the embodiments provided in this application, it should be understood that the disclosed apparatus / device and method can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0126] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program for the electric vehicle interconnection and shared charging operation method can be stored in a storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments, namely, realizing the heterogeneous system interconnection between the multi-source charging platform and the regulatory platform through a standardized interface protocol to establish a cross-platform data channel; constructing a dynamic user authentication system based on the cross-platform data channel, using multimodal authentication to complete electric vehicle user registration and permission allocation to obtain user permission levels, and feeding back the authentication results to the regulatory platform; performing intelligent charging pile matching based on real-time multi-dimensional charging resource maps according to user permission levels, generating dynamic service instructions containing navigation paths and charging parameters; creating a two-way regulatory data stream through the cross-platform data channel to perform real-time full lifecycle supervision of the charging process, and dynamically adjusting service strategies based on feedback instructions from the regulatory platform. Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the contents of storage media can be appropriately added or removed according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, storage media do not include electrical carrier signals and telecommunication signals.

[0127] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application. The specific embodiments described above further illustrate the purpose, technical solutions, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the protection scope of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this invention.

Claims

1. A method for operating interconnected and shared charging for electric vehicles, characterized in that, The method includes: establishing a cross-platform data channel by interconnecting heterogeneous systems of a multi-source charging platform and a regulatory platform through a standardized interface protocol; constructing a dynamic user authentication system based on the cross-platform data channel, using multimodal authentication to complete electric vehicle user registration and permission allocation to obtain user permission levels, and feeding back the authentication results to the regulatory platform; performing intelligent charging pile matching based on a real-time multi-dimensional charging resource map according to the user permission levels, generating dynamic service instructions containing navigation paths and charging parameters; creating a bidirectional regulatory data stream through the cross-platform data channel during the charging service execution process, performing real-time full lifecycle monitoring of the charging process, and dynamically adjusting the service strategy based on the feedback instructions from the regulatory platform; the method further includes: receiving grid dispatch instructions from the bidirectional regulatory data stream in real time, parsing demand response coefficients and time-of-use pricing incentive signals; calculating adjustable capacity margin based on the acquired vehicle battery status, and feeding back the adjustable capacity margin to the power adjustment module; the power adjustment module dynamically adjusting the charging pile output power curve according to the adjustable capacity margin; writing the adjustment result of the charging pile output power curve into the bidirectional regulatory data stream and triggering a billing strategy update, wherein the adjustable capacity margin... Represented as The The demand response coefficient, the The current state of battery charge, the For the rated total battery capacity, the power adjustment module dynamically adjusts the charging pile output power curve based on the adjustable capacity margin. Specifically, for high-priority vehicles, it maintains the lowest rate. Synchronize with the real-time multi-dimensional charging resource map; for adjustable loads, calculate the V2G reverse power supply based on the value λ of the time-of-use electricity price incentive signal. ,like The To trigger the V2G electricity price incentive threshold, k is the power release coefficient, and the... To maximize the allowed reverse power, the method also includes a data security enhancement system that runs throughout the method, performing the following collaborative protection steps: during data transmission through cross-platform data channels, a one-time encryption key is generated using quantum random numbers to perform end-to-end encryption on control commands; the encrypted data packet is appended with a digital signature and synchronized to the blockchain node; during the authentication of electric vehicle users in the dynamic user authentication system, Paillier semi-homomorphic encryption is applied to the biometric template, and the encryption result of Paillier semi-homomorphic encryption serves as the basis for generating user permission tokens; the timestamp hashes of key operations are recorded to the blockchain in the bidirectional monitoring data stream to form an audit chain, and the encryption key rotation strategy is dynamically updated based on the blockchain evidence storage results.

2. The electric vehicle interconnection and shared charging operation method as described in claim 1, characterized in that, The process of establishing a cross-platform data channel by interconnecting heterogeneous systems between a multi-source charging platform and a regulatory platform through a standardized interface protocol includes: using deep packet inspection technology to identify differences in interface specifications when performing protocol conversion tests on each access platform; constructing a protocol conversion middleware based on the analysis results of the interface specification differences; and establishing a dual-channel redundant transmission link based on the protocol conversion middleware.

3. The electric vehicle interconnection and shared charging operation method as described in claim 1, characterized in that, The construction of a dynamic user authentication system based on the cross-platform data channel, which uses multimodal authentication to complete electric vehicle user registration and permission allocation to obtain user permission levels, includes: generating biometric templates through liveness detection and document OCR recognition, and performing online verification with a public security database; dynamically generating user permission tokens based on the verification results of the online verification; binding the user permission tokens to the cross-platform data channel, and when cross-platform operations are detected, parsing the token parameters through the business logic adaptation layer to trigger a distributed authentication service for real-time authentication.

4. The electric vehicle interconnection and shared charging operation method as described in claim 1, characterized in that, The process of matching intelligent charging piles based on a real-time multi-dimensional charging resource map, according to the user's permission level, and generating dynamic service instructions containing navigation paths and charging parameters includes: real-time aggregation of charging pile status data from multiple platforms to construct a real-time multi-dimensional charging resource map containing geographical location, interface type, and real-time electricity price; dynamically calculating a charging demand urgency index based on the user's current vehicle SOC value, battery capacity, and destination information; generating a charging station recommendation sequence using a multi-objective optimization algorithm based on the charging demand urgency index and the real-time multi-dimensional charging resource map; and generating a dynamic navigation route through a path planning engine and calculating the optimal charging parameter combination using a charging strategy optimizer.

5. The electric vehicle interconnection and shared charging operation method as described in claim 4, characterized in that, The path planning engine's working method includes constructing a charging time estimation model and performing the following steps: establishing a vehicle routing problem model with time windows, integrating the real-time reachable radius output by the charging time estimation model; generating a dynamic road resistance function based on the real-time reachable radius, wherein the road resistance weight of the dynamic road resistance function is inversely proportional to the charging time estimation value; and using an ant colony optimization algorithm to solve for the optimal path, with the pheromone update rule being: The The pheromone evaporation coefficient is positively correlated with the time sensitivity output by the charging time prediction model. Q is the pheromone baseline intensity or pheromone total amount constant, representing the baseline value of the total amount of pheromone released by a single ant on a unit path. The travel time for path k is calculated in real time using the dynamic road resistance function. It is a small constant used to prevent division by zero errors.

6. The electric vehicle interconnection and shared charging operation method as described in claim 1, characterized in that, The method further includes: throughout the entire charging cycle, collecting equipment anomaly codes in real time through the charging pile controller, classifying them into communication failures, hardware failures, and safety alarms, and marking the classification results as anomaly event tags; initiating differentiated processing procedures based on the anomaly event tags: for communication failures, enabling link self-checks and channel switching, and synchronizing the switching records to the cross-platform data channel; for hardware failures, marking the equipment as unavailable and dispatching a maintenance work order, while updating the real-time multi-dimensional charging resource map; for safety alarms, triggering emergency stop protection and notifying the fire monitoring platform, generating an emergency event log and sending it back to the bidirectional monitoring data stream; generating a user-side anomaly report, including fault impact analysis and solution time estimation, and inputting the report data into the module that optimizes the dynamic service instructions.

7. An electronic device, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

8. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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