Charging pile reservation charging method, device and storage medium

Abstract

The application relates to the field of new energy automobile power supply, and provides a charging pile reservation charging method, equipment and a storage medium.The method comprises the following steps: receiving a charging reservation request sent by a user terminal; acquiring time-sharing load data of a current power grid based on a power grid load prediction model, collecting real-time state data of a target charging pile group through an Internet of Things interface; generating a charging queue sequence through a multi-target optimization algorithm; sending a charging authorization instruction to a corresponding target charging pile of the target charging pile identifier according to the charging queue sequence, and synchronously updating a reservation state identifier of a charging pile interactive interface; collecting multi-dimensional operation data of the charging pile, and uploading the multi-dimensional operation data to a cloud platform in real time through an encrypted communication link; performing abnormal mode identification on the multi-dimensional operation data, and triggering a power redistribution strategy when an abnormal feature is detected; when charging is completed or terminated abnormally, generating a charging voucher based on a blockchain smart contract, and writing key parameters of a charging process into a distributed ledger.

Description

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 scheduled charging of charging piles. Background Technology

[0002] With the rapid development of new energy vehicles, the large-scale deployment and efficient operation of charging piles have become a key focus of urban infrastructure construction. Currently, the operation and management of charging piles face multiple challenges: on the one hand, the uneven spatial and temporal distribution of user charging demand can easily lead to localized grid overload; on the other hand, the utilization rate of charging piles is limited by static scheduling strategies, making it difficult to dynamically respond to changes in user demand and grid conditions. Furthermore, issues such as equipment malfunctions during charging and insufficient billing transparency also constrain user experience and operational efficiency.

[0003] In existing technologies, charging pile scheduling is mostly based on a simple first-come, first-served principle or a reservation mechanism for fixed time periods. However, the above-mentioned existing technologies have key drawbacks such as low resource utilization, high risk to power grid stability, difficulty in resisting malicious attacks or data tampering, lack of trust, and low efficiency in dispute resolution. Summary of the Invention

[0004] This application provides a charging pile reservation charging method, equipment, and storage medium, which can significantly improve power grid stability, charging service reliability, and user satisfaction.

[0005] On the one hand, this application provides a method for reserving charging at a charging station, the method comprising:

[0006] Receive a charging reservation request sent by a user terminal, wherein the charging reservation request includes the identifier of the target charging pile;

[0007] The current time-of-use load data of the power grid is obtained based on the power grid load prediction model, and the real-time status data of the target charging pile group is collected through the Internet of Things interface.

[0008] The time-sharing load data and real-time status data are input into the dynamic scheduling engine, and a charging queue order is generated through a multi-objective optimization algorithm.

[0009] According to the charging queue order, a charging authorization instruction is sent to the target charging pile corresponding to the target charging pile identifier, and the reservation status identifier of the charging pile interaction interface is updated synchronously.

[0010] After the target charging pile receives the charging authorization command and starts charging, multi-dimensional operation data of the charging pile is collected and uploaded to the cloud platform in real time via an encrypted communication link;

[0011] The multidimensional operating data is subjected to abnormal pattern recognition, and a power redistribution strategy is triggered when abnormal features are detected.

[0012] Upon completion of charging or abnormal termination, a billing voucher is generated based on a blockchain smart contract, and key parameters of the charging process are written into the distributed ledger.

[0013] On the other hand, this application provides a charging pile reservation charging device, the device comprising:

[0014] The receiving module is used to receive a charging reservation request sent by a user terminal, wherein the charging reservation request includes the identifier of the target charging pile;

[0015] The acquisition module is used to acquire the time-of-use load data of the current power grid based on the power grid load prediction model, and to collect the real-time status data of the target charging pile group through the Internet of Things interface;

[0016] The first generation module is used to input the time-sharing load data and real-time status data into the dynamic scheduling engine and generate a charging queue order through a multi-objective optimization algorithm.

[0017] The sending module is used to send a charging authorization instruction to the target charging pile corresponding to the target charging pile identifier according to the charging queue order, and to synchronously update the reservation status identifier of the charging pile interaction interface.

[0018] The transmission module is used to collect multi-dimensional operation data of the charging pile after the target charging pile receives the charging authorization command and starts charging, and upload it to the cloud platform in real time via an encrypted communication link;

[0019] The triggering module is used to identify abnormal patterns in the multidimensional operating data and trigger a power redistribution strategy when abnormal features are detected.

[0020] The second generation module is used to generate billing vouchers based on blockchain smart contracts when charging is completed or abnormally terminated, and to write key parameters of the charging process into the distributed ledger.

[0021] Thirdly, this application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the technical solution of the above-described charging pile reservation charging method.

[0022] Fourthly, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described charging pile reservation charging method.

[0023] As can be seen from the technical solutions provided in this application, on the one hand, by integrating the grid's time-sharing load data and charging pile group status data in real time through a dynamic scheduling engine, and combining it with a multi-objective optimization algorithm to generate a charging queue order, the charging task allocation can dynamically adapt to the grid's carrying capacity and equipment operating status, effectively balancing the grid load and avoiding the risk of local overload. At the same time, by optimizing the spatiotemporal resource allocation of charging piles, the utilization rate of charging piles is significantly improved. On the other hand, during the charging process, through real-time collection of multi-dimensional operating data and abnormal pattern recognition, a power redistribution strategy can be quickly triggered to dynamically adjust the power supply parameters of adjacent charging piles, isolate the impact of faults in a timely manner, and maintain the continuity of charging services, thereby ensuring the safety of the charging process and the overall reliability of the system. On the third hand, an encrypted communication link is used to realize secure data transmission between the charging pile monitoring node and the cloud platform. Combined with blockchain smart contracts, key parameters of the charging process are distributed and notarized. This not only prevents the risk of data tampering during transmission and storage, but also improves the transparency and credibility of the billing process through a multi-party verification mechanism, reducing user disputes. Attached Figure Description

[0024] 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.

[0025] Figure 1 This is a flowchart of the charging pile reservation charging method provided in the embodiments of this application;

[0026] Figure 2 This is a schematic diagram of the structure of the charging pile reservation charging device provided in the embodiments of this application;

[0027] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0028] 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.

[0029] 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.

[0030] For ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn to actual scale.

[0031] With the rapid development of new energy vehicles, the large-scale deployment and efficient operation of charging piles have become a key focus of urban infrastructure construction. Currently, the operation and management of charging piles face multiple challenges: on the one hand, the uneven spatial and temporal distribution of user charging demand can easily lead to localized grid overload; on the other hand, the utilization rate of charging piles is limited by static scheduling strategies, making it difficult to dynamically respond to changes in user demand and grid status. Furthermore, issues such as equipment malfunctions during charging and insufficient billing transparency also restrict user experience and operational efficiency. Existing technologies often rely on simple first-come, first-served principles or fixed-time reservation mechanisms for charging piles. For example, they do not consider the dynamic changes in real-time grid load and charging pile status, easily leading to problems such as localized grid overload or simultaneous idle charging piles; furthermore, they lack rapid response mechanisms for abnormal events and fail to achieve reliable data storage during the charging process. In summary, the above-mentioned existing technologies have the following key defects: (1) The real-time coordination capability between the charging scheduling strategy and the grid load and equipment status is insufficient, resulting in low resource utilization and high grid stability risk; (2) The data collection and communication security during the charging process is weak, making it difficult to resist malicious attacks or data tampering; (3) The billing mechanism relies on a centralized platform, which has problems of lack of trust and low efficiency in dispute resolution.

[0032] To address the aforementioned problems in the existing technology, this application proposes a method for reserving charging at charging stations, the flowchart of which is attached. Figure 1 As shown, the main steps include S101 to S107, which are detailed below:

[0033] Step S101: Receive a charging reservation request sent by the user terminal, wherein the charging reservation request includes the identifier of the target charging pile.

[0034] In this embodiment, the charging reservation request may include not only the target charging pile identifier but also the user priority level. The target charging pile identifier clarifies the user's selection preference, avoiding resource mismatch caused by random allocation. The user priority level provides a basis for differentiated services in dynamic scheduling, such as prioritizing emergency vehicles. Without priority classification, scheduling will become rigid and may fail to adapt to fluctuations in grid load.

[0035] Step S102: Obtain the time-of-use load data of the current power grid based on the power grid load prediction model, and collect the real-time status data of the target charging pile group through the Internet of Things interface.

[0036] In this embodiment, acquiring the current time-of-use load data of the power grid ensures dynamic matching between the charging plan and the grid's carrying capacity, avoiding overload. The real-time status data of the target charging pile group mainly includes charging gun occupancy status, port fault codes, current charging power, and historical usage frequency, etc. By collecting charging gun occupancy status and port fault codes, equipment availability can be perceived in real time, preventing ineffective scheduling. In other words, relying solely on historical data or static predictions will lead to a disconnect between scheduling and real-time status.

[0037] Step S103: Input the time-sharing load data and real-time status data into the dynamic scheduling engine, and generate a charging queue order through a multi-objective optimization algorithm.

[0038] In this embodiment, the optimization objectives of the multi-objective optimization algorithm include at least grid load balance, charging pile utilization, and user waiting time, etc. Grid load balance ensures grid stability and reduces the need for distribution network upgrades; charging pile utilization reduces equipment idle rates through spatiotemporal resource optimization; and user waiting time balances efficiency and fairness, improving user experience. Using a single-objective optimization (e.g., considering only grid load) would sacrifice other key indicators. Therefore, to achieve the optimal solution for resource allocation and resolve scheduling conflicts under multiple constraints, time-sharing load data and real-time status data can be input into a dynamic scheduling engine, and a charging queue order can be generated through a multi-objective optimization algorithm.

[0039] As one embodiment of this application, inputting time-sharing load data and real-time status data into the dynamic scheduling engine, and generating a charging queue order through a multi-objective optimization algorithm can be achieved through steps S1031 to S1034, as detailed below:

[0040] Step S1031: Generate the first weight coefficient matrix based on the user priority level.

[0041] Specifically, generating the first weight coefficient matrix based on user priority levels can be achieved through the following steps S10311 to S10313:

[0042] Step S10311: Classify user priorities and define weight benchmarks.

[0043] Specifically, users can be divided into N There are priority levels, such as emergency vehicles = level 1, commercial users = level 2, regular users = level 3, and so on, with each level corresponding to a basic weight value. ,satisfy For example: Level 1 weight = 0.6, Level 2 = 0.3, Level 3 = 0.1, and so on.

[0044] Step S10312: Dynamically adjust the base weight value corresponding to the priority level of each user.

[0045] Calculate dynamic correction factors based on user historical behavior data (such as on-time performance, charging frequency, etc.). as follows:

[0046] Ultimately, user priority is determined by level. users weight The calculation is as follows:

[0047] Step S10313: Construct the first weight coefficient matrix.

[0048] Specifically, the weight of each user Expand by time window to generate the first weight coefficient matrix. ,in, For the number of users, This represents the number of time periods.

[0049] Define the first weight coefficient matrix elements in as follows: .

[0050] As can be seen from steps S10311 to S10313 of the above embodiment, by dynamically correcting factors and integrating user historical behavior, rigid level division is avoided and scheduling fairness is improved.

[0051] Step S1032: Calculate the grid carrying capacity margin based on time-of-use load data and generate the second weighting coefficient matrix.

[0052] As an embodiment of this application, calculating the grid carrying capacity margin based on time-of-use load data and generating the second weighting coefficient matrix can be achieved through the following steps S10321 to S10323:

[0053] Step S10321: Calculate the grid carrying capacity margin based on time-of-use load data.

[0054] Specifically, time-of-use load data of the power grid can be obtained. and maximum allowable load Then, calculate the grid capacity margin for each time period. as follows:

[0055] right After normalization, we get as follows: .

[0056] Step S10322: Define the margin-weight mapping rule.

[0057] Specifically, a piecewise function is defined to map the grid carrying capacity margin to the grid weights as follows: .

[0058] Step S10323: Construct the second weight coefficient matrix.

[0059] Specifically, the second weight coefficient matrix is ​​generated. The elements therein are: .

[0060] As can be seen from steps S10321 to S10323 of the above embodiment, the grid state can be quantified into operable weight parameters through the segmented mapping rule, ensuring that the scheduling scheme is adapted to the real-time carrying capacity of the grid.

[0061] Step S1033: Construct a dynamic ranking function by fusing the first weight coefficient matrix and the second weight coefficient matrix using fuzzy hierarchical analysis.

[0062] As an embodiment of this application, the dynamic ranking function is constructed by fusing the first weight coefficient matrix and the second weight coefficient matrix using fuzzy hierarchical analysis, which can be achieved through the following steps S10331 to S10334:

[0063] Step S10331: Construct a fuzzy judgment matrix.

[0064] Specifically, the fuzzy importance relationship between user priority and grid load is defined and represented using triangular fuzzy numbers:

[0065] For example, if the user priority is "significantly important" than the grid load, then .

[0066] Step S10332: Calculate the fuzzy weights.

[0067] Specifically, calculate the fuzzy synthesis degree. as follows:

[0068] Deblurring yields the weight ratio ,For example, .

[0069] Step S10333: Construct a dynamic sorting function.

[0070] Specifically, the comprehensive weight matrix The calculation is as follows:

[0071] The objective function is defined as follows:

[0072] in, , indicating user k Is it in time? t Distribute charging.

[0073] Step S10334: Generate Pareto optimal solution.

[0074] Specifically, the NSGA-II algorithm is used for iterative optimization, with constraints including: each user is only allocated to one time period; the number of charging piles occupied in each time period is less than or equal to the number of available charging piles; and the total power demand is less than or equal to the maximum load of the power grid.

[0075] As can be seen from steps S10331 to S10334 of the above embodiment, by using fuzzy numbers to handle the uncertainty of subjective judgment, the weight fusion can be made to better fit the actual operational needs. The combination of the multi-objective optimization function and the NSGA-II algorithm ensures that the generated charging sequence achieves a balance in dimensions such as grid stability and user satisfaction.

[0076] Step S1034: Iteratively optimize the dynamic sorting function to generate a charging sequence that satisfies Pareto optimality.

[0077] Specifically, as an embodiment of this application, iteratively optimizing the dynamic sorting function to generate a Pareto optimal charging sequence can be achieved by: automatically inserting load balancing constraints when a sudden change in grid load is detected exceeding a threshold; setting exemption factors for charging requests from high-priority users based on user priority levels; and using simulated annealing to solve for the optimal sorting scheme under load balancing constraints to obtain a Pareto optimal charging sequence. In the above embodiment, the exemption factor is negatively correlated with the user's remaining battery power, and its calculation process is as follows: obtaining the State of Charge (SOC) value of the user's vehicle battery through the Battery Management System (BMS); predicting the user's remaining battery power upon arrival at the charging station based on historical charging data; and raising the exemption factor to the critical protection level when the remaining battery power is lower than a preset safety threshold.

[0078] As can be seen from steps S102 and S103 of the above embodiments, by integrating the grid time-sharing load data and charging pile group status data in real time through the dynamic scheduling engine, and generating the charging queue order by combining the multi-objective optimization algorithm, the charging task allocation can dynamically adapt to the grid carrying capacity and equipment operating status, effectively balancing the grid load and avoiding the risk of local overload. At the same time, by optimizing the spatiotemporal resource allocation of charging piles, the utilization rate of charging piles is significantly improved.

[0079] Step S104: Send a charging authorization instruction to the target charging pile corresponding to the target charging pile identifier according to the charging queue order, and simultaneously update the reservation status identifier on the charging pile interaction interface.

[0080] In this embodiment, the charging authorization instruction can include a dynamically adjusted start time window and a maximum allowable power value. The dynamic adjustment of the time window can adapt to the uncertainty of user arrival time, reducing the default rate, while the maximum allowable power value can constrain the power output of the charging pile, preventing local grid overload. Using a fixed time window or power limit would fail to respond to real-time changes. Therefore, to resolve the core contradiction between "uncertainty of user arrival time and fairness of resource allocation" in charging pile scheduling, and to improve system robustness and user satisfaction, after generating a charging queue ranking through a multi-objective optimization algorithm, a charging authorization instruction can be sent to the target charging pile corresponding to the target charging pile identifier based on the charging queue ranking, simultaneously updating the reservation status identifier on the charging pile's interactive interface. In this embodiment, the reservation status identifier on the charging pile's interactive interface refers to the real-time display of the charging pile's reservation status and availability information through visual symbols or text on the charging pile's physical interface (e.g., display screen, indicator light) or user terminal (e.g., mobile application). This includes core status identifier types such as idle status, reserved but not started, charging in progress, and abnormal / fault status. It can also be used to display dynamic information such as time window adjustments, priority prompts, and power limit indications. By dynamically adjusting the start time window and updating the status indicator in real time, users can accurately know the available time periods and reservation status of charging stations, reducing waiting time caused by information asymmetry. At the same time, intelligent billing and status notification in case of abnormal termination further improve service response efficiency and user satisfaction.

[0081] It should be noted that the dynamically adjusted start time window in the above embodiment is a reservation time period dynamically calculated by the system based on the charging pile's idle time and the user's arrival prediction. Its core function is to balance the contradiction between the charging pile resource utilization rate and the uncertainty of the user's arrival time. As an embodiment of this application, the dynamically adjusted start time window can be implemented through steps S1041 to S1043, as detailed below:

[0082] Step S1041: Based on the charging queue sorting, parse the reservation task sequence of each charging pile and calculate the original idle time period between adjacent tasks of each charging pile.

[0083] The original idle period is the time period when the charging pile is not occupied, which is naturally formed according to the current reservation queue, for example, 10:00-11:00 am.

[0084] Step S1042: Based on the predicted arrival time fluctuation range of the user terminal positioning data, a buffer period is added before the start point of the original idle period to form an adjusted available period.

[0085] Based on the predicted arrival time fluctuation range using user terminal location data, adding a buffer period before the original idle time slot's start point is essentially a dynamic setting of the buffer period. That is, adding a buffer time before the start point of the idle time slot, for example, adjusting the start point from 10:00 to 10:15, forming a new idle time slot of 10:15-11:00. Specifically, the buffer period is set as follows: Real-time traffic information is obtained from a traffic big data platform, and a probability density function for user arrival time is constructed; the confidence interval of the probability density function (e.g., a 95% confidence interval) is calculated, and the upper limit of the confidence interval is extracted. Set the buffer period to ,in, The system predicts a dynamic buffer time with a positive correlation to the variance of the user's historical arrival time. Visual prompts for the dynamic buffer are displayed on the charging station's interface, including a countdown progress bar and explanations of road condition influencing factors. For example, assuming the original idle period for charging station A is 10:00-11:00: the system predicts a user arrival time with a standard deviation of 15 minutes; the dynamic buffer duration is set to 15 minutes (positively correlated with the standard deviation); the adjusted idle period becomes 10:15-11:00.

[0086] Step S1043: When it is detected that the adjusted available time periods of multiple users overlap, the following priority arbitration is performed: keep the adjusted available time periods of high-priority users unchanged; shift the starting point of the available time period of low-priority users backward by the length of their own buffer time period; update the status flag of the charging pile to conflict resolution mode.

[0087] For example, if the original idle time slot for high-priority user X is 10:15-11:00 AM, and the original idle time slot for low-priority user Y is 10:30-11:15 AM, and their available time slots overlap after adjustment, then the available time slot for high-priority user X remains unchanged (i.e., it retains its original idle time slot). Only the available time slot for low-priority user Y is shifted 15 minutes later, becoming 10:45-11:30 AM. This ensures the charging rights of high-priority user X, while shifting the available time slot for low-priority user Y avoids overloading of charging stations, maintains the time continuity of the charging queue, and compensates for the time loss caused by the buffer zone setting.

[0088] In the above embodiments, although the fluctuation range of arrival time can be predicted based on the user terminal's location data, and a buffer period can be added before the original idle period start point to form an adjusted available period, in real-world scenarios, users may arrive late, i.e., the user's actual arrival time is later than the start point of the adjusted available period. In this case, the system can trigger the following operations: scan the charging pile group for available charging piles with idle periods earlier than the user's current time; generate a set of alternative charging solutions; guide the user to the optimal alternative charging pile through an augmented reality interface. The set of alternative charging solutions includes nearby charging pile identifiers and compensation points options. Specifically, it can be generated by: calculating the available charging capacity based on the real-time maximum allowable power value of nearby charging piles; labeling each alternative solution with estimated charging time and compensation points value; and recommending the optimal alternative solution based on the user's vehicle's remaining battery power. Thus, the system accurately sets the buffer range through a probabilistic model, reducing the risk of user lateness, and forms a closed-loop solution for lateness scenarios, ensuring system robustness.

[0089] As can be seen from steps S1041 to S1043 of the above embodiments, on the one hand, by predicting the fluctuation range of user arrival time and setting a dynamic buffer, the reservation time conflict caused by traffic uncertainty can be effectively alleviated; on the other hand, when multiple user time windows overlap, the available time period is dynamically adjusted based on the priority arbitration mechanism to prioritize the rights and interests of high-priority users, while optimizing the resource allocation efficiency of low-priority users.

[0090] Step S105: After the target charging pile receives the charging authorization command and starts charging, collect the multi-dimensional operation data of the charging pile and upload it to the cloud platform in real time via an encrypted communication link.

[0091] Collecting only single-dimensional data or transmitting it in plaintext will lead to monitoring blind spots and security risks. Therefore, in this embodiment, after the target charging pile receives the charging authorization command and starts charging, multi-dimensional operational data of the charging pile is collected. This multi-dimensional operational data can be collected by various types of devices deployed on the charging pile, such as vibration sensors, current transformers, and temperature detectors. By monitoring the health status of the equipment in multiple dimensions, the accuracy of fault detection can be improved. This multi-dimensional operational data of the charging pile is uploaded to the cloud platform in real time via an encrypted communication link, effectively preventing data tampering and man-in-the-middle attacks, and ensuring system security.

[0092] As an embodiment of this application, the aforementioned secure communication link can be implemented through steps S1051 to S1053, as detailed below:

[0093] Step S1051: Issue a unique digital certificate for each charging pile through the Public Key Infrastructure (PKI) / Certificate Authority (CA) system, implement two-way authentication in the Transmission Control Protocol (TCP) / Transport Layer Security (TLS) protocol, and generate a session key.

[0094] Step S1052: Based on the session key, dynamically segment and encrypt the collected multidimensional running data.

[0095] Specifically, based on the session key, dynamic fragmentation and encryption of the collected multi-dimensional operational data can be achieved by: dynamically selecting the encryption mode according to the data type; that is, using the AES-GCM mode with integrity verification for control commands and the ChaCha20 algorithm for streaming encryption for sensor data; dividing the data stream into fixed-size data fragments and attaching an independent checksum containing a timestamp and charging pile identifier to each data fragment; generating an incrementing sequence number for each data fragment and calculating an anti-tampering hash chain based on device fingerprint information; and performing the following operations on the cloud platform: verifying the continuity of the data fragment sequence number and the consistency of the hash chain; marking the abnormal data fragment and triggering a key rotation process when a hash chain break is detected; and performing end-to-end MAC verification after reassembling the complete data stream. This dynamic selection of the encryption mode based on data type achieves an intelligent balance between security and communication efficiency.

[0096] Step S1053: Monitor transmission delay and packet loss rate in real time and dynamically adjust the complexity of the encryption algorithm.

[0097] Specifically, when the transmission delay is detected to be below the threshold, the AES-256 encryption algorithm is enabled; when the transmission delay is detected to exceed the threshold, the ChaCha20-Poly1305 lightweight encryption is switched to; when continuous packet loss is detected, the data fragment retransmission mechanism is triggered and the fragment size is reduced.

[0098] Step S106: Perform abnormal pattern recognition on the multidimensional operating data, and trigger the power redistribution strategy when abnormal features are detected.

[0099] If passive alarms or manual intervention are used, downtime will be greatly increased. Therefore, in order to achieve fault self-healing capability and ensure system robustness, in this embodiment, abnormal pattern recognition can be performed on multi-dimensional operating data. When overcurrent fluctuations or poor contact characteristics are detected, a power redistribution strategy is triggered to dynamically adjust the power supply parameters of adjacent charging piles. Detecting overcurrent fluctuations can prevent equipment overload damage and extend hardware life, while dynamically adjusting the power supply parameters of adjacent charging piles can isolate the scope of fault impact and maintain service continuity.

[0100] Specifically, as an embodiment of this application, the abnormal pattern recognition of multi-dimensional operating data and the triggering of a power redistribution strategy when abnormal features are detected can be as follows: establish a power coupling model of the charging pile cluster based on the maximum allowable power value, calculate the power adjustable range of each node; identify the fault impact radius of the abnormal charging pile, determine the set of affected adjacent charging piles; for the set of affected adjacent charging piles, generate a local power adjustment scheme according to the power adjustable range of each node and the upper limit of the power grid capacity, and implement millisecond-level power redistribution through edge computing nodes.

[0101] In the above embodiments, the power coupling model of the charging pile cluster is established based on the maximum allowable power value, and the power adjustable range of each node is calculated through the following steps Sa1 to Sa3:

[0102] Step Sa1: Define the power coupling matrix.

[0103] In this embodiment, defining the power coupling relationship matrix mainly includes two steps: topology modeling of the charging pile cluster and defining the power balance equation. Specifically, topology modeling of the charging pile cluster can be modeled as a power network node graph, with each charging pile as a node and electrical connections between nodes (such as cables and busbars) abstracted as edges. Then, an N×N node admittance matrix Y (where N is the total number of nodes) is constructed, where the elements... Represents a node With nodes The admittance between them reflects the electrical coupling strength, and the power balance equation can be defined as follows:

[0104] in, Represents a node The actual power and Representing nodes respectively and nodes voltage amplitude, Represents a node With nodes voltage phase difference, and These represent the real and imaginary parts of the nodal admittance matrix, respectively.

[0105] Step Sa2: Set constraints.

[0106] The constraints here mainly include the maximum allowable power of each node and grid stability constraints. The maximum allowable power of each node, i.e., the power ceiling, is determined by the grid carrying capacity and the hardware limitations of the charging pile. ,in, Represents a node The maximum allowable power value, and the grid stability constraint, also known as the global constraint, means that the total power of the cluster shall not exceed the grid's carrying capacity limit. ,Right now: .

[0107] Step Sa3: Solve for the adjustable power range.

[0108] Solving for the adjustable power range mainly involves linearization approximation, sensitivity analysis, and adjustable range calculation, among others. The linearization approximation primarily involves linearizing the nonlinear power equation near the steady-state operating point. ,in, The power change vector It is the Jacobian matrix (which can be calculated from the admittance matrix and voltage magnitude). Given the phase angle change vector, sensitivity analysis mainly includes calculating the sensitivity coefficient of power adjustment at each node to other nodes. Adjustable range calculation mainly refers to node Adjustable power range Must meet: , ,in, Represents a node Current actual power.

[0109] In the above embodiments, identifying the fault impact radius of the abnormal charging pile and determining the set of affected adjacent charging piles can be achieved through the following steps Sb1 to Sb4:

[0110] Step Sb1: Extract the fault characteristics of abnormal charging piles.

[0111] Specifically, nodes can be identified through multi-dimensional operational data (including current fluctuations, temperature anomalies, or vibration frequencies, etc.). k The fault types can be categorized, for example, if the instantaneous current value exceeds the threshold, it is an overcurrent fault; or if the vibration signal spectrum shows characteristic peaks in the range of 50 to 200 Hz, it is a poor contact fault, and so on.

[0112] Step Sb2: Model the electrical impact of abnormal charging piles.

[0113] Generally, voltage drops at faulty nodes (i.e., abnormal charging piles) have a significant impact on other nodes. Therefore, embodiments of this application can model the impact of voltage drops on other nodes as a function of electrical distance. Specifically, the faulty node... k The model for the impact of voltage drops on other nodes attenuating with electrical distance is as follows:

[0114] in, For nodes The voltage drop amplitude, For nodes arrive The electrical distance (which can be calculated from the admittance matrix). The attenuation coefficient is given (typically 0.1~0.3); then, nodes affected by electrical influences are defined, which only need to satisfy:

[0115] in, This is the voltage drop threshold (e.g., 5%). The radius of electrical influence is determined by the power grid topology and impedance characteristics.

[0116] Step Sb3: Analyze the physical topology impact of abnormal charging piles.

[0117] The physical topology impact of abnormal charging piles includes both the physical and topological impacts of the abnormal charging piles. Specifically, if the location of the charging pile is known, and the physical impact radius is set to... The set of nodes affected by the physical impact of abnormal charging piles. for:

[0118] As for the nodes affected by the abnormal charging pile's topology, a breadth-first search can be used based on the power grid topology graph to traverse the nodes directly or indirectly connected to the faulty node. Nodes with a traversal depth of no more than 3 hops or isolated by circuit breakers can be considered as the set of nodes affected by the abnormal charging pile's topology. ,Right now,

[0119] in, A set of nodes with a depth of no more than 3 hops. This is the set of nodes isolated by the circuit breaker.

[0120] Step Sb4: Combine the electrical and physical topology effects of the abnormal charging piles to obtain the set of affected adjacent charging piles.

[0121] Specifically, the affected set of adjacent charging stations .

[0122] For the affected set of adjacent charging piles, a local power adjustment scheme can be generated based on the adjustable power range of each node and the upper limit of the power grid capacity. This scheme can then be implemented via edge computing nodes with millisecond-level power redistribution. This can be achieved by: sending a power adjustment negotiation request to the affected user terminals; dynamically calculating the power descent gradient value based on user feedback; and executing the power adjustment corresponding to the power descent gradient value. The power adjustment negotiation request includes the current adjustable power range, estimated charging delay time, and corresponding compensation integral value, which is positively correlated with the power descent magnitude. User feedback on the power adjustment negotiation request can include acceptance, partial acceptance, or rejection. Therefore, dynamically calculating the power descent gradient value based on user feedback can be as follows: when the user accepts the power adjustment negotiation request, a linear descent mode is used to adjust the charging pile's power, and the power descent gradient value can be a preset proportion of the preset maximum descent rate (e.g., 80%); when the user partially accepts the power adjustment negotiation request, a stepped descent mode is used to adjust the charging pile's power, and the power descent gradient value can match the user-specified maximum acceptable descent; when the user rejects the power adjustment negotiation request, the power descent gradient value can be set to zero and a backup scheme can be triggered. Here, triggering the backup plan involves: initiating the preheating process of nearby backup charging stations; reallocating the available power quota of the charging station group based on the maximum allowable power value; and guiding the user to migrate to the backup charging station through an augmented reality interface. The reallocation of the available power quota of the charging station group based on the maximum allowable power value can be achieved through the following steps Sc1 to Sc5:

[0123] Step Sc1: Determine the global power constraints and available power margin.

[0124] As mentioned earlier, the charging authorization command sent to the target charging pile includes the maximum allowed power value. Therefore, determining the global power constraint and the total available power can specifically involve extracting the upper limit of the power grid's carrying capacity, i.e., the maximum total power allowed by the charging pile cluster. Calculate the current total power of the cluster. Then, the available power margin is calculated based on this. ;in, For charging pile nodes The current power, if This triggers the power grid overload protection mechanism, forcibly reducing the power of low-priority users.

[0125] Step Sc2: Identify available charging station clusters and electric vehicle user priority weights.

[0126] Specifically, this can be achieved by excluding faulty nodes and their directly affected neighboring nodes, while retaining the set of charging stations in normal condition. Assuming a set of available charging stations, the priority weights for electric vehicle users are then defined, specifically by mapping the priority level of electric vehicle users to a weight coefficient. Then, a charging urgency correction factor is added. (For example, when the remaining battery level is below 20%) ).

[0127] The priority weight of electric vehicle users is obtained by comprehensive analysis. .

[0128] Step Sc3: Allocate dynamic power quotas.

[0129] In this embodiment, the allocation of dynamic power quotas mainly includes two steps: allocating initial quotas according to the priority weight ratio of electric vehicle users and iteratively adjusting to meet local constraints. The allocation of initial quotas according to the priority weight ratio of electric vehicle users can be achieved by calculating the initial power quota for each charging pile. ,in, For charging piles The hardware power limit. The iterative adjustment satisfies the local constraints, primarily including constraint 1 and constraint 2. Then, according to the adjustment rules for constraint 1 and constraint 2, that is, if a certain node's... Then set its quota to and the remaining power The power of electric vehicle users is redistributed to other nodes according to their priority weights. This process is repeated iteratively until all nodes meet the constraints, where constraint 1 is that the power of a single node does not exceed its hardware limit. Constraint 2 means that the total power of the cluster does not exceed .

[0130] Step Sc4: Verify power coupling compensation and stability.

[0131] Specifically, the nodes can be calculated based on the power coupling model. Sensitivity of power adjustment to other nodes The actual power change is corrected according to the following formula:

[0132] in, Represents a node The amount of power adjustment, Indicates something different from a node The power adjustment of other nodes; then, deploy edge computing nodes to monitor grid frequency fluctuations. That is, if This triggers emergency load reduction, gradually decreasing power quotas from low to high priority. This is a preset frequency threshold.

[0133] Step Sc5: Update the dynamic scheduling engine and user notifications.

[0134] Specifically, this could be the final allocated power quota. The data is sent back to the dynamic scheduling engine to update the charging queue order. A new charging plan is then pushed to the user's terminal, including the adjusted power value, estimated charging completion time, and compensation points, etc. If the user refuses to migrate to the backup charging station, the new quota is forcibly applied and the charging power is limited to [a specific setting]. .

[0135] As can be seen from steps S105 and S106 of the above embodiments, during the charging process, the power redistribution strategy can be quickly triggered by real-time acquisition of multi-dimensional operating data and identification of abnormal features, the power supply parameters of adjacent charging piles can be dynamically adjusted, the impact of faults can be isolated in time, and the continuity of charging services can be maintained, thereby ensuring the safety of the charging process and the overall reliability of the system. The use of encrypted communication links realizes secure data transmission between the charging pile monitoring node and the cloud platform.

[0136] Step S107: When charging is completed or abnormally terminated, a billing voucher is generated based on the blockchain smart contract, and the key parameters of the charging process are written into the distributed ledger.

[0137] Because relying on a centralized billing system may lead to a lack of trust and difficulties in auditing, in order to solve the problems of billing transparency and data credibility, in this embodiment of the application, when charging is completed or abnormally terminated, a billing voucher can be generated based on a blockchain smart contract, and the key parameters of the charging process can be written into a distributed ledger. The smart contract can automatically execute the billing rules, reducing human error, while the distributed ledger can prevent single-point data tampering and improve the efficiency of dispute resolution. In the above embodiments, the generation of the blockchain smart contract can be as follows: Key events in the charging process are divided into multiple blocks by timestamps, each block containing the hash value of the preceding block, forming an immutable hash chain; before a block is submitted to the distributed ledger, the following operations are performed: requiring the user, charging operator, and power grid regulator to verify the continuity of the hash chain of the current block; after at least two of the three parties sign and confirm, the block is written into the candidate chain; for the billing calculation logic, a zero-knowledge proof containing the following elements is generated: a proof of the correctness of the charging amount calculation in the current block and a commitment to the anonymity of user privacy data (such as identity information, charging location, etc.); the proof of the correctness of the charging amount calculation in the current block is compared with the hash value of the previous block in the candidate chain. After the block is bound, it is submitted to the main chain. Specifically, for the billing calculation logic, generating a zero-knowledge proof containing a proof of the correctness of the charging volume calculation for the current block and a hidden commitment to user privacy data can be: converting the charging volume calculation formula into a verifiable arithmetic circuit, generating commitment parameters containing a hash of the circuit structure; allowing users to verify the following based on the commitment parameters without exposing electricity usage details: the charging volume calculation conforms to a preset formula and privacy data remains encrypted during the calculation process; when a user objects to the billing result, an on-chain arbitration node extracts the commitment parameters and circuit structure of the disputed block, verifies the integrity of the computing environment based on the preceding hash value, and verifies the current charging volume calculation process, generating an arbitration proof, etc.

[0138] As can be seen from steps S106 and S107 of the above embodiments, the use of encrypted communication links to achieve secure data transmission between the charging pile monitoring node and the cloud platform, combined with blockchain smart contracts to distribute and store key parameters of the charging process, not only prevents the risk of data tampering during transmission and storage, but also improves the transparency and credibility of the billing process through multi-party verification mechanisms, reducing user disputes.

[0139] Furthermore, in order to combine equipment operation data with LSTM model prediction of fault probability to achieve preventive maintenance and thereby reduce the sudden failure rate of equipment, the charging pile reservation charging method in the above embodiment may also include: analyzing historical fault data through LSTM neural network to establish an equipment health prediction model, wherein the equipment health prediction model outputs the predicted fault probability and confidence interval; collecting the harmonic distortion rate, contactor operation count and temperature rise rate of the charging pile, inputting them into the equipment health prediction model, and calculating the remaining service life and corresponding health index of the charging pile; when the health index of the charging pile is lower than a preset threshold, performing the following operations: calculating the maintenance urgency based on the remaining service life of the charging pile, generating a maintenance work order and scheduling resources; dynamically adjusting the charging queue sorting to reduce the power allocation weight of fault-risk charging piles; pushing charging pile health status prompts to associated user terminals, etc. To improve the accuracy and generalization ability of fault prediction, the equipment health prediction model can be continuously optimized through federated learning and digital twin technology. Specifically, this includes: collecting on-site maintenance records and constructing a mapping database containing fault features, maintenance measures, and repair effects; using a federated learning framework, performing the following operations: training local models at the edge nodes of each charging station, extracting latent space representations of fault features, aggregating model gradients through secure multi-party computation, and updating global model parameters; and performing the following operations through the digital twin system: simulating fault evolution paths based on the mapping database, injecting simulation data into the health prediction model for adversarial training, and transmitting the optimized model parameters back to each edge node.

[0140] Furthermore, the charging pile reservation charging method in the above embodiments may also include an emergency response mechanism, namely: when receiving an emergency peak-shaving instruction from the power grid, initiating a rapid load reduction procedure; setting different power reduction priorities according to user contract type and vehicle usage; providing charging credit compensation to affected users and recommending alternative charging solutions; wherein, the rapid load reduction procedure may include: calculating the adjustable power capacity of the charging pile cluster and generating a multi-level reduction plan; prioritizing the reduction of charging power for low-priority users while preserving power supply for emergency service vehicles; calling upon vehicles with discharge capabilities to support the local power grid through V2G technology, etc.

[0141] Furthermore, the charging station reservation charging method in the above embodiments may also include: displaying a three-dimensional navigation interface of the charging station on the user terminal, overlaying real-time availability information; recommending the optimal arrival route and charging time combination based on the user's driving habits; providing a virtual charging experience function to simulate the differences in charging costs at different times, etc.

[0142] From the above appendix Figure 1The example of the charging pile reservation method demonstrates that, on the one hand, by dynamically integrating grid time-sharing load data and charging pile group status data in real time through a dynamic scheduling engine, and combining it with a multi-objective optimization algorithm to generate a charging queue order, the charging task allocation can dynamically adapt to the grid's carrying capacity and equipment operating status, effectively balancing the grid load and avoiding the risk of local overload. Simultaneously, by optimizing the spatiotemporal resource allocation of charging piles, the utilization rate of charging piles is significantly improved. On the other hand, during the charging process, through real-time collection of multi-dimensional operational data and anomaly pattern recognition, a power redistribution strategy can be quickly triggered, dynamically adjusting the power supply parameters of adjacent charging piles, promptly isolating the impact of faults and maintaining the continuity of charging services, thereby ensuring the safety of the charging process and the overall reliability of the system. Thirdly, an encrypted communication link is used to achieve secure data transmission between the charging pile monitoring nodes and the cloud platform. Combined with blockchain smart contracts for distributed storage of key parameters in the charging process, this not only prevents the risk of data tampering during transmission and storage but also improves the transparency and credibility of the billing process through a multi-party verification mechanism, reducing user disputes.

[0143] Please see the appendix Figure 2 This application provides a charging pile reservation charging device, which may include a receiving module 201, an acquiring module 202, a first generating module 203, a sending module 204, a transmission module 205, a triggering module 206, and a second generating module 207, as detailed below:

[0144] The receiving module 201 is used to receive a charging reservation request sent by a user terminal, wherein the charging reservation request includes the identifier of the target charging pile;

[0145] The acquisition module 202 is used to acquire the time-of-use load data of the current power grid based on the power grid load prediction model, and to collect the real-time status data of the target charging pile group through the Internet of Things interface.

[0146] The first generation module 203 is used to input time-sharing load data and real-time status data into the dynamic scheduling engine and generate a charging queue order through a multi-objective optimization algorithm.

[0147] The sending module 204 is used to send a charging authorization instruction to the target charging pile corresponding to the target charging pile identifier according to the charging queue order, and to synchronously update the reservation status identifier of the charging pile interaction interface.

[0148] The transmission module 205 is used to collect multi-dimensional operation data of the charging pile after the target charging pile receives the charging authorization command and starts charging, and upload it to the cloud platform in real time via an encrypted communication link;

[0149] Trigger module 206 is used to identify abnormal patterns in multidimensional operating data and trigger a power redistribution strategy when abnormal features are detected.

[0150] The second generation module 207 is used to generate billing vouchers based on blockchain smart contracts when charging is completed or abnormally terminated, and to write key parameters of the charging process into the distributed ledger.

[0151] From the above appendix Figure 2 As illustrated by the example of the charging pile reservation charging device, on the one hand, the dynamic scheduling engine integrates real-time grid load data and charging pile group status data, and combines multi-objective optimization algorithms to generate charging queue ordering. This allows the charging task allocation to dynamically adapt to the grid's carrying capacity and equipment operating status, effectively balancing the grid load and avoiding the risk of local overload. Simultaneously, by optimizing the spatiotemporal resource allocation of charging piles, the utilization rate of charging piles is significantly improved. On the other hand, during the charging process, real-time acquisition of multi-dimensional operating data and anomaly pattern recognition can quickly trigger power redistribution strategies, dynamically adjusting the power supply parameters of adjacent charging piles, timely isolating fault impacts, and maintaining the continuity of charging services, thereby ensuring the safety of the charging process and the overall reliability of the system. Thirdly, an encrypted communication link is used to achieve secure data transmission between the charging pile monitoring nodes and the cloud platform. Combined with blockchain smart contracts, key parameters of the charging process are distributed and notarized. This not only prevents the risk of data tampering during transmission and storage but also improves the transparency and credibility of the billing process through a multi-party verification mechanism, reducing user disputes.

[0152] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 3 As shown, the electronic device 3 in 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 a charging pile reservation charging method. When the processor 30 executes the computer program 32, it implements the steps in the above-described charging pile reservation charging method embodiment, for example... Figure 1 The steps S101 to S107 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of the receiving module 201, the acquiring module 202, the first generating module 203, the sending module 204, the transmission module 205, the triggering module 206, and the second generating module 207 are shown.

[0153] For example, the computer program 32 of the charging pile reservation charging method mainly includes: receiving a charging reservation request sent by a user terminal, wherein the charging reservation request includes a target charging pile identifier; obtaining the current grid load data based on a grid load prediction model, and collecting real-time status data of the target charging pile group through an IoT interface; inputting the load data and real-time status data into a dynamic scheduling engine, and generating a charging queue order through a multi-objective optimization algorithm; sending a charging authorization instruction to the target charging pile corresponding to the target charging pile identifier according to the charging queue order, and synchronously updating the reservation status identifier of the charging pile interaction interface; after the target charging pile accepts the charging authorization instruction and starts charging, collecting multi-dimensional operation data of the charging pile, and uploading it to the cloud platform in real time via an encrypted communication link; performing abnormal pattern recognition on the multi-dimensional operation data, and triggering a power reallocation strategy when abnormal features are detected; generating a billing voucher based on a blockchain smart contract when charging is completed or abnormally terminated, and writing the key parameters of the charging process into a distributed ledger. The computer program 32 can be divided into one or more modules / units, one or more modules / units are stored in memory 31, and executed by 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. These instruction segments describe the execution process of the computer program 32 in the electronic device 3. For example, the computer program 32 can be divided into the functions of a receiving module 201, an acquisition module 202, a first generation module 203, a sending module 204, a transmission module 205, a triggering module 206, and a second generation module 207 (a module in the virtual device). The specific functions of each module are as follows: The receiving module 201 is used to receive a charging reservation request sent by a user terminal, wherein the charging reservation request includes the identifier of the target charging pile; the acquisition module 202 is used to acquire the time-of-use load data of the current power grid based on the power grid load prediction model, and collect the real-time status data of the target charging pile group through the Internet of Things interface; the first generation module 203 is used to input the time-of-use load data and the real-time status data into the dynamic scheduling engine, and through multiple The target optimization algorithm generates a charging queue order; the sending module 204 is used to send a charging authorization instruction to the target charging pile corresponding to the target charging pile identifier according to the charging queue order, and synchronously update the reservation status identifier of the charging pile interaction interface; the transmission module 205 is used to collect multi-dimensional operation data of the charging pile after the target charging pile receives the charging authorization instruction and starts charging, and upload it to the cloud platform in real time via an encrypted communication link; the triggering module 206 is used to perform abnormal pattern recognition on the multi-dimensional operation data, and trigger a power redistribution strategy when abnormal features are detected; the second generation module 207 is used to generate a billing voucher based on the blockchain smart contract when charging is completed or abnormally terminated, and write the key parameters of the charging process into the distributed ledger.

[0154] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This 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 shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the implementation of all or part of the processes in the above-described embodiments can also be accomplished by a computer program instructing related hardware. The computer program for the charging pile reservation method can be stored in a storage medium. When executed by a processor, the computer program can implement the steps of the above-described method embodiments, namely: receiving a charging reservation request sent by a user terminal, wherein the charging reservation request includes a target charging pile identifier; obtaining the current grid load data based on a grid load prediction model and collecting real-time status data of the target charging pile group through an IoT interface; inputting the load data and real-time status data into a dynamic scheduling engine and generating a charging queue order through a multi-objective optimization algorithm; sending a charging authorization instruction to the target charging pile corresponding to the target charging pile identifier according to the charging queue order, and synchronously updating the reservation status identifier of the charging pile interaction interface; after the target charging pile accepts the charging authorization instruction and starts charging, collecting multi-dimensional operation data of the charging pile and uploading it to the cloud platform in real time via an encrypted communication link; performing abnormal pattern recognition on the multi-dimensional operation data, and triggering a power redistribution strategy when abnormal features are detected; and generating a billing voucher based on a blockchain smart contract when charging is completed or abnormally terminated, and writing the key parameters of the charging process into a distributed ledger. 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.

[0164] 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 charging pile reservation charging method, characterized in that, The method comprises: receiving a charging reservation request sent by a user terminal, the charging reservation request containing a target charging pile identifier and a user priority level; obtaining time-of-use load data of a current power grid based on a power grid load prediction model, and collecting real-time state data of a target charging pile group through an Internet of Things interface; inputting the time-of-use load data and the real-time state data into a dynamic scheduling engine, and generating a charging queue ranking through a multi-objective optimization algorithm; sending a charging authorization instruction to a target charging pile corresponding to the target charging pile identifier according to the charging queue ranking, and synchronously updating a reservation state identifier of a charging pile interactive interface, the reservation state identifier of the charging pile interactive interface being displayed on a physical interface of the charging pile or a user terminal, and real-time display of reservation state and availability information of the charging pile being realized through visual symbols or text, the charging authorization instruction including a maximum allowable power value; after the target charging pile starts charging upon acceptance of the charging authorization instruction, collecting multi-dimensional operation data of the charging pile, and uploading the multi-dimensional operation data to a cloud platform in real time through an encrypted communication link; performing abnormal pattern recognition on the multi-dimensional operation data, and triggering a power redistribution strategy when an abnormal feature is detected, the abnormal pattern recognition including: establishing a power coupling model of a charging pile cluster based on the maximum allowable power value, and calculating a power adjustable range of each node; identifying a failure influence radius of an abnormal charging pile, and determining a set of affected adjacent charging piles; for the set of affected adjacent charging piles, generating a local power adjustment scheme according to the power adjustable range of each node and an upper limit of power grid bearing, and implementing millisecond-level power redistribution through an edge computing node, the identification including: extracting failure features of the abnormal charging pile; modeling electrical influence of the abnormal charging pile; analyzing physical topology influence of the abnormal charging pile; and synthesizing the electrical influence and the physical topology influence of the abnormal charging pile to obtain the set of affected adjacent charging piles; when charging is completed or abnormally terminated, generating a billing voucher based on a blockchain smart contract, and writing key parameters of the charging process into a distributed ledger.

2. The charging pile reservation charging method of claim 1, wherein, The inputting of the time-of-use load data and the real-time state data into the dynamic scheduling engine, and the generation of the charging queue ranking through the multi-objective optimization algorithm include: generating a first weight coefficient matrix according to the user priority level; calculating a power grid bearing margin based on the time-of-use load data, and generating a second weight coefficient matrix; fusing the first weight coefficient matrix and the second weight coefficient matrix through fuzzy analytic hierarchy process to construct a dynamic ranking function; iteratively optimizing the dynamic ranking function to generate a charging sequence that satisfies Pareto optimality.

3. The charging pile reservation charging method of claim 2, wherein, The iterative optimization of the dynamic ranking function to generate a charging sequence that satisfies Pareto optimality includes: when a power grid load mutation exceeds a threshold value, automatically inserting a load balancing constraint condition; according to the user priority level, setting an exemption factor for a charging request of a high-priority user; Analog annealing algorithm is used to solve the optimal scheduling scheme under the load balancing constraint, and the charging sequence satisfying the Pareto optimality is obtained.

4. The charging pile reservation charging method of claim 1, wherein, The charging authorization instruction also includes a dynamically adjusted starting time window, and the dynamically adjusted starting time window is generated to include: Based on the charging queue sorting, the reservation task sequence of each charging pile is analyzed, and the original idle period between adjacent tasks of each charging pile is calculated; According to the fluctuation range of the predicted arrival time of the user terminal positioning data, a buffer time period is added before the starting point of the original idle period to form an adjusted available time period; When it is detected that the adjusted available time periods of multiple users overlap, the following priority arbitration is performed: The adjusted available time period of the high-priority user is kept unchanged; The starting point of the available time period of the low-priority user is shifted backward by the length of its own buffer time period; The state identifier of the charging pile is updated to the conflict resolution mode.

5. The charging pile reservation charging method of claim 1, wherein, The generation of the local power adjustment scheme and the implementation of millisecond-level power redistribution through the edge computing node include: Sending a power adjustment negotiation request to the affected user terminal; According to the acceptance degree of user feedback, the power drop gradient value is dynamically calculated; Performing the power adjustment corresponding to the power drop gradient value. 6.The charging pile reservation charging method of claim 1, wherein, The establishment of the encrypted communication link includes: Issuing a unique digital certificate for each charging pile through a PKI / CA system, implementing two-way authentication in a TCP / TLS handshake protocol, and generating a session key; Based on the session key, the collected multi-dimensional operation data is dynamically sharded and encrypted; Real-time monitoring of transmission delay and packet loss rate, and dynamic adjustment of encryption algorithm complexity.

7. A charging pile reservation charging device, characterized in that, The device includes: A receiving module for receiving a charging reservation request sent by a user terminal, the charging reservation request including a target charging pile identifier and a user priority level; An acquisition module for acquiring time-sharing load data of the current power grid based on a power grid load prediction model, and collecting real-time state data of a target charging pile group through an Internet of Things interface; A first generation module for inputting the time-sharing load data and real-time state data into a dynamic scheduling engine to generate a charging queue sorting through a multi-objective optimization algorithm; A sending module for sending a charging authorization instruction to a target charging pile corresponding to the target charging pile identifier based on the charging queue sorting, and synchronously updating a reservation state identifier of a charging pile interactive interface, the reservation state identifier of the charging pile interactive interface being displayed on a physical interface of the charging pile or a user terminal through visual symbols or text to display the reservation state and availability information of the charging pile in real time, the charging authorization instruction including a maximum allowed power value; A transmission module for collecting multi-dimensional operation data of the charging pile after the target charging pile accepts the charging authorization instruction for charging start, and uploading the data to a cloud platform in real time through an encrypted communication link; The triggering module is configured to perform abnormal pattern recognition on the multi-dimensional operation data, and trigger a power redistribution strategy when an abnormal feature is detected, and the abnormal pattern recognition on the multi-dimensional operation data and the triggering of the power redistribution strategy when the abnormal feature is detected comprises: establishing a power coupling model of a charging pile cluster based on the maximum allowed power value, and calculating a power adjustable range of each node; identifying a failure influence radius of an abnormal charging pile, and determining a set of affected adjacent charging piles; and for the set of affected adjacent charging piles, generating a local power adjustment scheme according to the power adjustable range of each node and an upper limit of power grid bearing, and implementing millisecond-level power redistribution through an edge computing node, and the identifying of the failure influence radius of the abnormal charging pile and the determining of the set of affected adjacent charging piles comprises: extracting a failure feature of the abnormal charging pile; modeling an electrical influence of the abnormal charging pile; analyzing a physical topology influence of the abnormal charging pile; and synthesizing the electrical influence and the physical topology influence of the abnormal charging pile to obtain the set of affected adjacent charging piles. The second generation module is configured to generate a charging voucher based on a block chain smart contract when charging is completed or abnormally terminated, and write key parameters of a charging process into a distributed ledger.

8. 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, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

9. A storage medium storing a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

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