Charging pile reservation charging method and device and storage medium

Abstract

The invention relates to the field of new energy automobile power supply, and provides a charging pile reservation charging method and device and a storage medium, and the method comprises the steps: receiving a charging reservation request sent by a user terminal; obtaining time-sharing load data of a current power grid based on the power grid load prediction model, and collecting real-time state data of the target charging pile group through the Internet of Things interface; generating a charging queue sequence 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 sequence, and synchronously updating a reservation state identifier of a charging pile interaction 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; carrying out abnormal mode identification on the multi-dimensional operation data, and triggering a power redistribution strategy when an abnormal feature is detected; and when the charging is completed or abnormally terminated, generating a billing voucher based on the block chain smart contract, and writing the key parameters of the charging process into a distributed account book.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle power supply, and in particular to a charging pile reservation charging method, device and storage medium. Background Art

[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 development. Currently, the operational management of charging piles faces multiple challenges: First, the uneven temporal and spatial distribution of user charging demand can easily lead to localized grid overloads; second, charging pile utilization 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 anomalies during charging and a lack of billing transparency hinder user experience and operational efficiency.

[0003] Existing technologies often rely on a simple first-come, first-served basis or fixed-time appointment mechanisms for charging station scheduling. However, these existing technologies suffer from key flaws, including low resource utilization, high grid stability risks, vulnerability to malicious attacks and data tampering, a lack of trust, and inefficient dispute resolution. Summary of the Invention

[0004] The present application provides a charging pile reservation charging method, device and storage medium, which can significantly improve the stability of the power grid, the reliability of charging services and user satisfaction.

[0005] In one aspect, the present application provides a method for reserving charging at a charging pile, the method comprising: Receiving a charging reservation request sent by a user terminal, wherein the charging reservation request includes a target charging pile identifier; Obtain the current grid's time-sharing load data based on the grid load forecasting model, and collect real-time status data of the target charging pile group through the Internet of Things interface; Input the time-sharing load data and real-time status data into a dynamic scheduling engine, and generate a charging queue ranking using 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 sorting, and synchronously updating the reservation status identifier of the charging pile interaction interface; After the target charging pile receives the charging authorization instruction 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; performing abnormal pattern recognition on the multi-dimensional operating data, and triggering a power reallocation strategy when abnormal features are detected; When charging is completed or terminated abnormally, 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.

[0006] On the other hand, the present application provides a charging pile reservation charging device, the device comprising: A receiving module, configured to receive a charging reservation request sent by a user terminal, wherein the charging reservation request includes a target charging pile identifier; The acquisition module is used to obtain the time-sharing load data of the current power grid based on the power grid load forecasting model, and collect the real-time status data of the target charging pile group through the Internet of Things interface; A first generation module is configured to input the time-sharing load data and real-time status data into a dynamic scheduling engine and generate a charging queue ranking using a multi-objective optimization algorithm; A sending module, configured to send a charging authorization instruction to a target charging pile corresponding to the target charging pile identifier according to the charging queue sorting, and synchronously update a reservation status identifier on the charging pile interaction interface; A transmission module is used to collect multi-dimensional operating data of the charging pile after the target charging pile receives the charging authorization instruction and starts charging, and upload the data to the cloud platform in real time via an encrypted communication link; a trigger module, configured to identify abnormal patterns in the multi-dimensional operating data and trigger a power reallocation strategy when abnormal features are detected; The second generation module 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.

[0007] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the technical solution of the above-mentioned charging pile reservation charging method are implemented.

[0008] In a fourth aspect, the present 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-mentioned charging pile reservation charging method.

[0009] From the technical solutions provided in the present application, it can be seen that, on the one hand, the dynamic scheduling engine is used to integrate the time-sharing load data of the power grid and the status data of the charging pile group in real time, and the charging queue sorting is generated by combining the multi-objective optimization algorithm, so that the charging task allocation can dynamically adapt to the power grid carrying capacity and the operating status of the equipment, effectively balancing the power grid load and avoiding the risk of local overload. At the same time, by optimizing the spatiotemporal resource allocation of the charging piles, the utilization rate of the charging piles is significantly improved; on the other hand, during the charging process, through the real-time collection and abnormal pattern recognition of multi-dimensional operating data, the power redistribution strategy can be quickly triggered, the power supply parameters of adjacent charging piles can be dynamically adjusted, the fault impact can be isolated in time and the continuity of the charging service can be maintained, thereby ensuring the safety of the charging process and the overall reliability of the system; thirdly, an encrypted communication link is used to realize secure data transmission between the charging pile monitoring node and the cloud platform, and the key parameters of the charging process are distributedly stored in combination with the blockchain smart contract, which 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 This is a flow chart of the charging pile reservation charging method provided in an embodiment of the present application; Figure 2 This is a schematic diagram of the structure of the charging pile reservation charging device provided in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0013] In this specification, adjectives such as first and second may be used only to distinguish one element or action from another element or action, 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.

[0014] In this specification, for the convenience of description, the sizes of various parts shown in the drawings are not drawn according to the actual proportions.

[0015] 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 development. Currently, the operational management of charging piles faces multiple challenges: On the one hand, the uneven temporal and spatial distribution of user charging demand can easily lead to localized grid overloads; on the other hand, charging pile utilization 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 anomalies and insufficient billing transparency during charging also hinder user experience and operational efficiency. Existing technologies often use a simple first-come, first-served basis or a fixed-time reservation mechanism for charging piles. For example, these methods fail to account for the dynamic changes in the real-time grid load and the status of charging piles, which can easily lead to the coexistence of localized grid overloads and idle charging piles. Furthermore, there is a lack of a rapid response mechanism to abnormal events, and reliable evidence of charging process data is not available. In summary, the above-mentioned existing technologies have the following key defects: (1) The real-time coordination capability of charging scheduling strategies with grid load and equipment status is insufficient, resulting in low resource utilization and high grid stability risk; (2) The data collection and communication security of the charging process are weak, making it difficult to resist malicious attacks or data tampering; (3) The billing mechanism relies on a centralized platform, resulting in a lack of trust and low dispute resolution efficiency.

[0016] In view of the above problems of the prior art, this application proposes a charging pile reservation charging method, the flow chart of which is shown in the attached Figure 1 As shown, it mainly includes steps S101 to S107, which are detailed as follows: Step S101: receiving a charging reservation request sent by a user terminal, wherein the charging reservation request includes a target charging pile identifier.

[0017] In this embodiment of the present application, a charging reservation request may include not only the target charging station identifier but also the user priority level. The target charging station identifier clarifies the user's selection preference, preventing resource mismatches caused by random allocation, while the user priority level provides a basis for differentiated service in dynamic scheduling, such as prioritizing emergency vehicles. Without priority classification, scheduling becomes rigid and may not adapt to grid load fluctuations.

[0018] Step S102: Obtain the time-sharing 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.

[0019] In the embodiment of the present application, obtaining the time-sharing load data of the current power grid can ensure that the charging plan is dynamically matched with the power grid's carrying capacity to avoid overload. The real-time status data of the target charging pile group mainly includes the charging gun occupancy status, port fault code, current charging power, and historical usage frequency, etc. By collecting the charging gun occupancy status, port fault code, etc., the equipment availability can be perceived in real time to prevent invalid scheduling. In other words, if only relying on historical data or static predictions, the scheduling will be out of touch with the real-time status.

[0020] Step S103: Input the time-sharing load data and real-time status data into the dynamic scheduling engine, and generate the charging queue ranking through a multi-objective optimization algorithm.

[0021] In an embodiment of the present application, the optimization objectives of the multi-objective optimization algorithm include at least grid load balance, charging pile utilization, and user waiting time, etc. Among them, grid load balance can ensure grid stability and reduce the need for distribution network transformation. Charging pile utilization is to reduce equipment idle rate through spatiotemporal resource optimization, and user waiting time can balance efficiency and fairness and improve user experience. If a single-objective optimization is adopted (for example, only considering grid load), other key indicators will be sacrificed. Therefore, in order 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 the dynamic scheduling engine, and the charging queue ranking can be generated through the multi-objective optimization algorithm.

[0022] As an embodiment of the present application, time-sharing load data and real-time status data are input into the dynamic scheduling engine, and charging queue sorting is generated by a multi-objective optimization algorithm through steps S1031 to S1034, as detailed below: Step S1031: Generate a first weight coefficient matrix according to the user priority level.

[0023] Specifically, generating the first weight coefficient matrix according to the user priority level can be achieved through the following steps S10311 to S10313: Step S10311: Classify user priorities and define weight criteria.

[0024] Specifically, users can be divided into N Priority levels, such as emergency vehicles = level 1, commercial users = level 2, ordinary users = level 3, etc., each level corresponds to a basic weight value ,satisfy For example: level 1 weight = 0.6, level 2 = 0.3, level 3 = 0.1, and so on.

[0025] Step S10312: Dynamically modify the basic weight value corresponding to each user priority level.

[0026] Calculate dynamic correction factors based on user historical behavior data (such as punctuality rate, charging frequency, etc.) as follows:

[0027] Finally, user priority is level Users Weight The calculation is as follows:

[0028] Step S10313: Construct a first weight coefficient matrix.

[0029] Specifically, the weight of each user Expand by time window to generate the first weight coefficient matrix ,in, is the number of users, is the number of time periods.

[0030] Define the first weight coefficient matrix Elements in as follows: .

[0031] From step S10311 to step S10313 of the above embodiment, it can be seen that by integrating user historical behaviors through dynamic correction factors, rigid level division is avoided and scheduling fairness is improved.

[0032] Step S1032: Calculate the grid carrying margin based on the time-sharing load data and generate a second weight coefficient matrix.

[0033] As an embodiment of the present application, calculating the grid carrying margin based on time-sharing load data and generating the second weight coefficient matrix may be implemented by the following steps S10321 to S10323: Step S10321: Calculate the grid carrying margin based on the time-sharing load data.

[0034] Specifically, the grid time-sharing load data can be obtained and the maximum allowable load Then, calculate the grid carrying margin for each period as follows:

[0035] right Normalized processing, we get as follows: .

[0036] Step S10322: define margin-weight mapping rules.

[0037] Specifically, a piecewise function is defined to map the grid carrying margin to the grid weight as follows: .

[0038] Step S10323: Construct a second weight coefficient matrix.

[0039] Specifically, generate the second weight coefficient matrix , where the elements are: .

[0040] From step S10321 to step S10323 of the above embodiment, it can be seen that the grid state can be quantified into an operational weight parameter through the segmented mapping rule, ensuring that the scheduling plan adapts to the real-time carrying capacity of the grid.

[0041] Step S1033: The first weight coefficient matrix and the second weight coefficient matrix are integrated through the fuzzy analytic hierarchy process to construct a dynamic ranking function.

[0042] As an embodiment of the present application, the construction of a dynamic ranking function by fusing the first weight coefficient matrix and the second weight coefficient matrix through the fuzzy analytic hierarchy process can be achieved by the following steps S10331 to S10334: Step S10331: Construct a fuzzy judgment matrix.

[0043] Specifically, the fuzzy importance relationship between user priority and grid load is defined and expressed using triangular fuzzy numbers:

[0044] For example, if the user priority is “significantly more important” than the grid load, then .

[0045] Step S10332: Calculate the fuzzy weight.

[0046] Specifically, the fuzzy synthesis degree is calculated as follows:

[0047] Defuzzification to obtain weight ratio ,For example, .

[0048] Step S10333: Construct a dynamic sorting function.

[0049] Specifically, the comprehensive weight matrix The calculation is as follows:

[0050] The objective function is defined as follows:

[0051] in, , indicating that the user k Is it in time t Allocate charging.

[0052] Step S10334: Generate a Pareto optimal solution.

[0053] Specifically, the NSGA-II algorithm is used for iterative optimization, and the constraints include: each user is assigned to only one time period; the number of occupied charging piles in each time period ≤ the number of available piles; the total power demand ≤ the maximum load of the grid.

[0054] From steps S10331 to S10334 of the above embodiment, it can be seen that by processing the uncertainty of subjective judgment through fuzzy numbers, weight fusion can be made more in line with actual operational needs, and the combination of the multi-objective optimization function and the NSGA-II algorithm ensures that the generated charging sequence is balanced in terms of grid stability, user satisfaction and other dimensions.

[0055] Step S1034: Iteratively optimize the dynamic sorting function to generate a charging sequence that satisfies Pareto optimality.

[0056] Specifically, as one embodiment of the present application, iteratively optimizing a dynamic sorting function to generate a Pareto-optimal charging sequence can include: automatically inserting a load balancing constraint when a sudden change in grid load exceeding a threshold is detected; setting an exemption factor for charging requests from high-priority users based on user priority levels; and employing a simulated annealing algorithm to solve the optimal sorting scheme under the load balancing constraint to obtain a Pareto-optimal charging sequence. In the above embodiment, the exemption factor is negatively correlated with the user's remaining battery life, and its calculation process is as follows: obtaining the State of Charge (SOC) value of the user's vehicle battery through the onboard Battery Management System (BMS); predicting the remaining battery life of the user upon arrival at the charging station based on historical charging data; and raising the exemption factor to a critical protection level when the remaining battery life falls below a preset safety threshold.

[0057] It can be seen from step S102 and step S103 of the above embodiment that the dynamic scheduling engine integrates the grid time-sharing load data and the charging pile group status data in real time, and combines the multi-objective optimization algorithm to generate the charging queue sorting, so that 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.

[0058] Step S104: Send a charging authorization instruction to the target charging pile corresponding to the target charging pile identifier according to the charging queue sorting, and simultaneously update the reservation status identifier of the charging pile interaction interface.

[0059] In an embodiment of the present application, a charging authorization instruction may include a dynamically adjusted start time window and a maximum allowable power value. Dynamic adjustment of the time window can adapt to the uncertainty of user arrival times and reduce default rates, while the maximum allowable power value can constrain the power output of the charging pile and prevent local grid overloads. Using a fixed time window or power cap would be incapable of responding to real-time changes. Therefore, to address the core conflict between user arrival time uncertainty and resource allocation fairness in charging pile scheduling and improve system robustness and user satisfaction, after generating a charging queue ranking using 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, and the reservation status indicator on the charging pile interaction interface can be simultaneously updated. In an embodiment of the present application, the reservation status indicator on the charging pile interaction interface refers to a 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 information includes core status indicator types such as idle, reserved but not started, charging, and abnormal / faulty states. It can also be used to display dynamic information such as time window adjustments, priority prompts, and power limit indications. Through dynamically adjusted start time windows and real-time status updates, users can accurately understand the available time slots and reservation status of charging stations, reducing waiting times caused by information asymmetry. Furthermore, intelligent billing and status notifications during abnormal terminations further improve service response efficiency and user satisfaction.

[0060] It should be noted that the dynamically adjusted starting time window in the above embodiment is a reservation time period dynamically calculated by the system based on the idle period of the charging pile and the user arrival prediction. Its core function is to balance the contradiction between the utilization rate of the charging pile resources and the uncertainty of the user's arrival time. As an embodiment of this application, the dynamically adjusted starting time window can be implemented through steps S1041 to S1043, as detailed below: Step S1041: parsing the scheduled task sequence of each charging pile based on the charging queue sorting, and calculating the original idle time period between adjacent tasks of each charging pile.

[0061] The original idle period is the unoccupied time period naturally formed by the charging pile according to the current reservation queue, for example, 10:00-11:00 in the morning.

[0062] Step S1042: Based on the fluctuation range of the predicted arrival time according to the user terminal positioning data, a buffer period is added before the starting point of the original idle period to form an adjusted available period.

[0063] Based on the fluctuation range of the arrival time predicted by the user terminal positioning data, adding a buffer time period before the starting point of the original idle period is actually a dynamic setting of the buffer time period, that is, adding a buffer time before the starting point of the idle period, for example, adjusting the starting point of 10:00 to 10:15, forming a new idle period of 10:15-11:00. Specifically, the setting of the buffer time period is as follows: accessing the traffic big data platform to obtain real-time road condition information, constructing the probability density function of the user's arrival time; calculating the confidence interval of the probability density function (for example, 95% confidence interval), and extracting the upper limit value of the confidence interval ; Set the buffer period to ,in, Positively correlated with the variance of historical user arrival times; a visual indicator of the dynamic buffer zone is provided on the charging station interface, including a countdown progress bar and an explanation of road condition influencing factors. For example, assuming charging station A's original idle period is 10:00-11:00: the system predicts the standard deviation of user arrival times to be 15 minutes; the dynamic buffer zone duration is set to 15 minutes (positively correlated with the standard deviation); the adjusted idle period becomes 10:15-11:00.

[0064] Step S1043: 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; and the status flag of the charging pile is updated to conflict resolution mode.

[0065] For example, if high-priority user X's original idle period is 10:15-11:00 AM and low-priority user Y's original idle period is 10:30-11:15 AM, if the adjustment causes their available periods to overlap, high-priority user X's available period will remain unchanged, while low-priority user Y's available period will be shifted 15 minutes later, to 10:45-11:30 AM. This protects high-priority user X's charging rights, while shifting low-priority user Y's available period prevents charging pile overload, maintains the temporal continuity of the charging queue, and compensates for the time lost due to the buffer setting.

[0066] In the above embodiment, although the fluctuation range of the arrival time can be predicted based on the user terminal positioning data, a buffer period is added before the starting point of the original idle period to form an adjusted available period. However, in actual scenarios, users may be late, that is, the user's actual arrival time is later than the starting 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 whose idle periods are earlier than the user's current time; generate a set of alternative charging solutions; and guide the user to the optimal alternative charging pile through an augmented reality interface. The alternative charging solution set includes the identifiers of nearby charging piles and compensation points options. The specific generation method can be: calculate the available charging capacity based on the real-time maximum allowable power values ​​of nearby charging piles; annotate each alternative with an estimated charging time and compensation points value; and recommend the optimal alternative based on the remaining power of the user's vehicle. In this way, the buffer range is accurately set through the probability model to reduce the risk of user lateness, and a closed-loop solution is formed for late arrival scenarios to ensure system robustness.

[0067] From steps S1041 to S1043 of the above embodiment, it can be seen that, on the one hand, by predicting the fluctuation range of user arrival time and setting a dynamic buffer zone, the appointment 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 give priority to protecting the rights and interests of high-priority users while optimizing the resource allocation efficiency of low-priority users.

[0068] Step S105: After the target charging pile receives the charging authorization instruction and starts charging, the multi-dimensional operation data of the charging pile is collected and uploaded to the cloud platform in real time via an encrypted communication link.

[0069] If only single-dimensional data is collected or transmitted in plain text, it will lead to monitoring blind spots and safety risks. Therefore, in an embodiment of the present application, after the target charging pile receives the charging authorization instruction and starts charging, the multi-dimensional operation data of the charging pile is collected. These multi-dimensional operation data can be collected by various types of devices such as vibration sensors, current transformers and temperature detectors deployed on the charging pile. By monitoring the health status of the equipment in multiple dimensions, the accuracy of fault detection can be improved. The multi-dimensional operation data of these charging piles are uploaded to the cloud platform in real time via an encrypted communication link, which effectively prevents data tampering and man-in-the-middle attacks and ensures system security.

[0070] As an embodiment of the present application, the above-mentioned encrypted communication link may be implemented through steps S1051 to S1053, as detailed below: Step S1051: A unique digital certificate is issued for each charging station through the Public Key Infrastructure (PKI) / Certificate Authority (CA) system, two-way authentication is implemented in the Transmission Control Protocol (TCP) / Transport Layer Security (TLS) protocol, and a session key is generated.

[0071] Step S1052: Based on the session key, the collected multi-dimensional operation data is dynamically encrypted in slices.

[0072] Specifically, based on the session key, dynamic sharding encryption of collected multi-dimensional operational data can be performed by: dynamically selecting the encryption mode based on the data type, i.e., using the AES-GCM mode with integrity check for data such as control instructions, and using the stream encryption ChaCha20 algorithm for sensor data; dividing the data stream into fixed-size data slices, and appending an independent checksum containing a timestamp and charging pile identifier to each data slice; generating an incremental serial number for each data slice, and calculating a tamper-proof hash chain based on the device fingerprint information; performing the following operations on the cloud platform: verifying the serial number continuity and hash chain consistency of the data slice; when a hash chain break is detected, marking the abnormal data slice and triggering the key rotation process; and implementing end-to-end MAC verification after reassembling the complete data stream. The above-mentioned dynamic selection of encryption mode based on data type achieves an intelligent balance between security and communication efficiency.

[0073] Step S1053: Monitor the transmission delay and packet loss rate in real time, and dynamically adjust the complexity of the encryption algorithm.

[0074] Specifically, when the transmission delay is detected to be lower than the threshold, the AES-256 encryption algorithm is enabled; when the transmission delay is detected to be higher than the threshold, it switches to ChaCha20-Poly1305 lightweight encryption; when continuous packet loss is detected, the data segment retransmission mechanism is triggered and the segment size is reduced.

[0075] Step S106: performing abnormal pattern recognition on the multi-dimensional operation data, and triggering a power reallocation strategy when abnormal features are detected.

[0076] If passive alarms or manual intervention are used, the downtime will be greatly increased. Therefore, in order to achieve fault self-healing capability and ensure system robustness, in an embodiment of the present application, abnormal pattern recognition can be performed on multi-dimensional operating data. When overcurrent fluctuations or poor contact characteristics are detected, the power redistribution strategy is triggered and the power supply parameters of adjacent charging piles are dynamically adjusted. Among them, 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.

[0077] Specifically, as an embodiment of the present application, abnormal pattern recognition is performed on multi-dimensional operating data, and when abnormal features are detected, the power reallocation strategy triggered can be: establishing a power coupling model of the charging pile cluster based on the maximum allowable power value, and calculating the power adjustable range of each node; identifying the fault impact radius of the abnormal charging pile, and determining the affected set of adjacent charging piles; for the affected set of adjacent charging piles, based on the power adjustable range of each node and the upper limit of the power grid carrying capacity, generating a local power adjustment plan and implementing millisecond-level power redistribution through the edge computing node.

[0078] In the above embodiment, a 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 by the following steps Sa1 to Sa3: Step Sa1: Define the power coupling relationship matrix.

[0079] In the embodiment of the present application, defining the power coupling relationship matrix mainly includes two steps: topological modeling of the charging pile cluster and defining the power balance equation. Among them, the topological modeling of the charging pile cluster can be specifically modeling the charging pile cluster as a power network node graph, each charging pile is a node, and the electrical connections between nodes (such as cables, busbars) are abstracted as edges. Then, an N×N node admittance matrix Y (N is the total number of nodes) is constructed, where the element Representation node With node The admittance between them reflects the electrical coupling strength, and the power balance equation can be defined as:

[0080] in, Representation node The actual power, and Represents nodes respectively and nodes The voltage amplitude, Representation node With node The voltage phase difference, and denote the real and imaginary parts of the node admittance matrix, respectively.

[0081] Step Sa2: Set constraints.

[0082] The constraints here mainly include the maximum allowable power of each node and the grid stability constraint. The maximum allowable power of each node, i.e., the upper limit of power, is determined by the grid carrying capacity and the hardware limitations of the charging pile: ,in, Representation node The maximum allowable power value of the grid stability constraint is also called the global constraint, that is, the total power of the cluster does not exceed the upper limit of the grid load. ,Right now: .

[0083] Step Sa3: Calculate the power adjustable range.

[0084] Solving the power adjustable range mainly includes linearization approximation, sensitivity analysis, and adjustable range calculation, etc. Among them, linearization approximation mainly linearizes the nonlinear power equation near the steady-state operating point as follows: ,in, is the power change vector, is the Jacobian matrix (which can be calculated from the admittance matrix and the voltage amplitude), is the phase angle change vector. Sensitivity analysis mainly includes calculating the sensitivity coefficient of each node’s power adjustment to other nodes. , the adjustable range calculation mainly refers to the node Power adjustable range Need to meet: , ,in, Representation node Current actual power.

[0085] In the above embodiment, identifying the fault impact radius of the abnormal charging pile and determining the affected set of adjacent charging piles can be achieved through the following steps Sb1 to Sb4: Step Sb1: Extract the fault characteristics of the abnormal charging pile.

[0086] Specifically, nodes can be identified through multi-dimensional operating data (including current fluctuations, temperature anomalies or vibration frequencies, etc.). k For example, if the instantaneous value of the current exceeds the threshold, it is an overcurrent fault; for example, if the vibration signal spectrum has a characteristic peak in the range of 50 to 200 Hz, it is a poor contact fault, and so on.

[0087] Step Sb2: Model the electrical impact of abnormal charging piles.

[0088] Generally speaking, the voltage drop of the faulty node (i.e., the abnormal charging pile) has a greater impact on other nodes. Therefore, the embodiment of the present application can model the impact of the voltage drop on other nodes along the electrical distance. Specifically, the faulty node k The model of the impact of voltage drop on other nodes attenuating with electrical distance is:

[0089] in, For nodes The voltage drop amplitude, For nodes arrive The electrical distance (which can be calculated from the admittance matrix), is the attenuation coefficient (typical value 0.1~0.3); then define the nodes affected by electricity, which only need to meet the following requirements:

[0090] in, is the voltage drop threshold (e.g. 5%), is the electrical influence radius, which is determined by the grid topology and impedance characteristics.

[0091] Step Sb3: Analyze the physical topology impact of abnormal charging piles.

[0092] The physical topological impact of abnormal charging piles includes the physical impact and topological impact of abnormal charging piles. If the location of the charging pile is known, and the physical impact radius is set to , then the set of nodes that are physically affected by the abnormal charging pile is for:

[0093] As for the nodes affected by the topology of abnormal charging piles, we can use breadth-first search based on the grid topology to traverse the nodes directly or indirectly connected to the faulty node, and take the nodes with a traversal depth of no more than 3 hops or isolated by circuit breakers as the node set affected by the topology of abnormal charging piles. ,Right now,

[0094] in, is a set of nodes with a depth of no more than 3 hops, A collection of nodes isolated by a circuit breaker.

[0095] Step Sb4: The electrical impact and physical topology impact of the abnormal charging pile are combined to obtain a set of affected adjacent charging piles.

[0096] Specifically, the affected adjacent charging pile set .

[0097] For a set of affected adjacent charging piles, a local power adjustment plan is generated based on the power adjustable range of each node and the grid's upper capacity. Millisecond-level power redistribution is then implemented through edge computing nodes. This can include: sending a power adjustment negotiation request to the affected user terminal; dynamically calculating a power reduction gradient based on user feedback; and executing power adjustments corresponding to the power reduction gradient. The power adjustment negotiation request includes the current adjustable power range, the estimated charging delay time, and a corresponding compensation integral value, with the compensation integral value being positively correlated with the power reduction magnitude. The user's acceptance of the power adjustment negotiation request can include acceptance, partial acceptance, or rejection. Therefore, based on the user's acceptance of the power adjustment negotiation request, the power reduction gradient value is dynamically calculated. When the user accepts the power adjustment negotiation request, the charging pile's power is adjusted using a linear reduction mode, with the power reduction gradient value being a preset proportion (e.g., 80%) of a preset maximum reduction rate. When the user partially accepts the power adjustment negotiation request, the charging pile's power is adjusted using a step-down mode, with the power reduction gradient value matching the user-specified maximum acceptable reduction magnitude. When the user rejects the power adjustment negotiation request, the power reduction gradient value is reset to zero, triggering a backup plan. Here, triggering the backup plan includes: starting the preheating process of the adjacent backup charging pile; reallocating the available power quota of the charging pile group based on the maximum allowable power value; and guiding the user to migrate to the backup charging pile through the augmented reality interface. The reallocation of the available power quota of the charging pile group based on the maximum allowable power value can be achieved through the following steps Sc1 to Sc5: Step Sc1: Determine the global power constraint and available power margin.

[0098] As mentioned above, the charging authorization instruction sent to the target charging pile contains the maximum allowed power value. Therefore, determining the global power constraint and the total available power can be specifically done by extracting the grid load limit, that is, the maximum total power allowed by the charging pile cluster. , calculate the actual total power of the current cluster , and then calculate the available power margin based on this ;in, Charging pile node The current power, if , then the grid overload protection mechanism is triggered, forcing the power of low-priority users to be reduced.

[0099] Step Sc2: Identify the available charging pile groups and electric vehicle user priority weights.

[0100] Specifically, the set of charging piles in normal state can be retained by excluding the faulty node and its directly affected adjacent nodes. As a set of available charging piles, the electric vehicle user priority weight is then defined, specifically: the electric vehicle user priority level is mapped to the weight coefficient Then, add the charging urgency correction factor (For example, when the remaining power is less than 20% ).

[0101] Comprehensively obtain the priority weight of electric vehicle users .

[0102] Step Sc3: Allocate dynamic power quota.

[0103] In the embodiment of the present application, allocating dynamic power quotas mainly includes allocating initial quotas according to the priority weight ratio of electric vehicle users and iteratively adjusting to meet local constraints. Among them, allocating initial quotas according to the priority weight ratio of electric vehicle users can be calculating the initial power quota of each charging pile. ,in, For charging piles The hardware power limit of the iterative adjustment is to meet the local constraints, which mainly include constraints 1 and 2. Then, according to the above constraints 1 and 2, the rules are adjusted. That is, if a node , then set its quota to and the remaining power Redistribute to other nodes according to the priority weight of electric vehicle users, and repeat the iteration until all nodes meet the constraints. Constraint 1 is that the power of a single node does not exceed its hardware limit. , constraint 2 means the total power of the cluster does not exceed .

[0104] Step Sc4: Verify power coupling compensation and stability.

[0105] Specifically, the node can be calculated based on the power coupling model. Sensitivity of power adjustment to other nodes , correct the actual power change according to the following formula:

[0106] in, Representation node The power adjustment amount, Indicates that it is different from the node Then, edge computing nodes are deployed to monitor grid frequency fluctuations. , that is, if , then emergency load shedding is triggered, and the power quota is gradually reduced from low to high priority, among which, is the preset frequency threshold.

[0107] Step Sc5: Update the dynamic scheduling engine and user notification.

[0108] Specifically, the power quota that will be finally allocated The data is sent back to the dynamic scheduling engine to update the charging queue order and push the new charging plan through the user terminal, including the adjusted power value, estimated charging completion time and compensation points, etc. If the user refuses to migrate to the backup pile, the new quota is applied and the charging power is limited to .

[0109] It can be seen from step S105 and step S106 of the above embodiment that during the charging process, through the real-time collection of multi-dimensional operating data and the identification of abnormal characteristics, the power redistribution strategy can be quickly triggered, the power supply parameters of adjacent charging piles can be dynamically adjusted, the fault impact can be isolated in time and the continuity of charging service can be maintained, thereby ensuring the safety of the charging process and the overall reliability of the system, and the use of encrypted communication links realizes secure data transmission between the charging pile monitoring node and the cloud platform.

[0110] 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.

[0111] Due to the reliance on a centralized billing system, there may be a lack of trust and auditing difficulties. Therefore, in order to solve the problems of billing transparency and data credibility, in an embodiment of the present 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. Among them, the smart contract can automatically execute billing rules and reduce human operational errors, while the distributed ledger can prevent single-point data tampering and improve dispute resolution efficiency. In the above embodiment, the generation of the blockchain smart contract can be: dividing the key events of the charging process into multiple blocks according to the timestamp, each block contains the hash value of the previous block, forming an unalterable hash chain; before the block is submitted to the distributed ledger, the following operations are performed: requiring the user, the charging operator and the grid regulator to verify the continuity of the hash chain of the current block; when at least two of the three parties sign and confirm, the block is written to the candidate chain; for the billing calculation logic, a zero-knowledge proof is generated containing the following elements: a proof of the correctness of the charge calculation of the current block and a commitment to the anonymity of the user's privacy data (such as identity information, charging location, etc.); the proof of the correctness of the charge calculation of the current block is compared with the proof in the candidate chain. After the block is bound, it is submitted to the main chain. For the billing calculation logic, a zero-knowledge proof containing the proof of correctness of the current block's charge calculation and the anonymity commitment of the user's privacy data can be generated by: converting the charge calculation formula into a verifiable arithmetic circuit and generating a commitment parameter containing a circuit structure hash; allowing the user to verify the following based on the commitment parameter without exposing the details of electricity usage: the charge calculation complies with the preset formula and the privacy data remains encrypted during the calculation process; when the user objects to the billing result, the on-chain arbitration node is used to extract the commitment parameters and circuit structure of the disputed block, verify the integrity of the computing environment and the current charge calculation process based on the previous hash value, and generate an arbitration proof, etc.

[0112] It can be seen from step S106 and step S107 of the above embodiment that an encrypted communication link is used to realize secure data transmission between the charging pile monitoring node and the cloud platform, and the key parameters of the charging process are distributedly stored in combination with the blockchain smart contract. 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, thereby reducing user disputes.

[0113] Furthermore, in order to combine equipment operation data with the LSTM model to predict the probability of failure, realize preventive maintenance, and thus reduce the sudden failure rate of equipment, the charging pile reservation charging method of the above embodiment may also include: analyzing historical fault data through the LSTM neural network to establish an equipment health prediction model, wherein the equipment health prediction model outputs the predicted failure probability and confidence interval; collecting the harmonic distortion rate, contactor operation times and temperature rise rate of the charging pile, inputting 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 the 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 dispatching resources; dynamically adjusting the charging queue sorting to reduce the power allocation weight of the fault risk pile position; pushing the charging pile health status prompt to the associated user terminal, etc. In order to improve the accuracy and generalization ability of fault prediction, federated learning and digital twin technology can also be used to continuously optimize the equipment health prediction model, including: collecting on-site maintenance records, building a mapping relationship database containing fault characteristics, 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 the latent space representation of fault characteristics, and aggregating model gradients through secure multi-party computing to update global model parameters; performing the following operations through the digital twin system: simulating the fault evolution path based on the mapping relationship database, injecting simulation data into the health prediction model for adversarial training, and transmitting the optimized model parameters back to each edge node.

[0114] Furthermore, the charging pile reservation charging method of the above embodiment may also include an emergency response mechanism, namely: when an emergency peak-shaving instruction from the power grid is received, a rapid load reduction program is started; different power reduction priorities are set according to the user contract type and the purpose of the vehicle; charging points compensation is provided to affected users, and alternative charging solutions are recommended; wherein the rapid load reduction program may be: calculating the adjustable power capacity of the charging pile cluster and generating a multi-level reduction plan; giving priority to reducing the charging power of low-priority users and retaining the power supply for emergency service vehicles; calling vehicles with discharge capabilities through V2G technology to support the local power grid, and so on.

[0115] Furthermore, the charging pile reservation charging method of the above embodiment may also include: displaying a three-dimensional navigation interface of the charging pile on the user terminal, superimposing real-time available status information; recommending the optimal arrival route and charging time combination based on the user's driving habits; providing a virtual charging experience function, simulating the difference in charging costs in different time periods, etc.

[0116] From the above attached Figure 1From the example charging pile reservation charging method, it can be seen that, on the one hand, the dynamic scheduling engine is used to integrate the grid time-sharing load data and the charging pile group status data in real time, and the charging queue sorting is generated by combining the multi-objective optimization algorithm, so that 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; on the other hand, during the charging process, through the real-time collection and abnormal pattern recognition of multi-dimensional operating data, the power redistribution strategy can be quickly triggered, the power supply parameters of adjacent charging piles can be dynamically adjusted, the fault impact can be isolated in time and the continuity of charging service can be maintained, thereby ensuring the safety of the charging process and the overall reliability of the system; thirdly, an encrypted communication link is used to realize secure data transmission between the charging pile monitoring node and the cloud platform, and the key parameters of the charging process are distributedly stored in combination with blockchain smart contracts, which 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.

[0117] Please see the attached Figure 2 , is a charging pile reservation charging device provided in an embodiment of the present application, the device may include a receiving module 201, an acquisition module 202, a first generation module 203, a sending module 204, a transmission module 205, a trigger module 206 and a second generation module 207, which are described in detail as follows: The receiving module 201 is configured to receive a charging reservation request sent by a user terminal, wherein the charging reservation request includes a target charging pile identifier; The acquisition module 202 is used to obtain the time-sharing 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-sharing load data and real-time status data into the dynamic scheduling engine and generate the charging queue ranking through a multi-objective optimization algorithm; 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 sorting, and simultaneously 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; A trigger module 206 is configured to identify abnormal patterns in the multi-dimensional operating data and trigger a power reallocation 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.

[0118] From the above attached Figure 2 From the example charging pile reservation charging device, it can be seen that, on the one hand, the dynamic scheduling engine integrates the grid time-sharing load data and the charging pile group status data in real time, and combines the multi-objective optimization algorithm to generate the charging queue sorting, so that the charging task allocation can dynamically adapt to the grid carrying capacity and equipment operating status, effectively balance the grid load, avoid the risk of local overload, and at the same time, by optimizing the spatiotemporal resource allocation of charging piles, significantly improve the utilization rate of charging piles; on the other hand, during the charging process, through the real-time collection and abnormal pattern recognition of multi-dimensional operation data, it can quickly trigger the power redistribution strategy, dynamically adjust the power supply parameters of adjacent charging piles, timely isolate the fault impact and maintain the continuity of charging service, thereby ensuring the safety of the charging process and the overall reliability of the system; thirdly, an encrypted communication link is used to realize secure data transmission between the charging pile monitoring node and the cloud platform, and the key parameters of the charging process are distributedly stored in combination with blockchain smart contracts, which 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.

[0119] Figure 3 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 3 As shown, 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 a charging pile reservation charging method. When the processor 30 executes the computer program 32, the steps in the above-mentioned charging pile reservation charging method embodiment are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of the modules / units in the above-mentioned device embodiments are realized, 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.

[0120] Exemplarily, a computer program 32 for a charging pile reservation method includes: receiving a charging reservation request sent by a user terminal, wherein the charging reservation request includes a target charging pile identifier; obtaining time-sharing load data of the current power grid based on a power grid load forecasting model, and collecting real-time status data of the target charging pile group through an Internet of Things interface; inputting the time-sharing load data and real-time status data into a dynamic scheduling engine, and generating a charging queue ranking using a multi-objective optimization algorithm; sending a charging authorization instruction to the target charging pile corresponding to the target charging pile identifier based on the charging queue ranking, and simultaneously updating the reservation status identifier on the charging pile interaction interface; after the target charging pile receives the charging authorization instruction and starts charging, collecting multi-dimensional operating data of the charging pile and uploading it to a cloud platform in real time via an encrypted communication link; performing abnormal pattern recognition on the multi-dimensional operating data, and triggering a power reallocation strategy when abnormal characteristics are detected; and upon completion or abnormal termination of charging, generating a billing voucher based on a blockchain smart contract, and writing 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 of which are stored in a memory 31 and executed by a processor 30 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the electronic device 3. For example, the computer program 32 may 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 trigger module 206, and a second generation module 207 (modules in the virtual device). The specific functions of each module are as follows: a receiving module 201 is used to receive a charging reservation request sent by a user terminal, wherein the charging reservation request includes a target charging pile identifier; an acquisition module 202 is used to obtain the time-sharing load data of the current power grid based on a power grid load forecasting model, and collect the real-time status data of the target charging pile group through an Internet of Things interface; a first generation module 203 is used to input the time-sharing load data and the real-time status data into a dynamic scheduling engine, and through multiple The target optimization algorithm generates a charging queue sorting; 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 sorting, and synchronously update the reservation status identifier of the charging pile interaction interface; the transmission module 205 is used to collect the 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 identify abnormal patterns in the multi-dimensional operation data, and trigger the power reallocation 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.

[0121] The electronic device 3 may include but is not limited to a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of electronic device 3 and does not constitute a limitation of electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0122] The processor 30 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0123] The memory 31 can be an internal storage unit of the electronic device 3, such as the hard drive or memory of the electronic device 3. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 31 can include both the internal storage unit of the electronic device 3 and an external storage device. 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 is about to be output.

[0124] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by 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 embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0125] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0126] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0127] In the embodiments provided in this application, it should be understood that the disclosed devices / equipment and methods can be implemented in other ways. For example, the device / equipment embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0128] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0129] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0130] If the integrated module / unit is implemented in the form of 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 present application implements all or part of the processes in the above-mentioned method embodiments, and a computer program can also be used to instruct related hardware to complete the process. The computer program of the charging pile reservation charging method can be stored in a storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned 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 time-sharing load data of the current power grid based on a power grid load forecasting model, and collecting real-time status data of the target charging pile group through an Internet of Things interface; inputting the time-sharing load data and real-time status data into a dynamic scheduling engine, and generating a charging queue ranking using a multi-objective optimization algorithm; sending a charging authorization instruction to the target charging pile corresponding to the target charging pile identifier based on the charging queue ranking, and simultaneously updating the reservation status identifier of the charging pile interactive interface; after the target charging pile receives the charging authorization instruction and starts charging, collecting multi-dimensional operating 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 operating data, and triggering a power reallocation strategy when abnormal characteristics are detected; 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. Computer programs include computer program code, which may be in source code, object code, executable files, or some intermediate form. Storage media may include any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunications signals, and software distribution media. It should be noted that the content of storage media may be appropriately expanded or reduced based on the requirements of legislation and patent practice within a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, storage media do not include electric carrier signals and telecommunications signals.

[0131] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application. The specific implementation methods described above further explain the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only the specific implementation method of the present application and is not used to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included in the protection scope of the present 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, wherein the charging reservation request includes a target charging pile identifier; Obtain the current grid's time-sharing load data based on the grid load forecasting model, and collect real-time status data of the target charging pile group through the Internet of Things interface; Input the time-sharing load data and real-time status data into a dynamic scheduling engine, and generate a charging queue ranking using 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 sorting, and synchronously updating the reservation status identifier of the charging pile interaction interface; After the target charging pile receives the charging authorization instruction 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; performing abnormal pattern recognition on the multi-dimensional operating data, and triggering a power reallocation strategy when abnormal features are detected; When charging is completed or terminated abnormally, 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.

2. The charging pile reservation charging method according to claim 1, characterized in that: The charging reservation request further includes a user priority level. Inputting the time-sharing load data and real-time status data into a dynamic scheduling engine and generating a charging queue ranking using a multi-objective optimization algorithm includes: generating a first weight coefficient matrix according to the user priority level; Calculating the grid carrying margin based on the time-sharing load data and generating a second weight coefficient matrix; Fusion of the first weight coefficient matrix and the second weight coefficient matrix by fuzzy analytic hierarchy process to construct a dynamic ranking function; The dynamic sorting function is iteratively optimized to generate a charging sequence that satisfies Pareto optimality.

3. The charging pile reservation charging method according to claim 2, characterized in that: The iterative optimization of the dynamic sorting function to generate a charging sequence that satisfies Pareto optimality includes: When a sudden load change exceeding a threshold is detected in the power grid, load balancing constraints are automatically inserted; According to the user priority level, setting an exemption factor for the charging request of the high priority user; A simulated annealing algorithm is used to solve the optimal sorting scheme under the load balancing constraint condition, and the charging sequence that satisfies Pareto optimality is obtained.

4. The charging pile reservation charging method according to claim 1, characterized in that: The charging authorization instruction includes a dynamically adjusted starting time window, and the generation of the dynamically adjusted starting time window includes: Analyze the scheduled task sequence of each charging pile based on the charging queue sorting, and calculate the original idle time period between adjacent tasks of each charging pile; According to the fluctuation range of the arrival time predicted by the user terminal positioning data, a buffer period is added before the starting point of the original idle period to form an adjusted available period; When overlapping adjusted available time periods of multiple users are detected, priority arbitration is performed as follows: Maintaining the adjusted availability time for high-priority users; Shifting the starting point of the available time period of the low-priority user backward by the length of its own buffer time period; Update the status of the charging pile to conflict resolution mode.

5. The charging pile reservation charging method according to claim 1, characterized in that: The power authorization instruction further includes a maximum allowable power value, and the performing of abnormal pattern recognition on the multi-dimensional operation data and triggering a power reallocation strategy when abnormal features are detected include: Establishing a power coupling model for the charging pile cluster based on the maximum allowable power value and calculating the power adjustable range of each node; Identify the fault impact radius of the abnormal charging pile and determine the set of affected adjacent charging piles; For the affected set of adjacent charging piles, a local power adjustment plan is generated according to the power adjustable range of each node and the upper limit of the power grid load, and millisecond-level power redistribution is implemented through the edge computing node.

6. The charging pile reservation charging method according to claim 5, characterized in that: Generating a local power regulation scheme and implementing millisecond-level power redistribution through edge computing nodes includes: Sending a power adjustment negotiation request to the affected user terminal; Dynamically calculate the power reduction gradient value based on the acceptance level of user feedback; Perform power adjustment corresponding to the power reduction gradient value.

7. The charging pile reservation charging method according to claim 1, characterized in that: The establishment of the encrypted communication link includes: Issue a unique digital certificate for each charging station through the PKI / CA system, implement two-way authentication in the TCP / TLS handshake protocol, and generate session keys; Based on the session key, dynamically sharding and encrypting the collected multi-dimensional operation data; Monitor transmission delay and packet loss rate in real time and dynamically adjust the complexity of the encryption algorithm.

8. A charging pile reservation charging device, characterized in that: The device comprises: A receiving module, configured to receive a charging reservation request sent by a user terminal, wherein the charging reservation request includes a target charging pile identifier; The acquisition module is used to obtain the time-sharing load data of the current power grid based on the power grid load forecasting model, and collect the real-time status data of the target charging pile group through the Internet of Things interface; A first generation module is configured to input the time-sharing load data and real-time status data into a dynamic scheduling engine and generate a charging queue ranking using a multi-objective optimization algorithm; A sending module, configured to send a charging authorization instruction to a target charging pile corresponding to the target charging pile identifier according to the charging queue sorting, and synchronously update a reservation status identifier on the charging pile interaction interface; A transmission module is used to collect multi-dimensional operating data of the charging pile after the target charging pile receives the charging authorization instruction and starts charging, and upload the data to the cloud platform in real time via an encrypted communication link; a trigger module, configured to identify abnormal patterns in the multi-dimensional operating data and trigger a power reallocation strategy when abnormal features are detected; The second generation module 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.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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