Big data-based intelligent operation management method and system for charging piles

Abstract

The application discloses a charging pile intelligent operation management method and system based on big data, comprising: collecting and preprocessing multi-source heterogeneous data to generate a real-time state set; establishing a priority certificate contract template, generating constraint and price parameters and initializing in a generated contract; receiving a user charging request, calling a feasible domain prover to output a deliverability certificate; calculating a waiting interval, a station arrival time window and a migration incentive to generate a reservation and queuing instruction; executing a provable fair scheduling under constraints, allocating power and queue and interrupting or renewing; monitoring operation data and evaluating risks, performing cross-station migration and difference compensation when exceeding a threshold; settling the contract, and compensating or compensating according to the repurchase parameters when the target power is not reached or the time is exceeded. The application aims to realize efficient utilization of the charging pile network and significant reduction of user waiting time through big data driven provable fair scheduling and cross-station migration optimization.

Description

Technical Field

[0001] This invention relates to the field of smart energy management technology, and in particular to a smart operation and management method and system for charging piles based on big data. Background Technology

[0002] Currently, with the rapid growth in the number of new energy vehicles, charging piles are playing an increasingly important role in urban transportation and energy systems. Existing charging pile operation and management systems are mostly centered around a single site, primarily focusing on the availability of charging equipment, power control, and simple queuing scheduling. However, traditional methods have significant shortcomings in areas such as multi-site collaborative operation, cross-site migration, dynamic pricing, and ensuring user experience. Especially when facing fluctuations in charging demand, grid load limitations, and sudden congestion, existing systems lack the ability to make real-time predictions and dynamic optimizations based on multi-source heterogeneous data, making it difficult to balance resource utilization efficiency and user service quality.

[0003] In existing technologies, the collection and processing of multi-source heterogeneous data mainly focuses on the aggregation and analysis of basic information such as power, temperature, and transaction records. It lacks the ability to deeply integrate cross-system data centered on charging piles, failing to fully integrate vehicle status, user behavior, external environment, and power grid dispatch information. This results in lagging dispatch strategies and inaccurate cross-site resource matching. Furthermore, existing contract and settlement mechanisms largely rely on manual or fixed rule execution, lacking a verifiable smart contract system for cross-site migration and fair dispatch. This makes it impossible to automatically verify and constrain the feasibility, deliverability, and fairness of migration, easily leading to disputes or resource waste.

[0004] Current cross-site migration decision rules are mostly based on static or empirical parameters, making it difficult to dynamically balance the load of different sites and the waiting costs for users. They also lack innovative mechanisms such as randomized fair scheduling and fair regret control, which can easily cause some users to be in a disadvantageous position for a long time, affecting the overall operational fairness and satisfaction.

[0005] Therefore, how to provide intelligent operation and management methods and systems for charging piles based on big data is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] One objective of this invention is to propose a smart operation and management method and system for charging piles based on big data. This invention integrates technologies such as real-time acquisition and intelligent processing of multi-source heterogeneous operational data, feasibility assessment and incentive calculation for cross-site migration, randomized fair scheduling, and deliverability settlement, forming a comprehensive smart operation mechanism that spans data monitoring, dynamic scheduling, smart contract execution, and result settlement. By introducing an innovative contract feasibility domain prover, it achieves automated verification of feasibility and fairness before cross-site migration; combined with randomized rules and fair regret control, it dynamically balances the load of each site and user waiting costs, improving scheduling fairness; and utilizes a deliverability certificate mechanism to automatically compensate or pay out for contracts that fail to meet agreed-upon indicators, protecting user rights. This invention possesses advantages such as high data integration, strong scheduling fairness, high resource utilization, and excellent user experience, enabling intelligent, sustainable, and efficient allocation of charging resources under different operating environments.

[0007] The intelligent operation and management method for charging piles based on big data according to embodiments of the present invention includes: Collect multi-source heterogeneous operation data covering charging piles, vehicles, user transactions, external environment and power grid, preprocess the multi-source heterogeneous operation data to form a real-time status set; Establish a priority certificate contract template, extract power distribution capacity, cable temperature rise limit, demand window and vehicle charge safety range based on real-time status set, form a set of constraint parameters and a set of price parameters, set cross-site transfer rules, initialize the set of existing contracts, and load the registered and unsettled contract records. Receive user charging requests, map them to candidate priority certificate contracts, call the contract feasibility domain prover composed of certificate network and feasibility projection layer to determine feasibility, and generate a deliverability certificate. Based on the real-time state set, price parameter set, and in-service contract set, calculate the commitment waiting time interval, arrival time window, and cross-station migration incentive parameters, and generate reservation and queuing control instructions. Under the constraints of the parameter set, provably fair scheduling is performed on the set of in-life contracts. According to the randomization rule that prioritizes the contract deadline and the fair regret objective, the power allocation of the charging gun port, the queue order, and the interruption and renewal instructions are calculated. Monitor power, temperature and metering information in multi-source heterogeneous operation data, assess default and congestion risks, and when the preset threshold is reached, calculate the target site for cross-site migration and the compensation amount based on the deliverability certificate and cross-site migration incentive parameters, and implement migration and resource reservation. Settlement will be completed based on the priority certificate contract template and the certifiable fair scheduling and migration execution results. For contracts that fail to reach the target incremental electricity volume or exceed the promised waiting time interval, compensation or payment will be made according to the repurchase parameters.

[0008] Optionally, the multi-source heterogeneous operation data specifically includes power, temperature, metering, order, meteorological, traffic and energy storage data collected in real time by charging piles, connected vehicles, user transaction systems, external environmental monitoring devices and power grid interaction interfaces.

[0009] Optionally, the preprocessing of multi-source heterogeneous operating data specifically includes performing time synchronization, outlier removal, missing value filling, dimension unification, and normalization on the multi-source heterogeneous operating data.

[0010] Optionally, the establishment of the priority certificate contract template involves extracting power distribution capacity, cable temperature rise limits, demand windows, and vehicle charge safety ranges based on the real-time status set, forming a set of constraint parameters and a set of price parameters, setting inter-site transfer rules, initializing the set of existing contracts, and loading registered but unsettled contract records, including: Establish a priority certificate contract template, adopt a structured field assembly method, select and combine items from the predefined field library to form the content of the priority certificate contract template, and complete consistency verification and priority certificate contract template version number generation; Based on the real-time state set, a constraint extraction list is constructed. The distribution capacity, cable temperature rise limit, demand window and vehicle charge safety range are extracted in the order of priority of power grid agreement, equipment nameplate and operation monitoring to form initial constraint items. The initial constraint entries are validated and conflict resolved to obtain a set of constraint parameters; Load the time-of-use basic electricity price table, priority fee table, cross-site migration supplementary unit price table and repurchase compensation benchmark table, combine the load period and energy storage availability in the real-time status set to generate a price parameter set and establish the effective period and site range; Set cross-site transfer rules, clarifying the scope of transferable sites, transfer triggering conditions, cross-site reserved time window, target site resource locking method, migration compensation criteria, contract status migration process, and the order of failure rollback and automatic compensation, and establish a one-to-one correspondence with the price parameter set, constraint parameter set, and priority certificate contract template fields; Initialize the collection of live contracts, import all registered but unsettled contract records, and create an index by site and contract expiration time. At the same time, supplement the records with the corresponding deliverability certificate reference information, cross-site reserved time window number and price parameter version number.

[0011] Optionally, the receipt of a user charging request, mapped to a candidate priority credential contract, and the invocation of a contract feasibility domain prover composed of a certificate network and a feasibility projection layer to determine feasibility and generate a deliverability certificate, includes: Receive user charging requests, parse user charging requests according to priority certificate contract template, assemble candidate priority certificate contracts, and fill in target incremental electricity, deadline, interruptibility level, cross-site transferability parameters, repurchase parameters, commitment waiting time interval and arrival time window. Read the real-time state set, constraint parameter set, and in-life contract set to form the feasibility determination input; Establish a contract feasibility domain prover: Training samples and stress scenario sets are constructed based on historical order records, charging pile operation monitoring, demand window records, vehicle charging safety range and cross-station transfer rules; The training certificate network outputs deliverable conclusions and confidence levels, and assembles electrical constraint projection, thermal constraint projection, demand window projection, and charged safety projection into a hierarchical feasibility projection sequence in a fixed order, setting confidence thresholds and triggering sequences. The contract feasibility domain prover is invoked to make the first determination on the candidate priority certificate contract. When the deliverability conclusion of the certificate network is undeliverable, alternative parameter suggestions are given. When the confidence of the certificate network is lower than the set threshold, the hierarchical feasibility projection correction is initiated. When the certificate network is deliverable and the confidence reaches the set threshold, it is directly approved. Perform hierarchical feasibility projection correction, adjusting the fields of the candidate priority certificate contract item by item in the order of electrical constraint projection, thermal constraint projection, demand window projection and charge safety projection, to generate the corrected alternative priority certificate contract; Generate a deliverability certificate, clearly record the parameters of the alternative priority certificate contract, the deliverability margin, the worst-case boundary, the effective period and the reference index, register the corresponding contract as an in-life contract and add it to the in-life contract set, and write the deliverability certificate reference in the priority certificate contract template.

[0012] Optionally, the step of calculating the committed waiting time interval, arrival time window, and cross-station migration incentive parameters based on the real-time state set, price parameter set, and in-service contract set, and generating reservation and queuing control instructions, includes: Read the real-time status set, price parameter set, and active contract set to determine the current available power, active contract occupation time, arrival request density, and number of idle gun ports; Based on the deadline and occupied time period of the existing contract set, combined with the current available power and the number of idle gun ports, generate the in-station arrival time prediction sequence and queue length prediction sequence, calculate the lower bound and upper bound of the waiting time, form the committed waiting time interval, and mark the effective time period and station range. Based on the promised waiting time interval and the deadline of the set of in-life contracts, an arrival time window is generated, specifying the arrival start time, arrival deadline, and timeout handling method. Based on the current available power, the period of occupation of existing contracts, and the promised waiting time interval of each candidate site, calculate the cross-site migration incentive parameters, clarify the target site, the amount of compensation, the cross-site reserved time window and the resource lock duration, and record the correspondence with the set of existing contracts. Based on the promised waiting time interval, arrival time window, and cross-station migration incentive parameters, reservation and queuing control instructions are generated and sent to the user side and the charging pile side. At the same time, reference identifiers for the promised waiting time interval field and the arrival time window field are written into the priority certificate contract template.

[0013] Optionally, the step of performing provably fair scheduling on the set of in-life contracts under the constraint parameter set, and calculating charging gun power allocation, queue order, interruption and renewal instructions according to a randomization rule prioritizing contract deadlines and a fair regret objective, includes: Read the constraint parameter set, the set of existing contracts, the promised waiting time interval, the arrival time window and the deliverability certificate reference, determine the time discrete set and the charging gun set, and obtain the target incremental power, deadline, interruptibility level and allowable interruption interval for each existing contract; Based on the real-time status set and deliverability certificate, the system generates a time-varying available power sequence within the station, peak power of each charging port, power distribution capacity margin, cable temperature rise margin, and effective range of demand window, and forms a waiting time prediction and a promised waiting upper bound. Randomization priority rule for the three factors of certificate margin, commitment risk, and cutoff margin; Under the constraints of single service per charging port, power not exceeding peak value, total station power not exceeding available power, temperature rise not exceeding limits, cumulative energy supply before contract expiration not less than target incremental power, and interruptions only within the allowable interruption range, calculate the power allocation and queuing order of charging ports. A quantile fairness regret target is introduced, grouped by interruptibility level, with the goal of reducing the high quantile threshold of excess waiting in each group. When the threshold increases, the commitment risk weight in the three-factor randomization priority rule is increased and the cutoff margin weight is decreased. At the same time, interruption and renewal instructions are generated and the effective period is recorded.

[0014] The intelligent operation and management system for charging piles based on big data according to an embodiment of the present invention includes the following modules: The data acquisition and preprocessing module is used to collect multi-source heterogeneous operational data and perform preprocessing to form a real-time status set; The constraint pricing module is used to extract power distribution capacity, cable temperature rise limit, demand window and vehicle charge safety range based on real-time status set, and generate constraint parameter set and price parameter set; The contract determination module is used to generate priority certificate contract templates and build a contract feasibility domain prover to determine feasibility and generate a deliverability certificate. The relocation decision module is used to perform cross-site migration target site selection, supplementary amount calculation and migration plan formulation based on the deliverability certificate and cross-site migration incentive parameters. The randomized scheduling module is used to introduce randomization rules and fair regret objectives to optimize resource allocation and scheduling results; The monitoring and risk response module is used to monitor power, temperature and metering information in real time, assess default and congestion risks, and trigger migration and resource reservation operations when thresholds are reached. The settlement and compensation module is used to complete the settlement based on the priority certificate contract template and the results of fair scheduling and migration, and to perform compensation or payment according to the repurchase parameters for contracts that have not reached the incremental electricity or have exceeded the waiting time.

[0015] The beneficial effects of this invention are: This invention achieves closed-loop management of the entire process of intelligent operation of charging piles by integrating multi-source heterogeneous data acquisition, contract feasible domain proof, randomized fair scheduling and deliverability settlement mechanism. Compared with the existing technology, it significantly improves the system's capabilities in dynamic load balancing, fair user experience and resource utilization efficiency.

[0016] By automating the assessment of the feasibility and fairness of cross-site migration through a contract feasibility domain prover, the risks of migration failure and unfair scheduling can be effectively avoided before execution. Randomized fair scheduling combined with fair regret control can dynamically balance waiting time and charging resource allocation among different sites and users, improving the fairness and flexibility of overall scheduling. The deliverability certificate mechanism ensures the consistency between the contract execution results and the promises, and can automatically trigger compensation or payment for situations where target indicators are not met, thereby protecting user rights and enhancing the platform's credibility.

[0017] This invention not only achieves real-time intelligent management during operation, but also improves user satisfaction and operational sustainability through a refined settlement and compensation mechanism. It has advantages such as strong scalability, good portability and flexible deployment. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 The flowchart shows the intelligent operation and management method for charging piles based on big data proposed in this invention. Figure 2 This is a schematic diagram of the structure of the intelligent operation and management system for charging piles based on big data proposed in this invention. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0020] refer to Figure 1 The intelligent operation and management method for charging piles based on big data includes: Collect multi-source heterogeneous operation data covering charging piles, vehicles, user transactions, external environment and power grid, preprocess the multi-source heterogeneous operation data to form a real-time status set; Establish a priority certificate contract template, extract power distribution capacity, cable temperature rise limit, demand window and vehicle charge safety range based on real-time status set, form a set of constraint parameters and a set of price parameters, set cross-site transfer rules, initialize the set of existing contracts, and load the registered and unsettled contract records. Receive user charging requests, map them to candidate priority certificate contracts, call the contract feasibility domain prover composed of certificate network and feasibility projection layer to determine feasibility, and generate a deliverability certificate. Based on the real-time state set, price parameter set, and in-service contract set, calculate the commitment waiting time interval, arrival time window, and cross-station migration incentive parameters, and generate reservation and queuing control instructions. Under the constraints of the parameter set, provably fair scheduling is performed on the set of in-life contracts. According to the randomization rule that prioritizes the contract deadline and the fair regret objective, the power allocation of the charging gun port, the queue order, and the interruption and renewal instructions are calculated. Monitor power, temperature and metering information in multi-source heterogeneous operation data, assess default and congestion risks, and when the preset threshold is reached, calculate the target site for cross-site migration and the compensation amount based on the deliverability certificate and cross-site migration incentive parameters, and implement migration and resource reservation. Settlement will be completed based on the priority certificate contract template and the certifiable fair scheduling and migration execution results. For contracts that fail to reach the target incremental electricity volume or exceed the promised waiting time interval, compensation or payment will be made according to the repurchase parameters.

[0021] In this embodiment, the multi-source heterogeneous operation data specifically includes power, temperature, metering, order, meteorological, traffic and energy storage data collected in real time by charging piles, connected vehicles, user transaction systems, external environmental monitoring devices and power grid interaction interfaces.

[0022] In this embodiment, the preprocessing of multi-source heterogeneous operating data specifically includes time synchronization, outlier removal, missing value filling, unit unification, and normalization of the multi-source heterogeneous operating data.

[0023] In this embodiment, the establishment of the priority certificate contract template involves extracting power distribution capacity, cable temperature rise limits, demand windows, and vehicle charge safety ranges based on the real-time status set, forming a set of constraint parameters and a set of price parameters, setting inter-site transfer rules, initializing the in-progress contract set, and loading registered but unsettled contract records, including: A priority certificate contract template is established. A structured field assembly method is adopted, and the content of the priority certificate contract template is formed by selecting and combining items from the predefined field library one by one. Consistency verification and priority certificate contract template version number generation are completed. The content of the priority certificate contract template includes contract number, associated charging pile number, target incremental electricity, deadline, interruptibility level, cross-site transferable parameters, repurchase parameters, commitment waiting time interval field, arrival time window field, deliverability certificate reference field, cross-site reserved time window number, migration compensation caliber identifier and settlement caliber identifier. Based on the real-time state set, a constraint extraction list is constructed. The distribution capacity, cable temperature rise limit, demand window and vehicle charge safety range are extracted in the order of priority of power grid agreement, equipment nameplate and operation monitoring to form initial constraint items. The initial constraint entries are validated and conflict resolved to obtain a set of constraint parameters. The set of constraint parameters includes the effective value of power distribution capacity, the effective value of cable temperature rise limit, the effective range of demand window and the effective range of vehicle charge safety interval, and a reference mapping is established with the priority certificate contract template fields. Load the time-of-use basic electricity price table, priority fee table, cross-site migration supplementary unit price table and repurchase compensation benchmark table, combine the load period and energy storage availability in the real-time status set to generate a price parameter set and establish the effective period and site range; Set cross-site transfer rules, clarifying the scope of transferable sites, transfer triggering conditions, cross-site reserved time window, target site resource locking method, migration compensation criteria, contract status migration process, and the order of failure rollback and automatic compensation, and establish a one-to-one correspondence with the price parameter set, constraint parameter set, and priority certificate contract template fields; Initialize the collection of live contracts, import all registered but unsettled contract records, and create an index by site and contract expiration time. At the same time, supplement the records with the corresponding deliverability certificate reference information, cross-site reserved time window number and price parameter version number.

[0024] This invention achieves end-to-end data-driven management of smart charging pile operation by organically combining priority certificate contract templates, real-time constraint extraction, price parameter binding, cross-site migration decision-making, and global indexing of existing contracts. It uses structured field assembly to generate standardized contract templates, ensuring the accuracy of contract generation and invocation. Based on grid access protocols, equipment nameplates, and operational monitoring data, it automatically extracts distribution capacity, cable temperature rise limits, demand windows, and safety intervals, and synchronously binds them with time-of-use pricing, supplementary unit prices, and other price information, providing real-time and reliable parameter support for dispatching. When power, temperature rise, or metering deviations reach thresholds, the system can quickly locate the target site for cross-site migration based on the deliverability certificate and instantly calculate the supplementary amount, achieving minute-level resource allocation. By establishing an index for unsettled contracts by site and deadline and supplementing key reference information, it ensures that the settlement process can quickly match and automatically calculate compensation or payment amounts. This invention effectively reduces manual intervention and calculation delays, improves the response speed and resource utilization of operation dispatching, and ensures the safe operation of the power grid and equipment.

[0025] In this embodiment, receiving a user charging request is mapped to a candidate priority credential contract. A feasibility determination is performed by calling a contract feasibility domain prover composed of a certificate network and a feasibility projection layer, generating a deliverability certificate, including: Receive user charging requests, parse user charging requests according to priority certificate contract template, assemble candidate priority certificate contracts, and fill in target incremental electricity, deadline, interruptibility level, cross-site transferability parameters, repurchase parameters, commitment waiting time interval and arrival time window. Read the real-time state set, constraint parameter set, and in-life contract set to form the feasibility determination input; Establish a contract feasibility domain prover: Training samples and stress scenario sets are constructed based on historical order records, charging pile operation monitoring, demand window records, vehicle charging safety range and cross-station transfer rules; The training certificate network outputs deliverable conclusions and confidence levels, and assembles electrical constraint projection, thermal constraint projection, demand window projection, and charged safety projection into a hierarchical feasibility projection sequence in a fixed order, setting confidence thresholds and triggering sequences, wherein: Electrical constraint projection refers to the normalization and mapping of real-time collected power grid distribution capacity, charging pile rated power, and line current to reflect the degree of matching between the current operating status and the electrical safety range, and to form a multi-dimensional vector that can quantify electrical feasibility. Thermal constraint projection refers to comparing the real-time collected charging pile power load, power distribution cable temperature rise, ambient temperature, and heat dissipation capacity with the temperature rise limit and environmental adaptability range specified on the equipment nameplate. Through normalization and interval mapping, the current thermal state is converted into a multi-dimensional vector reflecting the thermal safety margin. Demand window projection refers to matching the real-time collected site load curves, electricity consumption plan periods, and demand limits issued by the power grid dispatch to determine the available power space in the current and subsequent periods. Combined with historical load fluctuation characteristics, the available capacity and the upper limit of demand are mapped into a vector reflecting the resource sufficiency of the period. Charge safety projection refers to comparing the vehicle's real-time state of charge, charging rate, and battery temperature data with pre-set safe charging range and electrochemical characteristic parameters, mapping them into a multi-dimensional feasibility vector. The contract feasibility domain prover is invoked to make an initial judgment on the candidate priority certificate contract. When the deliverability conclusion of the certificate network is undeliverable, alternative parameter suggestions are given. When the confidence of the certificate network is lower than a set threshold, the hierarchical feasibility projection correction is initiated. When the certificate network is deliverable and the confidence reaches the set threshold, it is directly approved. Here, the certificate network refers to the set of rules and calculation units constructed internally by the contract feasibility domain prover. The judgment logic of multiple constraint projections such as electrical, thermal, demand window and charge safety are invoked in sequence to perform comprehensive calculation and condition comparison on the candidate priority certificate contract, output the deliverability conclusion and confidence, and generate alternative parameter suggestions when the conditions are not met. Perform hierarchical feasibility projection correction, adjusting the fields of the candidate priority certificate contract item by item in the order of electrical constraint projection, thermal constraint projection, demand window projection and charge safety projection, to generate the corrected alternative priority certificate contract; Generate a deliverability certificate, clearly record the parameters of the alternative priority certificate contract, the deliverability margin, the worst-case boundary, the effective period and the reference index, register the corresponding contract as an in-life contract and add it to the in-life contract set, and write the deliverability certificate reference in the priority certificate contract template.

[0026] This invention, by introducing a contract feasibility domain prover and a multi-dimensional constraint projection system, achieves closed-loop management of priority certificate contracts from generation and feasibility verification to dynamic correction, demonstrating significant innovation and practical value. Through multi-source real-time data vectorization processing, including electrical constraint projection, thermal constraint projection, demand window projection, and charge safety projection, it enables quantifiable comparison of multi-dimensional constraints in charging scenarios, avoiding biases from traditional single-indicator or experience-based judgments and improving the accuracy and interpretability of feasibility assessments. The certificate network deeply integrates rules and computational units, enabling deliverability determination, confidence assessment, and generation of alternative parameters for candidate contracts. Combined with a hierarchical feasibility projection correction mechanism, it optimizes parameters item by item, ensuring that contracts meet the constraints of the power grid, equipment, and users during actual execution. The generation and citation of deliverability certificates provide transparent and traceable technical credentials for contract execution. Simultaneously, centralized management of generated contracts enhances the automation and fairness of cross-site migration, power allocation, and compensation settlement, significantly reducing operational risks and improving resource utilization efficiency.

[0027] In this embodiment, the step of calculating the committed waiting time interval, arrival time window, and cross-station migration incentive parameters based on the real-time state set, price parameter set, and in-service contract set, and generating reservation and queuing control instructions, includes: Read the real-time status set, price parameter set, and active contract set to determine the current available power, active contract occupation time, arrival request density, and number of idle gun ports; Based on the deadlines and occupied periods of the active contract set, combined with the current available power and the number of idle charging guns, an in-station arrival time prediction sequence and a queue length prediction sequence are generated. The lower and upper bounds of the waiting time are calculated to form a committed waiting time interval, and the effective period and station range are marked. The generation of the in-station arrival time prediction sequence and queue length prediction sequence refers to the time series analysis and prediction based on the deadlines, occupied charging periods, and charging power requirements of each contract in the current active contract set, combined with the real-time available power and the number of idle charging guns at the station, using historical arrival data and user reservation information to estimate the possible changes in vehicle arrival density in future periods, i.e., the arrival time prediction sequence. The occupancy and release process of charging guns is simulated to calculate the expected change curve of the number of queued vehicles in each period, i.e., the queue length prediction sequence, providing data support for the accurate calculation of the waiting time interval. Based on the promised waiting time interval and the deadline of the set of active contracts, an arrival time window is generated, specifying the arrival start time, arrival end time, and timeout handling method. The generation of the arrival time window refers to determining the start and end times of the user's expected arrival by combining the promised waiting time interval and the deadline of the active contracts: the start time is taken as the lower bound of the promised waiting time minus the travel buffer, and the end time is taken as the earlier value between the contract deadline and the upper bound of the promised waiting time plus the buffer time; and timeout handling rules are set, such that if the user does not arrive within the window, reserved resources are released and priority is adjusted, or cross-site scheduling and compensation are triggered. Based on the current available power, the occupancy period of existing contracts, and the committed waiting time interval of each candidate site, cross-site migration incentive parameters are calculated, specifying the target site, the compensation amount, the cross-site reserved time window, and the resource lock duration, and recording the correspondence with the set of existing contracts. Specifically, the calculation of cross-site migration incentive parameters involves: The feasibility and cost of cross-site migration are determined by comprehensively evaluating the real-time available power, the duration of active contract occupancy, and the user's committed waiting time range for each candidate site. The amount of compensation to be provided should be calculated based on the resource constraints of the target site, the additional travel time required for user migration, and the potential delay risks. Based on the resource allocation plan of the target site, set cross-site reserved time windows and resource lock duration to ensure that resources are available when migrating users arrive, and establish a mapping relationship with the original living contract in the results; Based on the promised waiting time interval, arrival time window, and cross-station migration incentive parameters, reservation and queuing control instructions are generated and issued to both the user and charging pile sides. Simultaneously, reference identifiers for the promised waiting time interval and arrival time window fields are written into the priority certificate contract template. Specifically, generating reservation and queuing control instructions involves allocating available service time slots and charging ports to the existing contract based on the promised waiting time interval, arrival time window, and cross-station migration incentive parameters, determining the queuing order and priority, issuing reservation confirmation, arrival reminder, and timeout handling rules to the user side, and issuing queue insertion, resource locking, arrival verification, and release condition instructions to the charging pile side. At the same time, the corresponding field reference identifiers are recorded in the priority certificate contract template.

[0028] This invention achieves refined and dynamic management of charging resource allocation by introducing the collaborative calculation of in-station arrival time prediction sequences, queue length prediction sequences, arrival time windows, and cross-station migration incentive parameters, demonstrating significant innovation and practical value. It integrates multi-source dynamic information such as real-time available power, in-service contract occupancy periods, and committed waiting time intervals, using time series prediction and resource occupancy simulation to accurately depict the future distribution of charging demand. The results are mapped into executable reservation, queuing, and cross-station scheduling instructions. This not only identifies potential queuing bottlenecks in advance during peak periods and dynamically adjusts arrival time windows and timeout handling rules, but also balances resource load between stations through cross-station migration incentive mechanisms, reducing overall waiting time and improving user experience. The introduction of structured reference identifiers in the priority certificate contract template enables on-chain traceability of reservation parameters and execution logic, enhancing the transparency and credibility of the scheduling process. The overall solution has significant advantages in improving charging network operating efficiency, reducing user waiting costs, and optimizing resource utilization, and provides a scalable technical path for the integration of intelligent scheduling and contract execution.

[0029] In this embodiment, the step of performing provably fair scheduling on the set of in-life contracts under the constraint parameter set, and calculating the charging gun port power allocation, queue order, interruption and renewal instructions according to the randomization rule prioritizing contract deadline and the fairness regret objective, includes: Read the constraint parameter set, the set of existing contracts, the promised waiting time interval, the arrival time window and the deliverability certificate reference, determine the time discrete set and the charging gun set, and obtain the target incremental power, deadline, interruptibility level and allowable interruption interval for each existing contract; Based on the real-time status set and deliverability certificate, the system generates a time-varying available power sequence within the station, peak power of each charging port, power distribution capacity margin, cable temperature rise margin, and effective range of demand window, and forms a waiting time prediction and a promised waiting upper bound. Randomization priority rule for the three factors of execution certificate margin, commitment risk, and cutoff margin: Give higher selection probability to in-life contracts with closer deadlines, higher risk of breach of commitment waiting, and more sufficient minimum safety margin; When the minimum safety margin is low, the selection probability is reduced, and the existing contracts within the same arrival time window are randomized and scattered. Under the constraints of single service per charging port, power not exceeding peak value, total station power not exceeding available power over time, temperature rise not exceeding limits, cumulative energy supply before contract expiration not less than target incremental power, and interruptions only within the allowable interruption range, the power allocation and queuing order of charging ports are calculated as follows: Within each time slice, candidates are selected only from existing contracts that meet the allowable interruption interval, have not expired, have idle gun ports, and have not exceeded the temperature rise limit. Based on the time-varying available power limit and the peak power of each gun port, the list of allocable power and serviceable gun ports for this time slice is obtained. Candidate contracts are prioritized according to a three-factor rule. The three factors include those with closer deadlines, those whose commitments are more easily breached, and those with more safety margins. Within the same priority, the contracts are randomly shuffled to suppress congestion, generating a queuing order. Uninterruptible vehicles are placed at higher positions within the group, while interruptible vehicles are arranged according to the slots of the allowed interruption interval. According to the queuing order, idle gun ports are matched one by one and power is allocated to each contract. Priority is given to filling gun ports with higher temperature rise margin and close to the allowable range of demand window. After each allocation, the single gun single service, the gun port does not exceed the peak value, the whole station does not exceed the available power, the temperature rise does not exceed the limit, and the cumulative energy supply before the deadline can reach the target power. If the verification fails, the power is reduced or the gun is replaced and redistributed, and the effective time of the interruption / renewal instruction is recorded.

[0030] A quantile fairness regret target is introduced, grouping users by interruptibility level. The goal is to reduce the high quantile threshold for excessive waiting in each group. When the threshold rises, the commitment risk weight in the three-factor randomization priority rule is increased, while the cutoff margin weight is decreased. Simultaneously, interruption and renewal instructions are generated and their effective periods are recorded. Specifically, the quantile fairness regret target involves grouping users' waiting time distribution by interruptibility level and setting a control target for the high quantile value of waiting time for each group, aiming to lower the quantile threshold and reduce extreme cases of excessive waiting. During execution, when an increase in the high quantile threshold is detected, the weight allocation in the three-factor randomization priority rule is dynamically adjusted: the commitment risk weight is increased to prioritize the waiting commitment of users in that group, and the cutoff margin weight is decreased to reduce delay risk; interruption and renewal instructions are generated, and their effective periods are recorded, achieving a dynamic balance between waiting fairness and service continuity for users at different interruption levels.

[0031] This invention introduces a randomized priority rule based on three factors—"certificate margin, commitment risk, and cutoff margin"—into the power allocation and queuing scheduling of charging gun ports. Combined with a quantile fairness regret objective, this forms a dynamic, refined, and fair resource allocation mechanism. By grouping users according to interruptibility levels and monitoring high quantile values ​​of waiting time, the system adjusts the priority rule weights in real time when a certain type of user shows an excessive waiting trend. This increases the weight of commitment risk and decreases the weight of cutoff margin, proactively mitigating extreme waiting times and ensuring service fairness for different user groups. A randomized scattering strategy is incorporated into the priority ranking to effectively prevent resource monopoly caused by fixed priorities and reduce queuing congestion during peak arrival times. The power allocation process is dynamically optimized under multiple constraints, including single gun port, total station power, temperature rise, safety margin, demand window, and contracted energy supply, ensuring contract fulfillment while improving energy utilization efficiency. The overall solution achieves a multi-objective balance between waiting time control, service fairness, and operational efficiency, demonstrating significant technological innovation and practical value.

[0032] In this embodiment, the monitoring of power, temperature, and metering information in multi-source heterogeneous operation data, assessment of default and congestion risks, and calculation of the cross-site migration target site and compensation amount based on the deliverability certificate and cross-site migration incentive parameters when a preset threshold is reached, and implementation of migration and resource reservation, including: Based on multiple types of sensors deployed on charging piles, power distribution equipment and metering terminals, power, temperature and metering information are collected in real time. Multi-source heterogeneous operation data are processed for time synchronization, anomaly removal and format unification to generate structured monitoring datasets. The structured monitoring dataset is matched with the set of existing contracts to calculate the current power load ratio, temperature rise trend and metering deviation, and compare them with preset thresholds or safe ranges. Based on the risk prediction model based on gradient boosting decision tree, the default probability and congestion risk level are output. When the risk level reaches or exceeds the threshold, the deliverability certificate and cross-site migration incentive parameters are invoked. Combined with the real-time capacity, queuing status and demand window of the target site, the target site for cross-site migration is determined. The difference in charging unit price between the target site and the current site is compared to obtain the unit price compensation value. Then, based on the expected migration distance and vehicle energy consumption characteristics, it is converted into migration energy consumption cost. Then, combined with the available charging power of the target site and the expected waiting time, the user's time cost is evaluated. Finally, the unit price compensation value, energy consumption cost and time cost are weighted and summed to obtain the final compensation amount. Send migration instructions and resource reservation plans to the target site, lock the charging port and power distribution capacity, and simultaneously update the set of existing contracts, price parameters and cross-site reserved time windows.

[0033] In this embodiment, the settlement based on the priority certificate contract template and the certifiable fair scheduling and migration execution results, and the compensation or payment for contracts that fail to reach the target incremental electricity or exceed the promised waiting time interval according to the repurchase parameters, includes: Based on the priority certificate contract template, combined with the certifiable fair scheduling results and cross-site migration execution, calculate the actual charging volume, usage time, migration compensation, and fulfillment of commitments for each active contract, and generate a settlement list. By comparing the actual charging capacity with the target incremental capacity, and the actual waiting time with the promised waiting time range, contracts that have not reached the target capacity or exceeded the waiting time limit are marked, and the triggering conditions for compensation or payment are determined. Based on the repurchase parameters, calculate the amount payable for contracts that trigger the compensation or payment conditions, distribute the compensation or payment to the user's account according to the payment method stipulated in the priority certificate contract, and record it in the settlement and financial system.

[0034] refer to Figure 2 The intelligent operation and management system for charging piles based on big data includes the following modules: The data acquisition and preprocessing module is used to collect multi-source heterogeneous operational data and perform preprocessing to form a real-time status set; The constraint pricing module is used to extract power distribution capacity, cable temperature rise limit, demand window and vehicle charge safety range based on real-time status set, and generate constraint parameter set and price parameter set; The contract determination module is used to generate priority certificate contract templates and build a contract feasibility domain prover to determine feasibility and generate a deliverability certificate. The relocation decision module is used to perform cross-site migration target site selection, supplementary amount calculation and migration plan formulation based on the deliverability certificate and cross-site migration incentive parameters. The randomized scheduling module is used to introduce randomization rules and fair regret objectives to optimize resource allocation and scheduling results; The monitoring and risk response module is used to monitor power, temperature and metering information in real time, assess default and congestion risks, and trigger migration and resource reservation operations when thresholds are reached. The settlement and compensation module is used to complete the settlement based on the priority certificate contract template and the results of fair scheduling and migration, and to perform compensation or payment according to the repurchase parameters for contracts that have not reached the incremental electricity or have exceeded the waiting time.

[0035] Example 1: To verify the feasibility of this invention in practice, it was applied to a cluster of three DC fast charging stations located 3–6 kilometers apart in a core urban area, referred to as "Site A," "Site B," and "Site C," respectively. The pilot program ran from May 20th to May 27th, 2025, focusing on the overlapping load during weekday evening peak hours (17:30–21:30) and weekend daytime hours (10:00–18:00). Before the pilot, Site A experienced severe queuing during weekday evening peak hours, with P95 waiting times consistently exceeding 55 minutes; Site B experienced short-term congestion during the evening rush hour; and Site C remained relatively idle during the same time period, exhibiting uneven demand distribution, which could lead to insufficient utilization of off-peak opportunities, excessively high peak demand, and significant fluctuations in user experience. The implementation goal is to avoid congestion caused by prediction errors and scheduling delays by applying a big data-based intelligent operation and management method for charging piles without adding new hardware, thereby reducing peak demand and improving operational revenue while ensuring user experience.

[0036] In application scenarios, the system first collects multi-source heterogeneous operational data, encompassing charging facilities, vehicles, user transactions, the external environment, and the power grid, with charging piles as the core. This data is then synchronized in time, anomaly removal, and dimensional unification to form a directly calculable real-time status set. Based on this, a priority certificate contract template is assembled, with fields including target incremental electricity, deadline, interruptibility level, cross-site transferability parameters, repurchase parameters, commitment waiting time interval, and arrival time window. A "commit first, schedule later" service is then published. All candidate contracts undergo a contract feasibility domain proof process before issuance: the certificate network first outputs a deliverability conclusion and confidence level, then performs hierarchical corrections of electrical constraint projection, thermal constraint projection, demand window projection, and charge safety projection. Only when the contract parameters match the constraint parameter set is a deliverability certificate generated and issuance permitted. This intercepts undeliverable requests at the source, avoiding the systemic default risk of "selling first, scheduling later."

[0037] On the user side, the app displays the promised waiting time range and arrival time window, and pushes cross-site migration incentive plans when the load is unbalanced, clearly specifying the target site, the compensation amount, and the reserved time window. The station adopts fair random scheduling: the selection order considers the urgency of the deadline, the risk of the commitment being breached, and the minimum safety margin given by the deliverability certificate, prioritizing contracts nearing their deadline while avoiding allocating power to nozzles that might cause temperature rises or trigger demand overruns. Controlled interruptions and contract renewals are implemented for vehicles falling within the allowable interruption range, reducing long queue occupancy and suppressing queue pulses. Power, temperature, and metering information are continuously monitored during operation; once a commitment is breached or the congestion risk reaches a threshold, cross-site migration is calculated and executed based on the deliverability certificate and migration incentive parameters, while simultaneously locking the corresponding time window and nozzle resources at the target site to ensure "successful migration, reliable reception, and accurate calculation." Settlement will be made according to the contract template, scheduling and migration records after the session ends; if the target incremental power is not reached or the promised waiting time period is exceeded, compensation or payment will be automatically made according to the repurchase parameters, and all operating data will be fed back for parameter updates the next day.

[0038] Table 1 Comparison of key indicators in the pilot program during peak hours for the station cluster

[0039] As shown in Table 1, after implementing the method of this invention, the waiting time and peak demand at each pilot site during peak periods both decreased significantly, while the overall cross-site migration acceptance rate remained at a high level. Taking site A as an example, on May 20th, the P95 waiting time decreased from 58 minutes before implementation to 26 minutes, the peak demand decreased from 1380kW to 1160kW, and the migration acceptance rate reached 71%. On May 25th, the waiting time decreased from 61 minutes to 27 minutes, the peak demand decreased from 1390kW to 1180kW, and the migration acceptance rate further increased to 74%. Similarly, at site B, the P95 waiting time was shortened by more than 30 minutes on multiple dates, the peak demand decreased by about 200kW, and the migration acceptance rate remained stable between 65% and 68%. The waiting time at site C decreased even more significantly; for example, on May 24th, it decreased from 42 minutes to 19 minutes, the peak demand decreased by about 30kW, and the migration acceptance rate remained above 50%.

[0040] Overall, this invention effectively alleviates charging queuing pressure during peak hours, significantly reducing waiting time and peak demand. Simultaneously, by employing a migration incentive mechanism, it diverts some vehicles to other stations, achieving a dual improvement in load balancing and user experience. This not only demonstrates the feasibility and effectiveness of this invention in improving charging pile operational efficiency but also provides strong data support for its subsequent promotion and application.

[0041] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart operation and management method for charging piles based on big data, characterized in that, include: Collect multi-source heterogeneous operation data covering charging piles, vehicles, user transactions, external environment and power grid, preprocess the multi-source heterogeneous operation data to form a real-time status set; Establish a priority certificate contract template, extract power distribution capacity, cable temperature rise limit, demand window and vehicle charge safety range based on real-time status set, form a set of constraint parameters and a set of price parameters, set cross-site transfer rules, initialize the set of existing contracts, and load the registered and unsettled contract records. Receive user charging requests, map them to candidate priority certificate contracts, call the contract feasibility domain prover composed of certificate network and feasibility projection layer to determine feasibility, and generate a deliverability certificate. Based on the real-time state set, price parameter set, and in-service contract set, calculate the commitment waiting time interval, arrival time window, and cross-station migration incentive parameters, and generate reservation and queuing control instructions. Under the constraints of the parameter set, provably fair scheduling is performed on the set of in-life contracts. According to the randomization rule that prioritizes the contract deadline and the fair regret objective, the power allocation of the charging gun port, the queue order, and the interruption and renewal instructions are calculated. Monitor power, temperature and metering information in multi-source heterogeneous operation data, assess default and congestion risks, and when the preset threshold is reached, calculate the target site for cross-site migration and the compensation amount based on the deliverability certificate and cross-site migration incentive parameters, and implement migration and resource reservation. Settlement will be completed based on the priority certificate contract template and the certifiable fair scheduling and migration execution results. For contracts that fail to reach the target incremental electricity volume or exceed the promised waiting time interval, compensation or payment will be made according to the repurchase parameters.

2. The intelligent operation and management method for charging piles based on big data according to claim 1, characterized in that, The multi-source heterogeneous operation data specifically includes power, temperature, metering, order, meteorological, traffic and energy storage data collected in real time by charging piles, connected vehicles, user transaction systems, external environmental monitoring devices and power grid interaction interfaces.

3. The intelligent operation and management method for charging piles based on big data according to claim 1, characterized in that, The preprocessing of multi-source heterogeneous operating data specifically includes time synchronization, outlier removal, missing value imputation, unit unification, and normalization of the multi-source heterogeneous operating data.

4. The intelligent operation and management method for charging piles based on big data according to claim 1, characterized in that, The establishment of the priority certificate contract template involves extracting power distribution capacity, cable temperature rise limits, demand windows, and vehicle charge safety ranges based on the real-time status set, forming a set of constraint parameters and a set of price parameters, setting inter-site transfer rules, initializing the set of existing contracts, and loading registered but unsettled contract records, including: Establish a priority certificate contract template, adopt a structured field assembly method, select and combine items from the predefined field library to form the content of the priority certificate contract template, and complete consistency verification and priority certificate contract template version number generation; Based on the real-time state set, a constraint extraction list is constructed. The distribution capacity, cable temperature rise limit, demand window and vehicle charge safety range are extracted in the order of priority of power grid agreement, equipment nameplate and operation monitoring to form initial constraint items. The initial constraint entries are validated and conflict resolved to obtain a set of constraint parameters; Load the time-of-use basic electricity price table, priority fee table, cross-site migration supplementary unit price table and repurchase compensation benchmark table, combine the load period and energy storage availability in the real-time status set to generate a price parameter set and establish the effective period and site range; Set cross-site transfer rules, clarifying the scope of transferable sites, transfer triggering conditions, cross-site reserved time window, target site resource locking method, migration compensation criteria, contract status migration process, and the order of failure rollback and automatic compensation, and establish a one-to-one correspondence with the price parameter set, constraint parameter set, and priority certificate contract template fields; Initialize the collection of live contracts, import all registered but unsettled contract records, and create an index by site and contract expiration time. At the same time, supplement the records with the corresponding deliverability certificate reference information, cross-site reserved time window number and price parameter version number.

5. The intelligent operation and management method for charging piles based on big data according to claim 1, characterized in that, The process of receiving a user charging request is mapped to a candidate priority credential contract. A feasibility determination is performed by invoking a contract feasibility domain prover composed of a certificate network and a feasibility projection layer, generating a deliverability certificate, including: Receive user charging requests, parse user charging requests according to priority certificate contract template, assemble candidate priority certificate contracts, and fill in target incremental electricity, deadline, interruptibility level, cross-site transferability parameters, repurchase parameters, commitment waiting time interval and arrival time window. Read the real-time state set, constraint parameter set, and in-life contract set to form the feasibility determination input; Establish a contract feasibility domain prover: Training samples and stress scenario sets are constructed based on historical order records, charging pile operation monitoring, demand window records, vehicle charging safety range and cross-station transfer rules; The training certificate network outputs deliverable conclusions and confidence levels, and assembles electrical constraint projection, thermal constraint projection, demand window projection, and charged safety projection into a hierarchical feasibility projection sequence in a fixed order, setting confidence thresholds and triggering sequences. The contract feasibility domain prover is invoked to make the first determination on the candidate priority certificate contract. When the deliverability conclusion of the certificate network is undeliverable, alternative parameter suggestions are given. When the confidence of the certificate network is lower than the set threshold, the hierarchical feasibility projection correction is initiated. When the certificate network is deliverable and the confidence reaches the set threshold, it is directly approved. Perform hierarchical feasibility projection correction, adjusting the fields of the candidate priority certificate contract item by item in the order of electrical constraint projection, thermal constraint projection, demand window projection and charge safety projection, to generate the corrected alternative priority certificate contract; Generate a deliverability certificate, clearly record the parameters of the alternative priority certificate contract, the deliverability margin, the worst-case boundary, the effective period and the reference index, register the corresponding contract as an in-life contract and add it to the in-life contract set, and write the deliverability certificate reference in the priority certificate contract template.

6. The intelligent operation and management method for charging piles based on big data according to claim 1, characterized in that, The process involves calculating the committed waiting time interval, arrival time window, and cross-station migration incentive parameters based on the real-time state set, price parameter set, and in-service contract set, and generating reservation and queuing control instructions, including: Read the real-time status set, price parameter set, and active contract set to determine the current available power, active contract occupation time, arrival request density, and number of idle gun ports; Based on the deadline and occupied time period of the existing contract set, combined with the current available power and the number of idle gun ports, generate the in-station arrival time prediction sequence and queue length prediction sequence, calculate the lower bound and upper bound of the waiting time, form the committed waiting time interval, and mark the effective time period and station range. Based on the promised waiting time interval and the deadline of the set of in-life contracts, an arrival time window is generated, specifying the arrival start time, arrival deadline, and timeout handling method. Based on the current available power, the period of occupation of existing contracts, and the promised waiting time interval of each candidate site, calculate the cross-site migration incentive parameters, clarify the target site, the amount of compensation, the cross-site reserved time window and the resource lock duration, and record the correspondence with the set of existing contracts. Based on the promised waiting time interval, arrival time window, and cross-station migration incentive parameters, reservation and queuing control instructions are generated and sent to the user side and the charging pile side. At the same time, reference identifiers for the promised waiting time interval field and the arrival time window field are written into the priority certificate contract template.

7. The intelligent operation and management method for charging piles based on big data according to claim 1, characterized in that, The process of performing provably fair scheduling on the set of in-life contracts under the constraints of the parameter set, and calculating charging gun power allocation, queue order, interruption and renewal instructions according to a randomization rule prioritizing contract deadlines and a fair regret objective, includes: Read the constraint parameter set, the set of existing contracts, the promised waiting time interval, the arrival time window and the deliverability certificate reference, determine the time discrete set and the charging gun set, and obtain the target incremental power, deadline, interruptibility level and allowable interruption interval for each existing contract; Based on the real-time status set and deliverability certificate, the system generates a time-varying available power sequence within the station, peak power of each charging port, power distribution capacity margin, cable temperature rise margin, and effective range of demand window, and forms a waiting time prediction and a promised waiting upper bound. The three-factor randomization priority rule for execution certificate margin—commitment risk—cutoff margin; Under the constraints of single service per charging port, power not exceeding peak value, total station power not exceeding available power, temperature rise not exceeding limits, cumulative energy supply before contract expiration not less than target incremental power, and interruptions only within the allowable interruption range, calculate the power allocation and queuing order of charging ports. A quantile fairness regret target is introduced, grouped by interruptibility level, with the goal of reducing the high quantile threshold of excess waiting in each group. When the threshold increases, the commitment risk weight in the three-factor randomization priority rule is increased and the cutoff margin weight is decreased. At the same time, interruption and renewal instructions are generated and the effective period is recorded.

8. A smart operation and management system for charging piles based on big data, comprising the smart operation and management method for charging piles based on big data as described in any one of claims 1 to 7, characterized in that, Includes the following modules: The data acquisition and preprocessing module is used to collect multi-source heterogeneous operational data and perform preprocessing to form a real-time status set; The constraint pricing module is used to extract power distribution capacity, cable temperature rise limit, demand window and vehicle charge safety range based on real-time status set, and generate constraint parameter set and price parameter set; The contract determination module is used to generate priority certificate contract templates and build a contract feasibility domain prover to determine feasibility and generate a deliverability certificate. The relocation decision module is used to perform cross-site migration target site selection, supplementary amount calculation and migration plan formulation based on the deliverability certificate and cross-site migration incentive parameters. The randomized scheduling module is used to introduce randomization rules and fair regret objectives to optimize resource allocation and scheduling results; The monitoring and risk response module is used to monitor and assess default and congestion risks in real time, and trigger migration and resource reservation operations when thresholds are reached. The settlement and compensation module is used for settlement, and for contracts that have not reached the incremental electricity volume or have exceeded the waiting time, compensation or payment is executed according to the repurchase parameters.

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