Smart city energy dynamic scheduling system and method based on big data analysis

Abstract

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

Description

Technical Field

[0001] The present invention relates to the field of energy scheduling, and more specifically, to a smart city energy dynamic scheduling system and method based on big data analysis. Background Art

[0002] With the explosive growth of electric vehicles in cities and the accelerated electrification of public transportation, the transportation system has become the most dynamic and complex load in the smart city energy network.

[0003] Traditional static scheduling methods are unable to cope with challenges such as sudden changes in traffic flow, spatiotemporal heterogeneity in charging behavior, and multi-stakeholder competition. Especially when a sudden substation failure causes a sharp drop in the available power of the power grid, it is impossible to quickly adjust the charging pile power instructions that have been issued to ensure that the loss of user charging demand is minimized. Summary of the Invention

[0004] The present invention provides a smart city energy dynamic scheduling system and method based on big data analysis, which solves the technical problem in related technologies that when the available power of the power grid drops suddenly, the charging pile power instructions that have been issued cannot be quickly adjusted.

[0005] The present invention provides a method for dynamic energy scheduling in a smart city based on big data analysis, comprising the following steps: S100 collects substation operating status, charging pile basic parameters, and regional load forecast data in real time, performs data cleaning and outlier filtering, and builds a complete dynamic information database for power grids and charging facilities. S200: Calculate the power supply margin for each region based on the capacity loss of the faulty substation. Establish a weighted scoring system for charging pile power adjustment based on the urgency of charging demand, and clarify the priority for reducing or restoring each charging load. S300 generates charging pile power adjustment instructions through a multi-objective optimization model. Under the premise of meeting grid capacity constraints and with the goal of minimizing user satisfaction loss, it iteratively modifies the power allocation plan and generates the final scheduling instruction set. S400 verifies the safety of the adjusted system step by step, including whether the total load of the substation exceeds the limit and whether the charging pile power exceeds the limit. It aggregates all verification results to generate a global safety flag to intercept risky scheduling plans; S500, calculate the difference between the power instruction and the actual operating value, filter out small adjustments below the threshold, allocate communication time slots according to the urgency, and issue instructions in batches. After confirmation of execution, update the system real-time power status record.

[0006] Furthermore, in S100, the following steps are specifically included: S110, obtaining real-time available power of the power grid: collecting real-time operating data of all substations in the fault-affected area; S120, extracting the charging pile command power set: reading the currently issued power commands of all controlled charging piles; S130, calculating the total required power: aggregating the total power requirements of all charging piles; S140, determining a power gap baseline value: calculating a difference between the original power demand and the available power; S150, calculating the effective power gap: eliminating negative interference and determining the final power reduction required.

[0007] Furthermore, in S200, the following steps are specifically included: S210, collecting real-time vehicle status data: obtaining the battery status and charging progress of the vehicle associated with the charging pile; S220, defining multi-dimensional weight coefficients: setting weight parameters of SOC and time factors in the evaluation model; S230, calculating the SOC completion score: quantifying the difference between the vehicle's current power level and the target power level; S240, calculating a time urgency score: evaluating the constraint strength of the vehicle's dwell time margin on charging demand; S250, obtaining vehicle type weight: loading preset priority weight according to vehicle type; S260, generating a comprehensive priority score: integrating multi-dimensional parameters to calculate the final priority.

[0008] Furthermore, the calculation formula for the comprehensive priority score is as follows: ; in represents the comprehensive priority score of the j-th charging pile, Indicates the SOC completion weight, represents the time urgency weight, represents the vehicle type weight of the j-th charging pile, represents the time urgency ratio of the jth charging pile, where represents the SOC completion ratio of the j-th charging pile; Vehicle types include public buses, emergency vehicles and private cars.

[0009] Furthermore, in S300, the following steps are specifically included: S310, generating a priority weight vector: converting the priority score into a normalized weight; S320, allocating preliminary power reduction: allocating the total power reduction to each charging pile according to weight; S330, calculating the theoretical adjusted power: preliminarily calculating the adjusted power value of each charging pile; S340: Apply a power lower limit constraint to ensure that the adjusted power is not lower than the minimum value allowed by the device; S350, calculating the secondary compensation gap: counting the remaining gap that is not allocated due to the lower limit constraint; S360, dynamic rebalancing allocation: secondary weight allocation of the remaining gap; S370, generating a power instruction set: outputting the final power instructions for all charging piles.

[0010] Furthermore, the calculation formula of the power instruction set is as follows: ; in represents the final power instruction set, Indicates the final power command of the mth charging pile.

[0011] Furthermore, in S400, the following steps are specifically included: S410, calculating the total load power of the area: the sum of the statistically adjusted power load and the base load; S420, verify substation capacity constraints: verify whether all substation loads are within safety limits; S430, verify charging pile power limit: check whether the power of each charging pile is within the rated range; S440, global safety status determination: generating a global safety flag based on all verification results.

[0012] Furthermore, in S500, the following steps are specifically included: S510, calculating the power adjustment increment: comparing the difference between the final instruction and the current actual power; S520, setting the incremental issuance threshold: defining the minimum change threshold for issuing instructions; S530, generating a set of instructions to be issued: screening the charging pile instructions to be actually issued; S540, communication timing grouping optimization: assigning instructions to different communication time slots according to priority; S550, updating the current power status of the system: after confirming that the command has been issued, the system record is updated.

[0013] Furthermore, the calculation formula for the set of instructions to be issued is as follows: ; in Indicates the charging pile index set to which instructions need to be issued. Indicates the power increment that needs to be adjusted for the j-th charging pile, Indicates the threshold value for the jth charging pile, Indicates the charging pile index; The calculation formula for communication timing grouping is as follows: ; in Indicates the communication time slot number of the j-th charging pile, Express The ceiling operator, where represents the comprehensive priority score of the j-th charging pile, Indicates the total number of communication time slots, ; Issuing rules: The jth instruction is in the time slot Issued; in Indicates the Communication time windows, where .

[0014] The present invention also proposes a smart city energy dynamic scheduling system based on big data analysis, which performs the steps of the aforementioned smart city energy dynamic scheduling method based on big data analysis, including: Multi-source information acquisition module: This module obtains real-time information on substation fault status, remaining power supply capacity, and the real-time operating parameters of surrounding charging piles. It also simultaneously accesses regional short-term load forecast data, standardizes multi-source heterogeneous data, and builds a comprehensive power grid-charging network status monitoring system. Fault Analysis and Decision-Making Module: This module quantitatively assesses substation capacity loss caused by faults, calculates the real-time power margin of each power supply zone, and generates a charging pile power adjustment priority scoring matrix based on the remaining charging time of charging vehicles and user service level factors, providing a basis for subsequent scheduling decisions. Dynamic scheduling optimization module: This module uses a multi-objective optimization algorithm to generate a power adjustment instruction set. While ensuring safe grid operation, it balances charging efficiency and user satisfaction. It dynamically modifies the power allocation strategy for each charging station through iterative calculations and outputs the final scheduling plan. Safety protection verification module: Implements multi-level safety verification of the dispatch plan, including whether the total load of the substation exceeds the limit and whether the power of the charging pile exceeds the tolerance range of the equipment. It aggregates the verification results of each level through logical AND operations and intercepts illegal instructions that pose an overload risk. Intelligent communication execution module: Identifies charging pile instruction increments that require adjustment, automatically filters out small fluctuations below the sensitivity threshold, optimizes instruction issuance timing based on business urgency, transmits instructions in batches through the communication network, and synchronously updates the system operation status database.

[0015] The beneficial effects of the present invention are: This invention achieves precise and rapid control of traffic load in the event of sudden power shortage in the power grid by building a dynamic response mechanism and a multi-objective collaborative optimization model: Based on real-time grid margins and user profiles, the system dynamically reorganizes power instructions for a large number of charging piles within seconds, ensuring the continuity of high-priority charging services at key transportation nodes while minimizing the perceived interruption of charging needs for ordinary users through flexible power redistribution technology. At the same time, an equipment damage risk prediction module is introduced to automatically avoid out-of-limit operations of electrical parameters during emergency load adjustment, forming the optimal decision under the triple constraints of "grid security-user satisfaction-equipment life", and effectively solving the problems of response lag and coordination imbalance of traditional static scheduling strategies in the face of complex transportation and energy networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a smart city energy dynamic scheduling method based on big data analysis proposed by the present invention; Figure 2 It is a sub-step of "multi-source information acquisition and pre-processing" in the method of the present invention; Figure 3 This is a sub-step of "fault impact analysis and adjustment target quantification" in the method of the present invention; Figure 4 This is a sub-step of "dynamic power allocation and instruction generation" in the method of the present invention; Figure 5 This is a sub-step of "security constraint verification and risk interception" in the method of the present invention; Figure 6 This is a sub-step of "incremental instruction issuance and state synchronization" in the method of the present invention. DETAILED DESCRIPTION

[0017] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0018] like Figures 1-6 As shown, a smart city energy dynamic scheduling method based on big data analysis includes the following steps: S100, multi-source information collection and preprocessing: real-time collection of substation operating status (including fault information, remaining capacity), charging pile basic parameters (rated power, minimum power, current load), and regional load forecast data, performs data cleaning and outlier filtering, and builds a complete grid-charging facility dynamic information database; In one embodiment of the present invention, the following steps are specifically included: S110, obtaining real-time available power of the power grid: collecting real-time operating data of all substations in the fault-affected area; ; Where n represents the total number of affected substations, represents the rated output power of the i-th substation, represents the current actual load power of the i-th substation, Indicates the total power currently available on the grid; S120, extracting the charging pile command power set: reading the currently issued power commands of all controlled charging piles; ; Where m represents the total number of charging piles in the affected area. Indicates the power instruction currently executed by the j-th charging pile, Represents the power command set of all charging piles; S130, calculating the total required power: aggregating the total power requirements of all charging piles; ; in Indicates the total power currently required for charging; S140, determining a power gap baseline value: calculating a difference between the original power demand and the available power; ; in Indicates the preliminary power gap value; S150, calculating the effective power gap: eliminating negative interference and determining the final power reduction required; ; in Indicates the actual power gap that needs to be reduced; S200, Fault Impact Analysis and Adjustment Target Quantification: Calculate the power supply margin for each region based on the capacity loss of the faulty substation. Establish a weighted scoring system for charging pile power adjustment based on the urgency of charging demand (e.g., battery SOC and service contract priority), and clarify the priority for reducing or restoring each power load. In one embodiment of the present invention, the following steps are specifically included: S210, collecting real-time vehicle status data: obtaining the battery status and charging progress of the vehicle associated with the charging pile; ; in Indicates the current power level of the vehicle associated with the j-th charging pile (%), represents the target charging capacity of the jth charging pile (%), Indicates the remaining time (minutes) of the vehicle's planned stop. Indicates the vehicle type code (bus=1, emergency=2, private car=3), Represents a vehicle status data set, Indicates the charging pile index; S220, defining multi-dimensional weight coefficients: setting weight parameters of SOC and time factors in the evaluation model; ; in Indicates the SOC completion weight (focusing on battery status), , Indicates the time urgency weight (focusing on scheduling timeliness), ; S230, calculating the SOC completion score: quantifying the difference between the vehicle's current power level and the target power level; ; in represents the SOC completion ratio of the j-th charging pile; S240, calculating a time urgency score: evaluating the constraint strength of the vehicle's dwell time margin on charging demand; ; in Indicates the vehicle's preset total stay time (minutes). represents the time urgency ratio of the jth charging pile, Indicates the remaining time (minutes) of the vehicle's planned stop; S250, obtaining vehicle type weight: loading preset priority weight according to vehicle type; ; in represents the vehicle type weight of the j-th charging pile, Indicates the vehicle type code; S260, generating a comprehensive priority score: integrating multi-dimensional parameters to calculate the final priority; ; in represents the comprehensive priority score of the j-th charging pile, Indicates the SOC completion weight, Indicates the time urgency weight; S300, Dynamic Power Allocation and Instruction Generation: Generates charging pile power adjustment instructions through a multi-objective optimization model. While meeting grid capacity constraints and minimizing user satisfaction loss, it iteratively modifies the power allocation plan and generates the final dispatch instruction set. In one embodiment of the present invention, the following steps are specifically included: S310, generating a priority weight vector: converting the priority score into a normalized weight; ; in represents the power adjustment weight of the jth charging pile, k represents the temporary index of the charging pile, represents the comprehensive priority score of the kth charging pile; S320, allocating preliminary power reduction: allocating the total power reduction to each charging pile according to weight; ; in Indicates the initial power reduction required for the j-th charging pile, Indicates the actual power gap that needs to be reduced; S330, calculating the theoretical adjusted power: preliminarily calculating the adjusted power value of each charging pile; ; in represents the theoretical adjustment power of the j-th charging pile, represents the original command power of the j-th charging pile; S340: Apply a power lower limit constraint to ensure that the adjusted power is not lower than the minimum value allowed by the device; ; in represents the minimum allowable power of the jth charging pile (usually 20% of the rated power), represents the actual adjusted power of the jth charging pile after constraint; S350, calculating the secondary compensation gap: counting the remaining gap that is not allocated due to the lower limit constraint; ; in Indicates the remaining power gap that needs to be redistributed. represents the actual adjusted power of the jth charging pile after constraint; S360, dynamic rebalancing allocation: perform secondary weight allocation on the remaining gap (only for charging piles that have not reached the lower limit); ; Update final power: ; in represents the compensation adjustment power of the jth charging pile, Indicates the minimum power threshold; S370, generating a power instruction set: outputting the final power instructions for all charging piles; ; in represents the final power instruction set, Indicates the final power instruction of the mth charging pile; S400, Safety Constraint Verification and Risk Interception: This step verifies the safety of the adjusted system, including whether the total load of the substation exceeds the limit and whether the charging pile power exceeds the limit. It aggregates all verification results to generate a global safety flag and intercepts risky scheduling plans. In one embodiment of the present invention, the following steps are specifically included: S410, calculating the total load power of the area: the sum of the statistically adjusted power load and the base load; ; in represents the basic non-charging load of the power supply area of ​​the i-th substation, represents the index set of charging piles under the i-th substation, represents the total load power of the i-th substation; S420, verify substation capacity constraints: verify whether all substation loads are within safety limits; ; in Indicates the safe operation coefficient of the substation, , Indicates the capacity check flag of the i-th substation (1=pass, 0=warning); S430, verify charging pile power limit: check whether the power of each charging pile is within the rated range; ; in represents the rated maximum power of the j-th charging pile, Indicates the power verification flag of the jth charging pile (1=passed, 0=out of limit); S440, global safety status determination: generating a global safety flag by integrating all verification results; ; in Indicates the global security status (1 = safe, 0 = risky); S500, incremental command issuance and status synchronization: Calculate the difference between the power command and the actual operating value, filter out minor adjustments below the threshold, allocate communication time slots according to urgency, and issue commands in batches. After confirmation of execution, update the system's real-time power status record; In one embodiment of the present invention, the following steps are specifically included: S510, calculating the power adjustment increment: comparing the difference between the final instruction and the current actual power; ; in Indicates the actual operating power of the jth charging pile. Indicates the power increment that needs to be adjusted for the j-th charging pile; S520, setting the incremental issuance threshold: defining the minimum change threshold for issuing instructions; ; in Indicates the incremental sensitivity coefficient (default , i.e. 2% rated power), Indicates the threshold value for the jth charging pile, represents the rated maximum power of the j-th charging pile; S530, generating a set of instructions to be issued: screening the charging pile instructions to be actually issued; ; in Indicates the charging pile index set to which instructions need to be issued; S540, communication timing grouping optimization: assigning instructions to different communication time slots according to priority; ; in Indicates the communication time slot number of the j-th charging pile, Express Ceiling operator; Issuing rules: The jth instruction is in the time slot Issued; in Indicates the total number of communication time slots, , Indicates the Communication time windows, where ; S550, update the current power status of the system: update the system record after confirming the instruction is issued; ; In one embodiment of the present invention, according to the above method, a smart city energy dynamic scheduling system based on big data analysis is also provided, comprising the following modules: Multi-source information acquisition module: This module obtains substation fault status, remaining power supply capacity, and real-time operating parameters of surrounding charging piles (including power limit and current load rate) in real time. It also simultaneously accesses regional short-term load forecast data, standardizes multi-source heterogeneous data, and builds a panoramic status monitoring system for the power grid and charging network.

[0019] Fault Analysis and Decision-Making Module: Quantitatively assesses substation capacity loss caused by faults, calculates the real-time power margin of each power supply zone, and generates a charging pile power adjustment priority scoring matrix based on factors such as the remaining charging time of charging vehicles and user service levels, providing a decision-making basis for subsequent scheduling.

[0020] Dynamic scheduling optimization module: uses a multi-objective optimization algorithm to generate a power adjustment instruction set, balancing charging efficiency and user satisfaction while ensuring the safe operation of the power grid. Through iterative calculations, it dynamically modifies the power allocation strategy of each charging pile and outputs the final scheduling plan.

[0021] Safety protection verification module: Implements multi-level safety verification of the scheduling plan, including whether the total load of the substation exceeds the limit, whether the power of the charging pile exceeds the tolerance range of the equipment, etc. Through logical AND operations, the verification results of each level are aggregated to intercept illegal instructions that pose an overload risk.

[0022] Intelligent communication execution module: Identifies charging pile instruction increments that require adjustment, automatically filters out small fluctuations below the sensitivity threshold, optimizes instruction issuance timing based on business urgency, transmits instructions in batches through the communication network, and synchronously updates the system operation status database.

[0023] According to the above system and method, the following examples are given, which specifically include the following contents: Scene background: During a thunderstorm in a certain urban area, a lightning strike caused a main transformer failure at Substation A, which supplies power to three nearby electric vehicle fast-charging stations (including 15 charging piles). This occurred during the evening rush hour, leading to a surge in charging demand.

[0024] Example process: Multi-source information acquisition module: Obtain the fault alarm signal of substation A in real time and calculate the remaining power supply capacity to be 60% of the original capacity.

[0025] Synchronously collect data from surrounding charging piles: Fast charging station 1: 8 charging piles are running at full load, and the total power is approaching the limit; Fast charging station 2: 4 charging piles are idle, and 2 charging piles are medium-loaded; Fast charging station 3: One charging pile is offline due to fault, and the rest have low load rates.

[0026] Access the continuous thunderstorm warning and regional 1-hour load forecast issued by the Meteorological Bureau (charging demand is expected to increase by 20%).

[0027] Standardize all data into a unified format to generate a real-time panoramic view of the power grid and charging network status.

[0028] Fault analysis and decision module: Quantifying the capacity loss of substation A: The currently available power can only meet the base load + 50% charging demand.

[0029] Calculate the power margin of the power supply partition: the total power gap in the fault-affected area reaches 30%.

[0030] Dynamically generate a priority matrix based on vehicle data: Taxi (recharge required within 30 minutes) > Private car (recharge can be delayed by 1 hour); Vehicles with a battery level <20%> Vehicles with a battery level >50%; The overloaded charging piles at fast charging station 1 are marked as priority power reduction targets, and the idle charging piles at fast charging station 2 are marked as available resources.

[0031] Dynamic scheduling optimization module: Optimization is initiated with the goals of grid security (not exceeding the remaining capacity of substations) and user fairness (giving priority to high-priority vehicles): Command ①: Reduce the power of the four fully loaded charging piles at Fast Charging Station 1 to the safety threshold; Instruction ②: Guide two low-priority vehicles to fast charging station 2 for charging; Instruction ③: Temporarily increase the upper limit of idle charging piles at Fast Charging Station 2 to divert pressure.

[0032] After three rounds of iterative calculations, it was confirmed that the solution can stabilize the grid load within a safe range while ensuring the experience of 80% of high-priority users.

[0033] Safety protection verification module: Level 1 verification: After the simulation is executed, the total load of substation A does not exceed the post-fault capacity red line.

[0034] Level 2 verification: The adjusted power of all charging piles is within the tolerance range of the equipment.

[0035] Level 3 verification: The power increase value involved in instruction ③ does not exceed the upper limit of the line current carrying capacity.

[0036] Aggregation result: The solution passed verification and there is no overload risk.

[0037] Intelligent communication execution module: Identify the increment of key instructions: 4 charging piles at fast charging station 1 need to reduce the power, and 2 charging piles at fast charging station 2 need to increase the power.

[0038] Filter fluctuations: Ignore pile fine-tuning instructions with load rate < 5%.

[0039] Issued in batches: Second 1: Send a power reduction command to fast charging station 1 (emergency avoidance); 5th second: Push transfer guidance information to affected vehicles; 10 seconds: Activate the backup charging pile at Fast Charging Station 2 and increase the power.

[0040] Real-time database update: charging pile status, substation load rate, and user dispatch records are refreshed synchronously.

[0041] Here are the results: At the power grid level: Substation A maintained safe operation during the fault condition and did not trigger a secondary power outage.

[0042] User level: Charging delay for high-priority users such as taxis is controlled within 15 minutes, while the average delay for ordinary users is 40 minutes.

[0043] Equipment level: Charging piles are not damaged by overload, and resource utilization rate is increased by 22%.

[0044] The above describes the embodiments of the present invention, but the present invention is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms under the guidance of the present invention, all of which are protected by the present invention.

Claims

1. A smart city energy dynamic scheduling method based on big data analysis, characterized in that: The following steps are involved: S100 collects substation operating status, charging pile basic parameters, and regional load forecast data in real time, performs data cleaning and outlier filtering, and builds a complete dynamic information database for power grids and charging facilities. S200: Calculate the power supply margin for each region based on the capacity loss of the faulty substation. Establish a weighted scoring system for charging pile power adjustment based on the urgency of charging demand, and clarify the priority for reducing or restoring each charging load. S300 generates charging pile power adjustment instructions through a multi-objective optimization model. Under the premise of meeting grid capacity constraints and with the goal of minimizing user satisfaction loss, it iteratively modifies the power allocation plan and generates the final scheduling instruction set. S400 verifies the safety of the adjusted system step by step, including whether the total load of the substation exceeds the limit and whether the charging pile power exceeds the limit. It aggregates all verification results to generate a global safety flag to intercept risky scheduling plans; S500, calculate the difference between the power instruction and the actual operating value, filter out small adjustments below the threshold, allocate communication time slots according to the urgency, and issue instructions in batches. After confirmation of execution, update the system real-time power status record.

2. The method for dynamic energy scheduling in smart cities based on big data analysis according to claim 1 is characterized in that: In S100, the following steps are specifically included: S110, obtaining real-time available power of the power grid: collecting real-time operating data of all substations in the fault-affected area; S120, extracting the charging pile command power set: reading the currently issued power commands of all controlled charging piles; S130, calculating the total required power: aggregating the total power requirements of all charging piles; S140, determining a power gap baseline value: calculating a difference between the original power demand and the available power; S150, calculating the effective power gap: eliminating negative interference and determining the final power reduction required.

3. The method for dynamic energy scheduling in smart cities based on big data analysis according to claim 2 is characterized in that: In S200, the following steps are specifically included: S210, collecting real-time vehicle status data: obtaining the battery status and charging progress of the vehicle associated with the charging pile; S220, defining multi-dimensional weight coefficients: setting weight parameters of SOC and time factors in the evaluation model; S230, calculating the SOC completion score: quantifying the difference between the vehicle's current power level and the target power level; S240, calculating a time urgency score: evaluating the constraint strength of the vehicle's dwell time margin on charging demand; S250, obtaining vehicle type weight: loading preset priority weight according to vehicle type; S260, generating a comprehensive priority score: integrating multi-dimensional parameters to calculate the final priority.

4. The method for dynamic energy scheduling in smart cities based on big data analysis according to claim 3 is characterized in that: The formula for calculating the comprehensive priority score is as follows: ； in represents the comprehensive priority score of the j-th charging pile, Indicates the SOC completion weight, represents the time urgency weight, represents the vehicle type weight of the j-th charging pile, represents the time urgency ratio of the jth charging pile, where represents the SOC completion ratio of the j-th charging pile; Vehicle types include public buses, emergency vehicles and private cars.

5. The method for dynamic energy scheduling in smart cities based on big data analysis according to claim 4 is characterized in that: In S300, the following steps are specifically included: S310, generating a priority weight vector: converting the priority score into a normalized weight; S320, allocating preliminary power reduction: allocating the total power reduction to each charging pile according to the weight; S330, calculating the theoretical adjusted power: preliminarily calculating the adjusted power value of each charging pile; S340: Apply a power lower limit constraint to ensure that the adjusted power is not lower than the minimum value allowed by the device; S350, calculating the secondary compensation gap: counting the remaining gap that is not allocated due to the lower limit constraint; S360, dynamic rebalancing allocation: secondary weight allocation of the remaining gap; S370, generating a power instruction set: outputting the final power instructions for all charging piles.

6. The method for dynamic energy scheduling in smart cities based on big data analysis according to claim 5 is characterized in that: The calculation formula of the power instruction set is as follows: ； in represents the final power instruction set, Indicates the final power command of the mth charging pile.

7. The method for dynamic energy scheduling in smart cities based on big data analysis according to claim 6 is characterized in that: In S400, the following steps are specifically included: S410, calculating the total load power of the area: the sum of the statistically adjusted power load and the base load; S420, verify substation capacity constraints: verify whether all substation loads are within safety limits; S430, verify charging pile power limit: check whether the power of each charging pile is within the rated range; S440, global safety status determination: generating a global safety flag based on all verification results.

8. The method for dynamic energy scheduling in smart cities based on big data analysis according to claim 7 is characterized in that: In S500, the following steps are specifically included: S510, calculating the power adjustment increment: comparing the difference between the final instruction and the current actual power; S520, setting the incremental issuance threshold: defining the minimum change threshold for issuing instructions; S530, generating a set of instructions to be issued: screening the charging pile instructions to be actually issued; S540, communication timing grouping optimization: assigning instructions to different communication time slots according to priority; S550, updating the current power status of the system: after confirming that the command has been issued, the system record is updated.

9. The method for dynamic energy scheduling in smart cities based on big data analysis according to claim 8, characterized in that: The calculation formula for the set of instructions to be issued is as follows: ； in Indicates the charging pile index set to which instructions need to be issued. Indicates the power increment that needs to be adjusted for the j-th charging pile, Indicates the threshold value for the jth charging pile, Indicates the charging pile index; The calculation formula for communication timing grouping is as follows: ； in Indicates the communication time slot number of the j-th charging pile, Express The ceiling operator, where represents the comprehensive priority score of the j-th charging pile, Indicates the total number of communication time slots, ; Issuing rules: The jth instruction is in the time slot Issued; in Indicates the Communication time windows, where .

10. A smart city energy dynamic scheduling system based on big data analysis, characterized in that: The steps of executing the method for dynamic energy scheduling in a smart city based on big data analysis as described in any one of claims 1 to 9 include: Multi-source information acquisition module: This module obtains real-time information on substation fault status, remaining power supply capacity, and the real-time operating parameters of surrounding charging piles. It also simultaneously accesses regional short-term load forecast data, standardizes multi-source heterogeneous data, and builds a comprehensive power grid-charging network status monitoring system. Fault Analysis and Decision-Making Module: This module quantitatively assesses substation capacity loss caused by faults, calculates the real-time power margin of each power supply zone, and generates a charging pile power adjustment priority scoring matrix based on the remaining charging time of charging vehicles and user service level factors, providing a basis for subsequent scheduling decisions. Dynamic scheduling optimization module: This module uses a multi-objective optimization algorithm to generate a power adjustment instruction set. While ensuring safe grid operation, it balances charging efficiency and user satisfaction. It dynamically modifies the power allocation strategy for each charging station through iterative calculations and outputs the final scheduling plan. Safety protection verification module: Implements multi-level safety verification of the dispatch plan, including whether the total load of the substation exceeds the limit and whether the power of the charging pile exceeds the tolerance range of the equipment. It aggregates the verification results of each level through logical AND operations and intercepts illegal instructions that pose an overload risk. Intelligent communication execution module: Identifies charging pile instruction increments that require adjustment, automatically filters out small fluctuations below the sensitivity threshold, optimizes instruction issuance timing based on business urgency, transmits instructions in batches through the communication network, and synchronously updates the system operation status database.

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