A method, system, equipment, and medium for intelligent equipment management in charging stations.

Abstract

This application relates to the technical field of charging station equipment management, and more particularly to a method, system, device, and medium for intelligent equipment management in charging stations. This application first responds to vehicle charging requests, obtaining the battery status of the vehicle to be charged and the initial power quota of the equipment; then, it collects real-time operating parameters of the equipment, including load level, operating temperature, and response time, and calculates a fast-charging capability coefficient reflecting the equipment's charging capacity; then, based on this coefficient, it optimizes the initial power quota to obtain a more reasonable allocation scheme; finally, it updates the equipment configuration using the optimized power quota and performs dynamic scheduling. By introducing performance evaluation and dynamic allocation mechanisms, this application achieves reasonable allocation of charging resources, improves charging efficiency, extends equipment lifespan, and enhances user experience.

Description

Technical Field

[0001] This application relates to the technical field of charging station equipment management, and in particular to a method, system, equipment and medium for intelligent equipment management in charging stations. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the scale of charging infrastructure construction is constantly expanding. As an important supporting facility, the management level of charging stations directly affects the popularization of new energy vehicles and user experience. How to improve the intelligent management level of charging stations and achieve efficient equipment scheduling and optimal resource allocation has become an urgent problem to be solved.

[0003] Currently, charging stations generally adopt centralized monitoring systems, which manage charging equipment through preset power allocation strategies. The system allocates charging power based on fixed scheduling rules and monitors equipment status through real-time data acquisition, realizing basic equipment management and fault early warning functions.

[0004] However, existing charging station management systems lack intelligent dynamic power allocation mechanisms, making it impossible to optimize resource allocation based on real-time equipment performance and load capacity. Especially during peak charging periods, the lack of effective equipment coordination and scheduling strategies easily leads to uneven distribution of charging resources, impacting charging efficiency and user experience; this situation requires further improvement. Summary of the Invention

[0005] To address the problem that existing charging station management systems cannot optimize resource allocation based on real-time equipment performance and load capacity, leading to uneven distribution of charging resources and impacting charging efficiency and user experience, this application provides a method, system, device, and medium for intelligent equipment management in charging stations, employing the following technical solution:

[0006] In a first aspect, this application provides a method for managing intelligent equipment in charging stations, comprising the following steps:

[0007] In response to vehicle charging requests, obtain the battery status of each vehicle to be charged and the initial power quota of each charging device;

[0008] Obtain the real-time operating parameters of each charging device, and obtain the fast charging capability coefficient of each charging device based on the real-time operating parameters;

[0009] Based on the fast charging capability coefficient and the initial power quota, the optimized power quota is obtained;

[0010] The initial power quota is updated with the optimized power quota, and the charging stations are dynamically scheduled in real time according to the optimized power quota.

[0011] By adopting the above technical solution, the system first responds to the charging request of a vehicle, and simultaneously obtains the battery status information of each vehicle to be charged and the initial power quota of the charging equipment. Then, by collecting real-time operating parameters of the charging equipment, including equipment load level, operating temperature, response time, etc., the system calculates a fast-charging capability coefficient reflecting the current charging capacity of the equipment. Next, the system optimizes and adjusts the initial power quota based on this fast-charging capability coefficient to obtain a more reasonable power allocation scheme. Finally, the optimized power quota is used to update the quota settings of each charging equipment, and the charging stations are dynamically scheduled in real time accordingly. By introducing an equipment performance evaluation mechanism and a dynamic power allocation strategy, the allocation of charging resources is made more reasonable, charging efficiency is improved, the service life of charging equipment is extended, and the user experience is enhanced.

[0012] Optionally, the fast charging capability coefficient includes a device performance coefficient and a load capacity coefficient. The fast charging capability coefficient for each charging device is obtained based on the real-time operating parameters, specifically including the following steps:

[0013] Obtain the real-time operating status of each charging device, including current load level, operating temperature, and device health.

[0014] Based on the real-time operating parameters, the power output stability index and charging response speed index of the charging device are obtained;

[0015] The device performance coefficient is calculated based on the power output stability index and the charging response speed index; the load capacity coefficient is calculated based on the current load level, operating temperature and device health.

[0016] By adopting the above technical solution, the system first collects real-time operating status data of the charging equipment, including parameters such as the current load level, operating temperature, and equipment health. Simultaneously, based on these real-time operating parameters, the system calculates power output stability and charging response speed indicators, reflecting the charging quality of the equipment. Then, based on the comprehensive evaluation results of these two indicators, the system calculates the equipment performance coefficient, and combines this with real-time status parameters such as the current load level, operating temperature, and equipment health to calculate the load capacity coefficient. This comprehensively reflects the performance status and load capacity of the charging equipment, making equipment performance evaluation more accurate.

[0017] Optionally, based on the real-time operating parameters, the power output stability index and charging response speed index are obtained, specifically including the following steps:

[0018] Acquire data on the device's charging power fluctuation, number of charging interruptions, and device response time within a preset time window;

[0019] The power output stability index is calculated based on the charging power fluctuation data; the charging response speed index is calculated based on the number of charging interruptions and device response time data.

[0020] By adopting the above technical solution, the system first continuously collects the operating data of the charging equipment within a preset time window, including real-time fluctuation data of charging power, statistics of the number of interruptions during the charging process, and data on the response time of the equipment to control commands. Then, by analyzing the changing trend and amplitude of the charging power fluctuation data, the system calculates an index reflecting the stability of the equipment output. At the same time, by combining the data on the number of interruptions during the charging process and the equipment response time, the system calculates a speed index characterizing the response performance of the equipment. This enables dynamic evaluation of the performance of the charging equipment, making the performance evaluation results more objective and reliable, and effectively preventing charging quality problems caused by equipment performance fluctuations.

[0021] Optionally, based on the fast charging capability coefficient and the initial power quota, an optimized power quota is obtained, specifically including the following steps:

[0022] Obtain current charging data at charging stations, including real-time power output of each charging device, device utilization rate, and number of vehicles waiting to be charged;

[0023] Based on the current charging data of the charging station and the vehicle's battery status, a dynamic adjustment coefficient is calculated.

[0024] The optimized power quota is calculated based on the initial power quota, the dynamic adjustment coefficient, and the fast charging capability coefficient.

[0025] By adopting the above technical solution, the system first comprehensively collects the current operating data of the charging station, including the real-time power output of each charging device, the actual utilization rate of the device, and the number of vehicles waiting to be charged. Then, the system combines this real-time charging data with the battery status information of the vehicles waiting to be charged to calculate a dynamic adjustment coefficient that reflects changes in current charging demand. Finally, the system comprehensively considers the initial power quota, the dynamic adjustment coefficient, and the aforementioned fast charging capability coefficient, and calculates a more reasonable power quota allocation scheme through an optimization algorithm. This allows the allocation of power quotas to be adjusted in real time according to the operating status of the charging station, improving the utilization efficiency of charging resources and ensuring a balanced distribution of charging tasks.

[0026] Optionally, the optimized power quota is calculated based on the initial power quota, the dynamic adjustment coefficient, and the fast charging capability coefficient, specifically including the following steps:

[0027] Based on the device performance coefficient and the load capacity coefficient, the initial power quota corresponding to each charging device is adjusted to obtain a preliminary optimized power quota.

[0028] The preliminary optimized power quota is adjusted according to the dynamic adjustment coefficient to obtain the optimized power quota.

[0029] By adopting the above technical solution, the system first takes the performance status of the charging equipment as the primary consideration. Based on the obtained equipment performance coefficient and load capacity coefficient, the initial power quota is adjusted in the first round to obtain a preliminary optimized power quota that reflects the actual working capacity of the equipment. Next, the system takes the overall operation status of the charging station as the second consideration. Based on the dynamic adjustment coefficient, the preliminary optimized power quota is further fine-tuned to obtain an optimized power quota that takes into account both the characteristics of individual equipment and the overall operating efficiency. By adopting a hierarchical optimization strategy, the calculation process of the power quota is made more accurate and controllable, which not only ensures the safe operation of individual charging equipment, but also improves the overall charging efficiency of the charging station.

[0030] Optionally, obtain the current charging data of the charging station and calculate the dynamic adjustment coefficient, specifically including the following steps:

[0031] Obtain historical and current operating data of charging equipment within a preset time period;

[0032] Based on the comparative analysis of the historical and current operating data, and the distribution of the battery status of the vehicles to be charged, a dynamic adjustment coefficient is calculated.

[0033] By adopting the above technical solution, the system first acquires historical operating data and current real-time operating data of the charging equipment simultaneously within a preset time period. This data includes information such as the load change trend and usage frequency of the equipment. Then, by comparing and analyzing the differences between historical data and current data, and combining the power distribution of the current group of vehicles waiting to be charged, the system comprehensively calculates a dynamic adjustment coefficient that reflects the changing trend of charging demand. This makes the dynamic adjustment more forward-looking and accurate, enabling it to predict changes in charging load in advance and achieve precise scheduling of charging equipment.

[0034] Optionally, the following steps may also be included:

[0035] Real-time monitoring of the operating status of charging equipment and acquisition of abnormal equipment performance data;

[0036] Based on the execution status of the optimized power quota, charging abnormal events are statistically analyzed in real time. Charging abnormal events include charging interruption, charging power fluctuation exceeding the preset range, device response time exceeding the preset threshold, and charging device communication interruption.

[0037] When the frequency of abnormal charging events exceeds a preset threshold, an equipment alarm message is sent to the charging station management system.

[0038] By adopting the above technical solution, the system first continuously monitors the operating status of each charging device and collects various data that may reflect abnormal device performance in real time. At the same time, the system classifies and statistically analyzes abnormal events that occur during the charging process based on the actual execution effect of the optimized power quota. These abnormal events include unexpected interruptions in the charging process, fluctuations in charging power exceeding the safe range, abnormal response times of the device to control commands, and communication interruptions with the management system. When the system detects that the frequency of these abnormal events exceeds the preset safety threshold, it immediately sends equipment alarm information to the charging station management system. This enables early detection and timely handling of charging equipment failures, improving the operational safety and reliability of the charging station.

[0039] Secondly, this application provides a smart equipment management system for charging stations, comprising:

[0040] The device information acquisition module is used to respond to vehicle charging requests by acquiring the power status of each vehicle to be charged and the initial power quota of each charging device.

[0041] The capability coefficient acquisition module is used to acquire the real-time operating parameters of each charging device and acquire the fast charging capability coefficient of each charging device based on the real-time operating parameters.

[0042] A power quota optimization module is used to obtain an optimized power quota based on the fast charging capability coefficient and the initial power quota;

[0043] The power dynamic scheduling module is used to update the initial power quota with the optimized power quota and to perform real-time dynamic scheduling of charging stations according to the optimized power quota.

[0044] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described intelligent charging station equipment management method.

[0045] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described intelligent equipment management method for charging stations.

[0046] In summary, this application includes at least one of the following beneficial technical effects:

[0047] 1. The intelligent equipment management method for charging stations in this application first responds to vehicle charging requests and obtains the battery status of the vehicle to be charged and the initial power quota of the equipment; then, it collects real-time operating parameters of the equipment, including load level, operating temperature, and response time, and calculates a fast charging capability coefficient reflecting the charging capacity of the equipment; then, it optimizes the initial power quota based on this coefficient to obtain a more reasonable allocation scheme; finally, it updates the equipment configuration using the optimized power quota and performs dynamic scheduling; by introducing performance evaluation and dynamic allocation mechanisms, it achieves reasonable allocation of charging resources, improves charging efficiency, extends equipment life, and improves user experience.

[0048] 2. First, the system collects real-time operating status data of the charging equipment, including parameters such as the current load level, operating temperature, and equipment health. Simultaneously, based on these real-time operating parameters, the system calculates power output stability and charging response speed indicators, reflecting the charging quality. Then, based on the comprehensive evaluation results of these two indicators, the system calculates the equipment performance coefficient, and combines this with real-time status parameters such as the current load level, operating temperature, and equipment health to calculate the load capacity coefficient. This comprehensively reflects the performance status and load capacity of the charging equipment, making equipment performance evaluation more accurate.

[0049] 3. The system first continuously collects operational data of the charging equipment within a preset time window, including real-time fluctuation data of charging power, statistics on the number of interruptions during the charging process, and data on the equipment's response time to control commands. Next, by analyzing the trends and amplitudes of the charging power fluctuation data, the system calculates indicators reflecting the stability of the equipment's output. Simultaneously, by combining the number of interruptions during charging and the equipment's response time data, it calculates a speed indicator characterizing the equipment's response performance. This enables dynamic evaluation of the charging equipment's performance, making the evaluation results more objective and reliable, and effectively preventing charging quality problems caused by equipment performance fluctuations. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a method for managing intelligent equipment in a charging station according to an embodiment of this application;

[0051] Figure 2 This is a flowchart illustrating step S200 in a method for managing intelligent equipment in a charging station according to an embodiment of this application.

[0052] Figure 3 This is a flowchart illustrating step S220 in a method for managing intelligent equipment in a charging station according to an embodiment of this application.

[0053] Figure 4 This is a flowchart illustrating step S300 in a method for managing intelligent equipment in a charging station according to an embodiment of this application.

[0054] Figure 5 This is a flowchart illustrating step S320 in a method for managing intelligent equipment in a charging station according to an embodiment of this application.

[0055] Figure 6 This is a flowchart illustrating step S330 in a method for managing intelligent equipment in a charging station according to an embodiment of this application.

[0056] Figure 7 This is a flowchart illustrating the anomaly detection process in a smart device management method for charging stations according to an embodiment of this application.

[0057] Figure 8 This is a schematic diagram of a module of an intelligent equipment management system for charging stations according to an embodiment of this application;

[0058] Figure 9 This is an internal structural diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0059] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0060] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0061] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0062] Firstly, this application provides a method for managing intelligent equipment in charging stations, referring to... Figure 1 It includes the following steps:

[0063] S100: In response to a vehicle charging request, obtain the battery status of each vehicle to be charged and the initial power quota of each charging device.

[0064] In this embodiment, the power status includes the vehicle's current remaining power percentage, the total capacity of the battery pack, the maximum allowable charging power, and the expected charging time; the initial power quota refers to the basic charging power value allocated by the charging station to each charging device, which is determined comprehensively based on the rated power of the charging device and the charging demand of the currently connected vehicle.

[0065] Specifically, the system has established a charging strategy database, which contains two core data tables: a vehicle charging characteristic table and a device power allocation table. The vehicle charging characteristic table stores the optimal charging power curves for different vehicle models in different remaining battery ranges, and divides the remaining battery capacity into three charging stages: fast charging, standard charging, and trickle charging. When the system receives a charging request, it first obtains the vehicle identification number and real-time battery data through the vehicle communication interface, and then queries the charging characteristic table to determine the optimal charging power. Next, according to the device power allocation table, it classifies the charging devices into different power levels, prioritizing the allocation of high-power devices to vehicles in the fast charging range and low-power devices to vehicles in the trickle charging range, thus achieving a rational allocation of charging resources.

[0066] S200: Obtain the real-time operating parameters of each charging device, and obtain the fast charging capability coefficient of each charging device based on the real-time operating parameters.

[0067] In this embodiment, the real-time operating parameters include output voltage stability index, output current ripple coefficient, core component temperature distribution, input power factor and charging interface temperature; the fast charging capability coefficient is a comprehensive evaluation index that reflects the current high-power charging capability of the charging device, with a value range of 0-1, where 1 indicates that the device is in the best fast charging state.

[0068] Specifically, the system constructs a hierarchical data acquisition and evaluation framework. At the hardware level, each charging device is equipped with a high-speed sampling module, transmitting data to the controller via an industrial real-time network. At the software level, the system establishes a fast-charging capability evaluation table, classifying the operating status of charging devices into five levels. Voltage stability is evaluated based on the voltage fluctuation amplitude within 100ms; current ripple coefficient is calculated through FFT analysis; six key points for core component temperature monitoring are assessed, with uniform temperature distribution and a maximum temperature below 45℃ representing the highest level; a power factor greater than 0.95 also represents the highest level; and a charging interface temperature below 40℃ represents the highest level. The system evaluates each indicator in real time and dynamically adjusts the weight of each indicator based on the current charging stage of the connected vehicle. For example, during the fast-charging stage, the weight of the current capability indicator is increased to 40%, ultimately yielding the fast-charging capability coefficient.

[0069] S300: Based on the fast charging capability coefficient and the initial power quota, obtain the optimized power quota.

[0070] In this embodiment, the optimized power quota refers to the actual available power value after dynamic adjustment based on the charging equipment status and vehicle charging demand; it must simultaneously meet three basic principles: the safe operation threshold of the charging equipment, the optimal charging curve requirement for the vehicle, and the total power limit of the charging station.

[0071] Specifically, the system establishes a three-level power optimization mechanism. The first level is single-unit power optimization, where the system establishes a charging power adjustment table and divides the fast charging capacity coefficient into five intervals. The second level is group power optimization, where the system groups vehicles at the same charging stage together and establishes a charging priority table, with the fast charging stage having the highest priority, the standard charging stage maintaining 100% quota, and the trickle charging stage reducing to 80% quota. The third level is station power optimization, where the system sets a total power limit for the station, and when the total demand exceeds the limit, power is reduced according to priority to ensure the charging power for vehicles with fast charging needs.

[0072] S400 updates the initial power quota with the optimized power quota and performs real-time dynamic scheduling of charging stations based on the optimized power quota.

[0073] In this embodiment, real-time dynamic scheduling includes power command issuance, charging process monitoring, and abnormal state handling.

[0074] Specifically, the system constructs a hierarchical scheduling and control architecture. At the instruction execution level, a charging equipment control table is established, including the equipment number, current power value, target power value, and adjustment step size. Each time the system schedules, the power adjustment range is limited to within 10% of the current value to avoid drastic fluctuations. At the monitoring and management level, a charging status monitoring table is established, recording four key parameters: charging voltage, current, power, and rate of change of charge. When any parameter exceeds the preset range, the system automatically switches the charging equipment to protection mode, reducing the power to 30% of the rated value. At the anomaly handling level, a fault response table is established, classifying abnormal situations into four categories: communication interruption, power oscillation, temperature over-limit, and charging interruption, and formulating corresponding handling strategies to ensure that the charging equipment can operate safely and stably when anomalies occur.

[0075] In one embodiment, the fast charging capability coefficient includes a device performance coefficient and a load capacity coefficient, referring to... Figure 2 In step S200, the fast charging capability coefficient of each charging device is obtained based on real-time operating parameters, specifically including the following steps:

[0076] S210. Obtain the real-time operating status of each charging device, including the current load level, operating temperature, and device health.

[0077] In this embodiment, the current load level refers to the ratio of the actual output power of the charging device to the rated power; the operating temperature includes the temperature values ​​of three monitoring points: the charging module temperature, the heat sink temperature, and the output port temperature; and the device health refers to a comprehensive indicator that reflects the overall operating status of the charging device.

[0078] Specifically, the system establishes a device operation status monitoring table and acquires operation status data through a hierarchical data collection method. At the hardware level, the system collects the temperature of each monitoring point in real time through a temperature sensor array; at the software level, the system calculates a health score based on a pre-established device health assessment rule base and the device's historical operation records. When the charging device starts working, the monitoring program continuously collects operation data according to a preset sampling period and records the data in the status monitoring table.

[0079] S220. Based on real-time operating parameters, obtain the power output stability index and charging response speed index of the charging equipment.

[0080] In this embodiment, the power output stability index refers to the degree of fluctuation in output power during the charging process, which is represented by the discrete measure of the power curve; the charging response speed index refers to the time required for the charging device to reach the target output power from receiving the control command.

[0081] S230. Based on the power output stability index and charging response speed index, the equipment performance coefficient is calculated; based on the current load level, operating temperature and equipment health, the load capacity coefficient is calculated.

[0082] In this embodiment, the equipment performance coefficient refers to an evaluation index that reflects the dynamic performance characteristics of the charging equipment; the load capacity coefficient refers to an evaluation index that reflects the load-bearing capacity of the charging equipment.

[0083] Specifically, for the equipment performance coefficient, the system establishes a performance scoring matrix, weighting power stability and response speed indicators according to different weights. For the load capacity coefficient, the system pre-establishes a load capacity assessment table, dividing the three parameters of load level, operating temperature, and equipment health into multiple levels. Each level combination corresponds to a basic score, and the load capacity coefficient is finally determined by looking up the table.

[0084] Furthermore, the system constructs an adaptive weight adjustment mechanism to dynamically optimize the calculation weights of the device performance coefficient and load capacity coefficient based on the historical operating data and current working status of the charging equipment. Specifically, the system establishes a weight learning model, which includes a device state feature vector and a weight optimization objective function. The state feature vector consists of the charging equipment's cumulative operating time, historical failure rate, ambient temperature change trend, and load fluctuation pattern; the optimization objective function considers charging efficiency, device lifespan, and failure risk. The system performs a weight update every preset interval. First, it calculates the importance score of each feature based on historical data, then it iteratively optimizes the model to obtain new weight coefficients, and finally applies the optimized weights to the calculation of the performance coefficient and load capacity coefficient. For example, when the system detects that the device is operating under high temperature and continuous high load during a high-temperature season, it automatically increases the weight ratio of the temperature feature, making the load capacity coefficient more sensitive to temperature changes, thereby preventing the risk of device overheating in advance.

[0085] In one embodiment, refer to Figure 3 In step S220, based on real-time operating parameters, the power output stability index and charging response speed index are obtained, specifically including the following steps:

[0086] S221. Obtain data on the device's charging power fluctuation, number of charging interruptions, and device response time within a preset time window.

[0087] In this embodiment, the preset time window refers to the data acquisition period set by the system; the charging power fluctuation data includes the power sampling value sequence, the maximum fluctuation amplitude, and the fluctuation frequency; the number of charging interruptions records the number of times unexpected charging terminations occur; the device response time data includes the instruction reception timestamp, the power adjustment start timestamp, and the power stabilization timestamp.

[0088] Specifically, the system employs a high-speed sampling module at the bottom layer; a data preprocessing module in the middle layer to reduce noise and extract key feature points from the sampled data; and a data statistics table at the top layer to record the processed power fluctuation characteristics. The system also maintains a charging event log table, using a counter to count the number of interruptions and comparing timestamps to calculate response time. When an abnormal charging interruption is detected, the system automatically increments the count in the event log table and records the relevant time information.

[0089] S222. Calculate the power output stability index based on the charging power fluctuation data; calculate the charging response speed index based on the number of interruptions and equipment response time data during charging.

[0090] In this embodiment, the power output stability index is characterized by standard deviation, which quantifies the dispersion of power fluctuations into specific values; the charging response speed index is obtained by comprehensively evaluating the interruption recovery time and power adjustment time.

[0091] Specifically, the system pre-establishes a fluctuation characteristic evaluation table, dividing the amplitude range of power fluctuations into multiple intervals, each corresponding to a basic score. The system determines the basic score for the degree of fluctuation by looking up the table, and then adjusts it based on the fluctuation frequency to obtain the final stability index. For the response speed index, the system establishes a response time scoring rule, comparing the interruption recovery time and power adjustment time with preset standard times respectively, determining the deduction value according to the degree of time difference, and the weighted average of the two scores is the response speed index.

[0092] In one embodiment, refer to Figure 4 In step S300, the optimized power quota is obtained based on the fast charging capability coefficient and the initial power quota, specifically including the following steps:

[0093] S310. Obtain current charging data of the charging station, including real-time power output of each charging device, equipment utilization rate, and number of vehicles waiting to be charged.

[0094] In this embodiment, real-time power output refers to the active power value currently actually output by the charging device; device utilization rate refers to the ratio of the actual working time of the charging device to the duration of the statistical period, reflecting the efficiency of device use; the number of vehicles to be charged includes two statistical values: the number of vehicles currently being charged and the number of vehicles waiting to be charged.

[0095] Specifically, the system establishes a database of charging station operation status and updates operational data in real time through a distributed data acquisition network. For real-time power output, the system directly collects data using a power metering module; for equipment utilization, the system records the start-up and shutdown times of each device through a working time statistics table and calculates the utilization rate according to a fixed statistical period; for the number of vehicles waiting to be charged, the system establishes a vehicle queue management table and obtains real-time data through a parking space detection system and a charging reservation system. When a new vehicle enters the charging station or completes charging and leaves, the system automatically updates the relevant data in the management table.

[0096] S320. Based on the current charging data of the charging station and the vehicle's battery status, the dynamic adjustment coefficient is calculated.

[0097] In this embodiment, the dynamic adjustment coefficient refers to the correction factor that adjusts the power distribution according to the overall operating status of the charging station. This coefficient takes into account the station load level, equipment utilization efficiency and charging demand distribution.

[0098] Specifically, the system constructs a rule-based adjustment coefficient calculation framework. First, a load assessment matrix is ​​established, dividing the total load rate of the charging station into multiple intervals, each corresponding to a basic adjustment value. Then, a demand response table is created, determining demand correction values ​​based on the number of vehicles waiting to be charged and the distribution of electricity consumption. Finally, fine-tuning is performed using equipment utilization data. The system pre-sets multiple sets of adjustment rules, and a rule engine comprehensively evaluates various parameters to obtain the final dynamic adjustment coefficient. For example, when the charging station is under high load and there are many waiting vehicles, the system will reduce the adjustment coefficient to avoid equipment overload.

[0099] S330: Based on the initial power quota, dynamic adjustment coefficient, and fast charging capability coefficient, the optimized power quota is calculated.

[0100] In this embodiment, the optimized power quota refers to the final power allocation scheme after multi-dimensional adjustments.

[0101] In one embodiment, refer to Figure 5 In step S320, the current charging data of the charging station is obtained, and the dynamic adjustment coefficient is calculated. This includes the following steps:

[0102] S321. Obtain historical and current operating data of the charging equipment within a preset time period.

[0103] In this embodiment, the preset time period refers to the data analysis period set by the system, which includes two time dimensions: peak and valley time period division and weekday and rest day division; historical operation data includes power load curve, device power-on time and number of charging orders; current operation data includes real-time power load value, number of online devices and number of charging orders being processed.

[0104] Specifically, the system constructs a two-tier data storage architecture. A historical data warehouse is established in the persistent layer, storing various types of operational data according to the time dimension and creating a data index table for easy retrieval. A real-time data buffer pool is established in the caching layer to store current operational data. The system pre-defines a time period segmentation table, dividing a day into multiple characteristic time periods, each corresponding to a different data analysis strategy. When a new analysis cycle begins, the system automatically extracts data for the corresponding time period from the historical data warehouse and pairs it with the data in the real-time data buffer pool to form a complete data analysis set.

[0105] S322. Based on the comparative analysis of historical and current operating data, and the distribution of the battery status of vehicles to be charged, the dynamic adjustment coefficient is calculated.

[0106] In this embodiment, the comparative analysis results include three evaluation indicators: load deviation value, equipment utilization rate change trend, and charging demand change trend; the power status distribution refers to the distribution of the remaining power level of the vehicle group to be charged, which is described by segmented statistics.

[0107] Specifically, the system has established a trend analysis model library with pre-set data comparison and analysis rules. First, load deviation is obtained through simple numerical comparison, comparing the current load with historical loads for the same period. Then, a trend analysis table is used to assess the changing trends of equipment utilization and charging demand. This table divides the change range into multiple intervals, each corresponding to a trend coefficient. The system also establishes a power distribution evaluation matrix, dividing vehicles waiting to be charged into multiple intervals based on their remaining power, and determining the charging pressure coefficient based on the distribution of vehicles in each interval. Finally, through a combined calculation method, the analysis results are weighted according to preset weights to obtain a dynamic adjustment coefficient reflecting the operating trend of the charging station. For example, when the current load is detected to be significantly lower than historical levels, and the vehicles waiting to be charged are mainly concentrated in the low-power interval, the system will appropriately increase the adjustment coefficient to improve the charging power configuration.

[0108] In one embodiment, refer to Figure 6 In step S330, the optimized power quota is calculated based on the initial power quota, dynamic adjustment coefficient, and fast charging capability coefficient, specifically including the following steps:

[0109] S331. Based on the equipment performance coefficient and load capacity coefficient, adjust the initial power quota corresponding to each charging device to obtain the preliminary optimized power quota.

[0110] In this embodiment, the weight ratio of the equipment performance coefficient to the load capacity coefficient is six to four, reflecting the dominant role of the equipment's dynamic performance in power adjustment; the preliminary optimized power quota refers to the power allocation scheme after the first round of adjustment.

[0111] Specifically, the system establishes a device power adjustment strategy table, mapping different combinations of device performance coefficients and load capacity coefficients to specific power adjustment schemes. First, a basic adjustment matrix is ​​constructed, dividing the performance coefficient and load coefficient into multiple levels, resulting in various combinations. Then, a power adjustment range is determined for each combination, with the range determined based on the device's safety margin. The system pre-establishes a power calibration rule base, obtaining adjustment parameters through table lookups and applying these parameters to the initial power quota. When the performance coefficient of a charging device is at a low level, even if the load capacity is good, the system will adopt a conservative power adjustment strategy to ensure safe device operation.

[0112] S332. Adjust the initial optimized power quota according to the dynamic adjustment coefficient to obtain the optimized power quota.

[0113] In this embodiment, the dynamic adjustment coefficient serves as a correction factor at the site level to balance the power allocation of the entire charging site; the optimized power quota refers to the final determined power allocation scheme, which needs to simultaneously meet the two objectives of individual device safety and overall site efficiency.

[0114] In one embodiment, refer to Figure 7 The method also includes the following steps:

[0115] S710 monitors the operating status of charging equipment in real time and obtains data on abnormal equipment performance.

[0116] In this embodiment, the abnormal device performance data includes voltage abnormality records, current abnormality records, temperature abnormality records, and communication quality records.

[0117] Specifically, the system establishes a multi-level anomaly monitoring framework. A status monitoring module is deployed at the device end to collect operating parameters via a sensor network; an anomaly judgment table is established at the control end to divide the normal range of various operating parameters into multiple intervals. The system pre-sets a parameter monitoring rule base to filter and classify the collected data in real time. When a parameter is detected to exceed the preset range, the system automatically records the abnormal data and labels the anomaly level.

[0118] S720: Based on the execution status of the optimized power quota, real-time statistics are collected on abnormal charging events, including charging interruption, charging power fluctuation exceeding the preset range, equipment response time exceeding the preset threshold, and communication interruption of charging equipment.

[0119] Specifically, the system has built an abnormal event management database. First, an event classification table is established to categorize abnormal events according to their nature and severity. Then, an event log table is created to record detailed information for each abnormal event. The system has a pre-set set of abnormal event judgment rules, which are used by a rule engine to identify and classify abnormal events. For charging power fluctuations, the system has set a fluctuation amplitude limit table to record the allowable fluctuation range for different time periods; for device response time, a response time evaluation table has been established, specifying the maximum response time limit for different operation types.

[0120] S730: When the frequency of abnormal charging events exceeds a preset threshold, send equipment alarm information to the charging station management system.

[0121] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0122] Secondly, this application provides an intelligent equipment management system for charging stations. The intelligent equipment management system for charging stations described in this application will be described below in conjunction with the above-mentioned intelligent equipment management method for charging stations.

[0123] Reference Figure 8 A smart equipment management system for charging stations, comprising:

[0124] The device information acquisition module is used to respond to vehicle charging requests by acquiring the power status of each vehicle to be charged and the initial power quota of each charging device.

[0125] The capability coefficient acquisition module is used to acquire the real-time operating parameters of each charging device and, based on the real-time operating parameters, acquire the fast charging capability coefficient of each charging device.

[0126] The power quota optimization module is used to obtain the optimized power quota based on the fast charging capability coefficient and the initial power quota;

[0127] The power dynamic scheduling module is used to update the initial power quota with the optimized power quota and to perform real-time dynamic scheduling of charging stations based on the optimized power quota.

[0128] In one embodiment, this application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a smart device management method for charging stations.

[0129] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0130] In one embodiment, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0131] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0132] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for managing intelligent equipment in charging stations, characterized in that, Includes the following steps: In response to vehicle charging requests, obtain the battery status of each vehicle to be charged and the initial power quota of each charging device; The process involves obtaining real-time operating parameters for each charging device, and then calculating the fast-charging capability coefficient for each device based on these parameters. The fast-charging capability coefficient includes a device performance coefficient and a load capacity coefficient. Specifically, this process includes the following steps: obtaining the real-time operating status of each charging device, including current load level, operating temperature, and device health; obtaining the power output stability index and charging response speed index of the charging device based on the real-time operating parameters; calculating the device performance coefficient based on the power output stability index and the charging response speed index, whereby the power output stability index is calculated based on charging power fluctuation data within a preset time window, and the charging response speed index is calculated based on the number of charging interruptions and device response time data; and calculating the load capacity coefficient based on the current load level, operating temperature, and device health. Obtain current charging data at charging stations, including real-time power output of each charging device, device utilization rate, and number of vehicles waiting to be charged; Based on the device performance coefficient and the load capacity coefficient, the initial power quota corresponding to each charging device is adjusted to obtain a preliminary optimized power quota. Obtain historical and current operating data of the charging equipment within a preset time period; calculate the dynamic adjustment coefficient based on the comparative analysis results of the historical and current operating data and the power status distribution of the vehicles to be charged; The preliminary optimized power quota is adjusted according to the dynamic adjustment coefficient to obtain the optimized power quota; The initial power quota is updated with the optimized power quota, and the charging stations are dynamically scheduled in real time according to the optimized power quota.

2. The intelligent equipment management method for charging stations according to claim 1, characterized in that, It also includes the following steps: Real-time monitoring of the operating status of charging equipment and acquisition of abnormal equipment performance data; Based on the execution status of the optimized power quota, charging abnormal events are statistically analyzed in real time. Charging abnormal events include charging interruption, charging power fluctuation exceeding the preset range, device response time exceeding the preset threshold, and charging device communication interruption. When the frequency of abnormal charging events exceeds a preset threshold, an equipment alarm message is sent to the charging station management system.

3. A smart equipment management system for charging stations, characterized in that, The intelligent equipment management method for charging stations according to any one of claims 1-2 includes: The device information acquisition module is used to respond to vehicle charging requests by acquiring the power status of each vehicle to be charged and the initial power quota of each charging device. The capability coefficient acquisition module is used to acquire the real-time operating parameters of each charging device and acquire the fast charging capability coefficient of each charging device based on the real-time operating parameters. A power quota optimization module is used to obtain an optimized power quota based on the fast charging capability coefficient and the initial power quota; The power dynamic scheduling module is used to update the initial power quota with the optimized power quota and to perform real-time dynamic scheduling of charging stations according to the optimized power quota.

4. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the charging station intelligent equipment management method according to any one of claims 1-2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent equipment management method for charging stations as described in any one of claims 1-2.

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