Intelligent operation and maintenance management method and device for charging pile, computer equipment and medium

Abstract

The invention relates to an intelligent operation and maintenance management method and device for a charging pile, computer equipment and a medium. The method comprises the steps that battery state parameters and charging pile operation state parameters are collected; carrying out trend extraction on the time sequence change conditions of the battery state parameters and the charging pile operation state parameters, carrying out correlation analysis, and generating a multi-dimensional state index representing the current operation states of the charging object and the charging equipment; based on the multi-dimensional state index, selecting a corresponding control strategy from the charging control strategy set, and performing adaptation degree evaluation on stage control parameters set for different remaining power intervals in the control strategy and the current state index; and executing a charging operation of a corresponding strategy according to an adaptation degree evaluation result. The method has the effect of improving the charging control efficiency of the charging pile.

Description

Technical Field

[0001] The present application relates to the technical field of charging pile management, and in particular to a method, device, computer equipment and medium for intelligent operation and maintenance management of charging piles. Background Art

[0002] With the widespread adoption of new energy vehicles, charging piles, as essential supporting infrastructure, are now responsible for high-frequency daily charging services. To improve charging efficiency and operational stability, some charging pile systems have begun to incorporate remote monitoring and parameter control capabilities based on the Internet of Things (IoT) architecture, enabling preliminary monitoring and management of battery status and equipment operating conditions.

[0003] Existing charging pile operation and maintenance management solutions usually adopt a static control parameter configuration method, but lack the ability to evaluate the actual performance status of the charging object in real time, and have not established a dynamic matching mechanism between the charging strategy and the equipment operating status. As a result, when facing different battery health levels, complex load environments or changes in operating status, the control strategy cannot be adaptively optimized, which in turn affects charging efficiency and equipment stability.

[0004] The above-mentioned existing technical solutions have the following defects: the existing charging pile operation and maintenance management method has a poor charging control strategy optimization mechanism and is difficult to achieve adaptive and efficient charging control, so there is room for improvement. Summary of the Invention

[0005] In order to improve the charging control efficiency of charging piles, the present application provides a charging pile intelligent operation and maintenance management method, device, computer equipment and medium.

[0006] The above-mentioned invention objective of this application is achieved through the following technical solutions: An intelligent operation and maintenance management method for charging piles based on the Internet of Things, the method comprising: Collect battery status parameters and charging pile operation status parameters; Extract trends from the time series changes of the battery status parameters and the charging pile operating status parameters, and perform correlation analysis to generate a multidimensional status index representing the current operating status of the charging object and the charging equipment; Based on the multidimensional state index, a corresponding control strategy is selected from a set of charging control strategies, and a compatibility evaluation is performed between the stage control parameters set for different remaining power intervals in the control strategy and the current state index; According to the adaptability evaluation results, the charging operation of the corresponding strategy is executed.

[0007] By adopting the above technical solution, by collecting battery status parameters and charging pile operating status parameters, it is possible to achieve real-time grasp of the operating status of the charging object and equipment, thereby providing accurate basic information for the subsequent selection of control strategies and improving the preemptiveness of operation and maintenance response; by performing trend extraction and correlation analysis on the time series changes of the collected parameters, it is possible to explore the evolution law of the equipment state and the battery response characteristics, thereby avoiding the strategy mismatch problem caused by the use of static parameter judgment and improving the flexibility of state assessment; by selecting control strategies based on multi-dimensional state indexes and performing fitness assessments, it is possible to quickly identify the most suitable solution for the current state among multiple alternative strategies, thereby avoiding the decrease in charging efficiency or equipment loss due to strategy call mismatch; by performing charging operations according to the fitness results, it is possible to dynamically adjust the control strategy to adapt to the actual operating status, thereby improving the stability, safety and intelligence level of the overall charging process.

[0008] In one example, the present application may be further configured as follows: performing trend extraction on the time series changes of the battery status parameters and the charging pile operating status parameters, and performing correlation analysis to generate a multidimensional status index representing the current operating status of the charging object and the charging device, specifically including: Setting a sliding time window, extracting the change slope and relative change rate of the voltage, SOC state value and temperature in the battery state parameters, and obtaining a battery trend feature vector representing the current response trend of the battery; By calculating the correlation index between the current fluctuation frequency and the temperature rise rate in the operating state parameters of the charging pile, a pile trend feature vector representing the equipment load change characteristics is obtained; A state index structure including multiple dimensions is constructed based on the battery trend feature vector and the pile trend feature vector, and index quantization coding is performed based on the threshold interval corresponding to the feature value of each dimension to obtain the multi-dimensional state index.

[0009] By adopting the above technical solution, by constructing battery trend feature vectors and pile trend feature vectors, the electrical response changes of the charging object and the load bearing capacity of the charging pile can be reflected respectively, thereby improving the comprehensiveness and accuracy of state modeling; by fusing the two trend vectors into a multi-dimensional state index and performing quantitative encoding, complex operating states can be mapped into a unified index structure, thereby reducing the control system's processing pressure on multi-source raw data and improving the efficiency and standardization of strategy calls.

[0010] In one example, the present application may be further configured as follows: calculating the correlation index between the current fluctuation frequency and the temperature rise rate in the charging pile operating state parameter to obtain a pile body trend feature vector that characterizes the equipment load change characteristics, specifically including: Obtaining a current data sequence of a preset period, identifying the number of fluctuation events in the current data sequence that exceeds a preset fluctuation threshold, and then calculating the fluctuation frequency per unit time as a current stability feature of the device; Obtaining a temperature change curve within the period, and calculating an average temperature rise slope and a fluctuation amplitude of the temperature change curve as a temperature rise change index; A correlation function between the current stability characteristic and the temperature rise variation index is constructed to obtain a corresponding synergistic variation coefficient, and the synergistic variation coefficient is used as a core component to generate the pile trend characteristic vector.

[0011] By adopting the above technical solution, by extracting the current fluctuation frequency and temperature rise change indicators and calculating the coordinated change coefficient of the two, it is possible to effectively capture the thermal-electric load coupling relationship of the charging pile under high power output conditions, thereby achieving in-depth modeling of the equipment operating pressure and improving the control strategy's judgment ability and safety redundancy design level under high load conditions.

[0012] In one example, the present application may be further configured as follows: selecting a corresponding control strategy from a charging control strategy set based on the multi-dimensional state index, and performing a compatibility evaluation between the stage control parameters set for different remaining power intervals in the control strategy and the current state index, specifically including: Based on the battery charging adaptability level and the charging pile load tolerance level in the multidimensional state index, calling the control strategy with the corresponding weight from multiple candidate control strategies; The stage control parameters corresponding to each remaining power segment of the control strategy are compared with the current state index item by item, and the adaptability score of the stage control parameters is calculated.

[0013] By adopting the above technical solution, by selecting a strategy based on the battery adaptability level and pile bearing level in the state index and calculating the adaptability score of the stage control parameters, it is possible to achieve a precise match between the charging control strategy and the current state of the device, thereby improving the rationality of the strategy call, avoiding abnormal battery response or pile overload due to parameter mismatch, and further improving charging efficiency and equipment stability.

[0014] In one example, the present application may be further configured as follows: executing a charging operation corresponding to a strategy according to the adaptability evaluation result specifically includes: When the adaptability score meets a preset threshold, a charging operation of the corresponding stage control parameters is performed according to the control strategy; When the adaptability score is lower than a preset threshold, the power output upper limit, voltage setting value and / or phase switching timing in the current strategy are adjusted within a preset range, and the adjusted adaptability score is recalculated; When the adjusted fitness score does not reach the re-evaluation threshold, a suboptimal strategy is selected for re-adaptation, and corresponding stage control parameters are reconfigured.

[0015] By adopting the above technical solution, by performing fine-tuning of the control parameters within the range when the adaptability score is insufficient, local optimization can be quickly achieved without completely replacing the strategy, thereby enhancing strategy flexibility and improving the response speed and stability of local control; by selecting a suboptimal strategy to rematch the control parameters after fine-tuning is invalid, a fault-tolerant mechanism can be established for strategy switching, thereby ensuring the continuity of the control process and reducing the risk of equipment impact and service interruption caused by strategy mismatch.

[0016] In one example, the present application may be further configured as follows: the charging pile intelligent operation and maintenance management method further includes: During the charging control execution process, collecting execution feedback information corresponding to the control strategy, the execution feedback information including charging efficiency changes, battery temperature rise response and execution time overhead; The mapping relationship between the multidimensional state index and the control strategy is adjusted and updated according to the execution feedback information.

[0017] By adopting the above technical solution, by collecting execution feedback information during the charging process and analyzing its adaptation relationship with the control strategy, it is possible to achieve a quantitative evaluation of the actual effect of the strategy, thereby enhancing the traceability and iterative capabilities of the strategy system; by dynamically adjusting the mapping relationship between the state index and the strategy based on the feedback results, it is possible to build a self-learning mechanism, thereby achieving continuous optimization of the strategy library as the operation effect increases, and improving the adaptability and intelligent evolution capabilities of the system during long-term operation.

[0018] The second object of the present invention is achieved through the following technical solutions: A charging pile intelligent operation and maintenance management device, characterized in that the device includes: Data acquisition module, used to collect battery status parameters and charging pile operation status parameters; A trend modeling module is used to extract trends from the time series changes of the battery status parameters and the charging pile operating status parameters, perform correlation analysis, and generate a multidimensional status index representing the current operating status of the charging object and the charging equipment; a strategy matching module, configured to select a corresponding control strategy from a set of charging control strategies based on the multi-dimensional state index, and to evaluate the compatibility of the stage control parameters set for different remaining power intervals in the control strategy with the current state index; The control execution module is used to execute the charging operation of the corresponding strategy according to the adaptability evaluation result.

[0019] By adopting the above technical solution, by collecting battery status parameters and charging pile operating status parameters, it is possible to achieve real-time grasp of the operating status of the charging object and equipment, thereby providing accurate basic information for the subsequent selection of control strategies and improving the preemptiveness of operation and maintenance response; by performing trend extraction and correlation analysis on the time series changes of the collected parameters, it is possible to explore the evolution law of the equipment state and the battery response characteristics, thereby avoiding the strategy mismatch problem caused by the use of static parameter judgment and improving the flexibility of state assessment; by selecting control strategies based on multi-dimensional state indexes and performing fitness assessments, it is possible to quickly identify the most suitable solution for the current state among multiple alternative strategies, thereby avoiding the decrease in charging efficiency or equipment loss due to strategy call mismatch; by performing charging operations according to the fitness results, it is possible to dynamically adjust the control strategy to adapt to the actual operating status, thereby improving the stability, safety and intelligence level of the overall charging process.

[0020] The third objective of this application is achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned charging pile intelligent operation and maintenance management method are implemented.

[0021] The fourth objective of this application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned charging pile intelligent operation and maintenance management method.

[0022] In summary, this application has the following beneficial technical effects: 1. By collecting battery status parameters and charging pile operating status parameters, it is possible to achieve real-time understanding of the operating status of charging objects and equipment, thereby providing accurate basic information for the subsequent selection of control strategies and improving the preemptiveness of operation and maintenance response; by extracting trends and analyzing correlations in the time series changes of collected parameters, it is possible to explore the evolution law of equipment status and battery response characteristics, thereby avoiding the strategy mismatch problem caused by static parameter judgment and improving the flexibility of status assessment; by selecting control strategies based on multi-dimensional state indexes and performing fitness assessments, it is possible to quickly identify the most suitable solution for the current state among multiple alternative strategies, thereby avoiding charging efficiency degradation or equipment loss caused by strategy call mismatch; by executing charging operations based on the fitness results, it is possible to dynamically adjust the control strategy to adapt to the actual operating status, thereby improving the stability, safety and intelligence level of the overall charging process; 2. By constructing battery trend feature vectors and charging pile trend feature vectors, the electrical response changes of the charging object and the load-bearing capacity of the charging pile can be reflected respectively, thereby improving the comprehensiveness and accuracy of state modeling. By fusing the two trend vectors into a multi-dimensional state index and performing quantitative encoding, complex operating states can be mapped into a unified index structure, thereby reducing the control system's processing pressure on multi-source raw data and improving the efficiency and standardization of strategy invocation. 3. By extracting the current fluctuation frequency and temperature rise change indicators and calculating the coordinated change coefficient between the two, it is possible to effectively capture the thermal-electric load coupling relationship of the charging pile under high power output conditions, thereby achieving in-depth modeling of the equipment operating pressure and improving the control strategy's judgment ability and safety redundancy design level under high load conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a method for intelligent operation and maintenance management of charging piles in one embodiment of the present application; Figure 2 This is a principle block diagram of a charging pile intelligent operation and maintenance management device in one embodiment of the present application; Figure 3 It is a schematic diagram of a device in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below with reference to the accompanying drawings.

[0025] In one embodiment, if Figure 1 As shown, the present application discloses a charging pile intelligent operation and maintenance management method, which specifically includes the following steps: S10: Collect battery status parameters and charging pile operation status parameters.

[0026] Specifically, the data acquisition module deployed inside the charging pile automatically connects to the vehicle's battery management interface to obtain battery information. At the same time, combined with the operating data measured by the pile's own sensors, it completes the real-time acquisition of battery status parameters including the battery's current voltage, current, temperature, and SOC status value. At the same time, the charging pile's own operating status parameters are obtained, including output current, voltage, internal temperature, and switch control status. In the charging area scenario of the smart community, the collection action can be uniformly dispatched through the edge gateway to uniformly schedule the data collection tasks of each charging pile, so that the operating status of each pile can be standardized and reported in parallel for subsequent centralized decision-making analysis.

[0027] S20: extracting trends from the time series changes of the battery status parameters and the charging pile operation status parameters, and performing correlation analysis to generate a multi-dimensional status index representing the current operation status of the charging object and the charging equipment.

[0028] Specifically, the collected parameter data are cached in chronological order to form a rolling time series. A sliding analysis window is used to perform continuous value change rate statistics on each type of parameter, and features such as temperature slope, current fluctuation rate, and SOC change trend are extracted. At the same time, the change trajectory between battery parameters and pile parameters is jointly calculated to analyze whether there is a linkage relationship between the two under different load stages. By extracting these stability and load sensitivity indicators, a set of numerical vector structures for comprehensively characterizing the current charging state are constructed as input for subsequent strategic decision-making. In the smart community scenario, unified processing of the compatibility and status classification of battery systems of different brands in the public pile network can be achieved.

[0029] S30: Based on the multi-dimensional state index, a corresponding control strategy is selected from the charging control strategy set, and the adaptability of the stage control parameters set for different remaining power intervals in the control strategy is evaluated with the current state index.

[0030] Specifically, the generated multidimensional state index is used as the current state input, and a query and match is performed from the preset charging control strategy set to screen out the candidate control strategies corresponding to the state index. The control parameters corresponding to each power stage interval defined in the strategy, such as the target charging mode, voltage adjustment interval, and power upper limit, are matched and compared with the characteristic values of each dimension in the current state index. The adaptability score is calculated based on the comparison results. The adaptability reflects the degree of fit between the control strategy parameters and the current battery charging capacity and pile load capacity. In the smart community scenario, it can be used to determine whether the public pile position is suitable for fast charging of the current vehicle, so as to avoid abnormal operation of the equipment or battery due to strategy incompatibility.

[0031] S40: Execute a charging operation corresponding to the strategy according to the adaptability evaluation result.

[0032] Specifically, the adaptation evaluation result of the previous stage is compared with the preset adaptation threshold. If the score is above the threshold, the control parameter set corresponding to the strategy is loaded, including current, voltage and mode configuration items, and these parameters are sent to the charging controller module through control instructions, thereby driving the actual charging process to start and execute at the preset rhythm. In high-density areas such as underground garages in smart communities, this step supports the completion of strategy judgment and initial control parameter activation within 5 seconds after the vehicle is connected, ensuring that the power output closed loop is quickly completed during the initial connection period of the vehicle, making the power supply system of the entire community run more smoothly.

[0033] In one embodiment, in step S20, trend extraction is performed on the time series changes of the battery status parameters and the charging pile operating status parameters, and correlation analysis is performed to generate a multidimensional status index representing the current operating status of the charging object and the charging device, specifically including: S21: Setting a sliding time window, extracting the change slope and relative change rate of the voltage, SOC state value and temperature in the battery state parameters, and obtaining a battery trend feature vector representing the current response trend of the battery.

[0034] Specifically, within the set sliding time window, the battery voltage, SOC state value and temperature data points are continuously obtained, and the fitting slope of these data points is calculated by the least squares method as the main parameter of the change trend. At the same time, the ratio of the change amplitude between adjacent sampling points is recorded to form a relative change rate index. These trend features are then combined and encoded in vector form to describe the battery's response activity and charging state evolution trend in the current period. For example, in the slow charging area in the garage of a smart community, the trend vector can be used to quickly determine whether the battery is in an acceptable fast charging state, thereby avoiding abnormal battery response caused by the erroneous issuance of high-power strategies.

[0035] S22: By calculating the correlation index between the current fluctuation frequency and the temperature rise rate in the charging pile operation status parameters, a pile trend feature vector representing the equipment load change characteristics is obtained.

[0036] Specifically, the collected time series data of the output current and shell temperature of the charging pile are analyzed. First, the number of fluctuations per unit time is identified according to the set current fluctuation threshold to form the current fluctuation frequency. Then, the average change rate and fluctuation amplitude of the temperature rise trend in the corresponding time period are calculated. These two indicators are constructed as the temperature rise change index. Then, a collaborative function is constructed based on the two to judge the strength of the correlation between their change trajectories. The numerical value of the collaborative relationship is quantified as a characteristic quantity of the load bearing trend, which together with the temperature rise index constitutes a pile trend characteristic vector, which is used to judge whether the charging pile is currently in a normal load state or there is a possibility of overload warning. In smart communities, historical statistical models can be combined to identify in advance the trend of pile overheating caused by multiple vehicles concurrently or high-frequency rotation.

[0037] S23: Construct a state index structure containing multiple dimensions based on the battery trend feature vector and the pile trend feature vector, and perform index quantization encoding based on the threshold interval corresponding to the feature value of each dimension to obtain a multi-dimensional state index.

[0038] Specifically, the battery trend feature vector and pile trend feature vector obtained above are spliced into a set of high-dimensional state vectors according to the preset field structure, and multiple discrimination interval labels are set for the feature values in each dimension. For example, the voltage slope less than 0.1 is set to level 1, the temperature rise rate greater than 5 is set to level 3, etc. Then, a threshold matching operation is performed on the feature values of each dimension, and the matching results are uniformly recorded in a discrete coding form. Finally, a quantitative state index for state classification and strategy mapping is generated. This index can be used as the input key value of the control logic, and supports the generation of standardized adaptation mapping rules for different devices and battery types in the smart community scenario, thereby reducing the risk of strategy confusion caused by heterogeneous terminal access.

[0039] In one embodiment, in step S22, the correlation index between the current fluctuation frequency and the temperature rise rate in the charging pile operating state parameters is calculated to obtain a pile body trend feature vector that characterizes the equipment load change characteristics, specifically including: S221: Acquire a current data sequence of a preset period, identify the number of fluctuation events in the current data sequence that exceeds a preset fluctuation threshold, and then calculate the fluctuation frequency per unit time as a device current stability feature.

[0040] Specifically, the output current of the charging pile is continuously sampled based on a set time period, a sequence of sampling values is extracted in each time window, and a current fluctuation threshold is set as a judgment condition. The positive and negative direction jump points that exceed the threshold during the continuous sampling process are identified, the total number of jump points in the entire time period is counted, and the current fluctuation frequency per unit time is calculated. This frequency value is used to reflect the stability state of the output end of the device during actual operation. In smart communities, this method can effectively detect current disturbances caused by users frequently plugging and unplugging vehicles or suddenly charging multiple vehicles during peak hours, providing an important reference for strategy adjustments.

[0041] S222: Obtain a temperature change curve within the period, and calculate the temperature rise average slope and fluctuation amplitude of the temperature change curve as a temperature rise change index.

[0042] Specifically, the shell temperature or internal thermistor data is read within a time period consistent with the current sampling cycle to form a continuous temperature change curve. The average temperature rise slope is obtained through linear fitting on this curve as a trend indicator. At the same time, the maximum-minimum temperature difference of the entire curve within this time period is calculated as the fluctuation amplitude indicator. These two indicators are used to determine whether the thermal response of the equipment is stable and whether the temperature rise is within the normal variation range. In the deployment environment of a smart community, this method can promptly identify abnormal temperature rise trends caused by poor heat dissipation or equipment aging, providing a pre-emptive basis for triggering power protection or strategy replacement in advance.

[0043] S223: Construct a correlation function between the current stability characteristics and the temperature rise change index to obtain the corresponding synergistic change coefficient, and use the synergistic change coefficient as the core component to generate a pile trend feature vector.

[0044] Specifically, the calculated current fluctuation frequency, average temperature rise slope and fluctuation amplitude are used as input variables, and the Pearson correlation analysis method is used to establish a functional relationship to characterize the coupling strength between current instability and temperature rise amplitude changes within a specific period. The calculation result is output as a synergistic change coefficient. The larger the coefficient, the stronger the degree of coupling between electrical and thermal loads during equipment operation, indicating that the charging pile is in a high-load and high-heat risk state. The coefficient is then written into the pile trend feature vector as the core feature quantity for subsequent fusion judgment of the state index. In smart communities, it can be used as an important basis for identifying whether the equipment is on the edge of the critical load, thereby improving the accuracy of strategy adaptation.

[0045] In one embodiment, if Figure 2 As shown, in step S30, based on the multi-dimensional state index, a corresponding control strategy is selected from the charging control strategy set, and the stage control parameters set for different remaining power intervals in the control strategy are evaluated for adaptability with the current state index, specifically including: S31: Based on the battery charging adaptability level and the charging pile load tolerance level in the multi-dimensional state index, a control strategy with a corresponding weight is called from multiple candidate control strategies.

[0046] Specifically, the dimension value representing the battery charging adaptability level and the dimension value representing the pile load bearing capacity level are extracted from the state index structure, and this group of dimension values is combined as the state keyword and input into the control strategy matching module. The strategy matching score is calculated according to the weight priority and adaptation index range in the preset strategy set, and the target control strategy that best matches the current state is selected from the group of strategies with the highest score. This strategy will be used to set the charging control parameters for the current period. In smart communities, this method supports the rapid execution of strategy call actions after the user makes an appointment for charging access, which is especially suitable for concurrent scenarios where multiple battery types and inconsistent pile power levels exist at the same site.

[0047] S32: Compare the stage control parameters corresponding to each remaining power segment of the control strategy with the current state index item by item, and calculate the adaptability score of the stage control parameters.

[0048] Specifically, the control parameters corresponding to each SOC segment in the control strategy are read, including the target charging mode, voltage setting value, power output upper limit and stage switching conditions, etc., and these parameters are compared and judged with the characteristic values of each dimension in the current state index in turn. A corresponding error tolerance interval is set for each parameter dimension. If the strategy parameter falls within the tolerance interval, it is recorded as a match. Finally, all matching items are scored and counted, and the overall adaptability score is calculated according to the set weight. This score is used to evaluate whether the current strategy is highly adapted to the device status. In the smart community scenario, it is suitable for high-precision intelligent control areas, and realizes quantitative evaluation of the dynamic adjustment capability of strategies for different vehicles and different scenarios.

[0049] In one embodiment, in step S40, based on the adaptability evaluation result, a charging operation corresponding to the strategy is executed, specifically including: S41: When the adaptability score meets a preset threshold, a charging operation of corresponding stage control parameters is executed according to the control strategy.

[0050] Specifically, the stage parameters corresponding to the battery SOC remaining power range in the current control strategy are loaded, and control instructions are initiated to the control end in sequence according to the segment definition, including setting current and voltage output limits, voltage climb rate, power upper limit, etc., and the output step is determined in combination with the operating temperature and stability parameters of the current stage to ensure smooth stage switching. In smart communities, this method can prevent power system fluctuations caused by sudden changes in the battery status of different vehicles, and improve the response smoothness of the substation cabinet and the service life of the equipment.

[0051] S42: When the adaptability score is lower than a preset threshold, the power output upper limit, voltage setting value and / or phase switching timing in the current strategy are adjusted within a preset range, and the adjusted adaptability score is recalculated.

[0052] Specifically, the parameter items that do not meet the adaptation conditions in the current control strategy are adjusted within the range, including increasing the voltage set point upper limit by within 0.5V, reducing the power upper limit by within 5%, and moving the SOC segment boundary left and right within ±1%. After each adjustment is completed, the state index is re-scored for adaptability based on the updated parameters to evaluate whether the locally optimized strategy has higher adaptability to the current device state. In the smart community environment, this mechanism can make the same strategy template more flexibly adapt to the combination of different types of battery vehicles and different load piles, thereby improving the versatility of the control strategy.

[0053] S43: When the adjusted fitness score does not reach the re-evaluation threshold, a suboptimal strategy is selected for re-adaptation, and corresponding stage control parameters are reconfigured.

[0054] Specifically, when the evaluation score still does not reach the execution threshold after multiple parameter fine-tuning, the suboptimal strategy in the control strategy set is enabled as a substitute choice, and the next most suitable strategy is selected according to the priority identifier or index distance matching result. Then, the segmented control parameters of the strategy are reloaded in steps for matching calculation to confirm its adaptability to the current state. If the requirements are met, the strategy is executed, otherwise it continues to the next round of strategy retrieval process. In the smart community scenario, this mechanism can maintain strategy stability in extreme environments such as high temperatures at night or peak concurrent charging, ensuring that the control logic does not fall into output idleness or abnormal strategy execution paths under various working conditions.

[0055] In one embodiment, the charging pile intelligent operation and maintenance management method further includes: S50: During the charging control execution process, execution feedback information corresponding to the control strategy is collected. The execution feedback information includes charging efficiency changes, battery temperature rise response, and execution time overhead.

[0056] Specifically, after each round of charging control strategy is issued and executed, the real-time operating data of the charging process is synchronously recorded, including the output trajectory of the actual charging current and voltage, the fitting slope of the SOC growth curve, the continuous rise rate of the battery temperature, and the start and end time of the charging cycle. By performing structured extraction on these data, the charging efficiency index and the temperature rise response curve are formed. At the same time, the time taken from the start of the control strategy to the achievement of the charging target of each stage is recorded as the execution time evaluation indicator. In the smart community environment, this process can be uniformly collected by the gateway for multiple charging piles within its jurisdiction and uploaded to the platform in a summary, so that the strategy execution effects under different conditions of different vehicles can be compared horizontally, providing an objective evaluation data basis for subsequent control strategy optimization.

[0057] S60: Adjust and update the mapping relationship between the multi-dimensional state index and the control strategy according to the execution feedback information.

[0058] Specifically, the matching relationship between the feedback information, the original state index and the control strategy is written into the dynamic mapping optimization module as a set of historical data. In this module, the strategy adaptation performance is normalized and scored according to the feedback results. If the feedback is executed multiple times in a row and shows that the matching effect between the strategy and the state index is significantly lower than that of other strategy combinations, the adjustment mechanism of the index-strategy mapping relationship is triggered, and the priority of the strategy in this type of state is reduced or replaced with a new strategy. At the same time, the feedback information during the adjustment process is retained as an update basis record. In the smart community charging platform, this mechanism can realize cross-time strategy self-learning and dynamic adjustment, so that the entire platform strategy library can continuously adapt to operating trends such as vehicle type evolution and equipment aging.

[0059] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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.

[0060] In one embodiment, a charging pile intelligent operation and maintenance management device is provided, which corresponds one-to-one with the charging pile intelligent operation and maintenance management method in the above embodiment. Figure 2 As shown in the figure, the charging pile intelligent operation and maintenance management device includes a data acquisition module, a trend modeling module, a strategy matching module, and a control execution module. The detailed description of each functional module is as follows: Data acquisition module, used to collect battery status parameters and charging pile operation status parameters; The trend modeling module is used to extract trends from the time series changes of battery status parameters and charging pile operating status parameters, perform correlation analysis, and generate a multi-dimensional status index that represents the current operating status of the charging object and charging equipment; A strategy matching module is used to select a corresponding control strategy from the charging control strategy set based on the multi-dimensional state index, and to evaluate the adaptability of the stage control parameters set for different remaining power intervals in the control strategy with the current state index; The control execution module is used to execute the charging operation of the corresponding strategy according to the adaptability evaluation result.

[0061] Optionally, the trend modeling module specifically includes: The battery trend extraction submodule is used to set a sliding time window to extract the change slope and relative change rate of the battery state parameters such as voltage, SOC state value and temperature, and obtain the battery trend feature vector that represents the current response trend of the battery; The equipment load identification submodule is used to obtain the pile body trend feature vector that characterizes the equipment load change characteristics by calculating the correlation index between the current fluctuation frequency and the temperature rise rate in the charging pile operation status parameters; The state index generation submodule is used to construct a state index structure containing multiple dimensions based on the battery trend feature vector and the pile trend feature vector, and to perform index quantization encoding based on the threshold interval corresponding to the feature value of each dimension to obtain a multi-dimensional state index.

[0062] Optionally, the device load identification submodule specifically includes: A current stability extraction unit is used to obtain a current data sequence of a preset period, identify the number of fluctuation events in the current data sequence that exceeds a preset fluctuation threshold, and then calculate the fluctuation frequency per unit time as the current stability feature of the device; The temperature rise index extraction unit is used to obtain the temperature change curve within the cycle and calculate the temperature rise average slope and fluctuation amplitude of the temperature change curve as the temperature rise change index; The feature fusion analysis unit is used to construct a correlation function between the current stability feature and the temperature rise change index, obtain the corresponding synergistic change coefficient, and use the synergistic change coefficient as the core component to generate the pile trend feature vector.

[0063] Optionally, the strategy matching module specifically includes: A strategy screening submodule is used to call a control strategy with corresponding weight from multiple candidate control strategies based on the battery charging adaptability level and the charging pile load tolerance level in the multidimensional state index; The parameter adaptation analysis submodule is used to compare the stage control parameters corresponding to each remaining power segment of the control strategy with the current state index item by item, and calculate the adaptation score of the stage control parameters.

[0064] Optionally, the control execution module specifically includes: The strategy execution submodule is used to execute the charging operation of the corresponding stage control parameters according to the control strategy when the fitness score meets the preset threshold; A parameter fine-tuning submodule is used to adjust the power output upper limit, voltage setting value and / or phase switching timing in the current strategy within a preset range when the fitness score is lower than a preset threshold, and recalculate the adjusted fitness score; The strategy replacement submodule is used to select the suboptimal strategy for re-adaptation and reconfigure the corresponding stage control parameters when the adjusted fitness score does not reach the re-evaluation threshold.

[0065] Optionally, the charging pile intelligent operation and maintenance management method also includes: The feedback acquisition module is used to collect execution feedback information corresponding to the control strategy during the charging control execution process. The execution feedback information includes charging efficiency changes, battery temperature rise response, and execution time overhead; The strategy optimization module is used to adjust and update the mapping relationship between the multi-dimensional state index and the control strategy according to the execution feedback information.

[0066] For the specific definition of the intelligent operation and maintenance management device for charging piles, please refer to the definition of the intelligent operation and maintenance management method for charging piles above, and will not be repeated here. The various modules in the above-mentioned intelligent operation and maintenance management device for charging piles can be implemented in whole or in part through software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0067] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for intelligent operation and maintenance management of a charging pile is implemented.

[0068] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed: Collect battery status parameters and charging pile operation status parameters; Extract trends from the time series of battery status parameters and charging pile operating status parameters, perform correlation analysis, and generate a multi-dimensional status index that represents the current operating status of the charging object and charging equipment; Based on the multi-dimensional state index, the corresponding control strategy is selected from the charging control strategy set, and the adaptability of the stage control parameters set for different remaining power intervals in the control strategy is evaluated with the current state index; According to the adaptability evaluation results, the charging operation of the corresponding strategy is executed.

[0069] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Collect battery status parameters and charging pile operation status parameters; Extract trends from the time series of battery status parameters and charging pile operating status parameters, perform correlation analysis, and generate a multi-dimensional status index that represents the current operating status of the charging object and charging equipment; Based on the multi-dimensional state index, the corresponding control strategy is selected from the charging control strategy set, and the adaptability of the stage control parameters set for different remaining power intervals in the control strategy is evaluated with the current state index; According to the adaptability evaluation results, the charging operation of the corresponding strategy is executed.

[0070] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. 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 above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0071] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0072] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A charging pile intelligent operation and maintenance management method, characterized in that: The method comprises: Collect battery status parameters and charging pile operation status parameters; Extract trends from the time series changes of the battery status parameters and the charging pile operating status parameters, and perform correlation analysis to generate a multidimensional status index representing the current operating status of the charging object and the charging equipment; Based on the multidimensional state index, a corresponding control strategy is selected from a set of charging control strategies, and a compatibility evaluation is performed between the stage control parameters set for different remaining power intervals in the control strategy and the current state index; According to the adaptability evaluation results, the charging operation of the corresponding strategy is executed.

2. The charging pile intelligent operation and maintenance management method according to claim 1, characterized in that: The method extracts trends from the time series changes of the battery status parameters and the charging pile operation status parameters, and performs correlation analysis to generate a multidimensional state index representing the current operation status of the charging object and the charging device, specifically including: Setting a sliding time window, extracting the change slope and relative change rate of the voltage, SOC state value and temperature in the battery state parameters, and obtaining a battery trend feature vector representing the current response trend of the battery; By calculating the correlation index between the current fluctuation frequency and the temperature rise rate in the operating state parameters of the charging pile, a pile trend feature vector representing the equipment load change characteristics is obtained; A state index structure including multiple dimensions is constructed based on the battery trend feature vector and the pile trend feature vector, and index quantization coding is performed based on the threshold interval corresponding to the feature value of each dimension to obtain the multi-dimensional state index.

3. The charging pile intelligent operation and maintenance management method according to claim 2, characterized in that: The method of calculating the correlation index between the current fluctuation frequency and the temperature rise rate in the charging pile operation state parameters to obtain the pile body trend feature vector that characterizes the equipment load change characteristics specifically includes: Obtaining a current data sequence of a preset period, identifying the number of fluctuation events in the current data sequence that exceeds a preset fluctuation threshold, and then calculating the fluctuation frequency per unit time as a current stability feature of the device; Obtaining a temperature change curve within the period, and calculating an average temperature rise slope and a fluctuation amplitude of the temperature change curve as a temperature rise change index; A correlation function between the current stability characteristic and the temperature rise variation index is constructed to obtain a corresponding synergistic variation coefficient, and the synergistic variation coefficient is used as a core component to generate the pile trend characteristic vector.

4. The charging pile intelligent operation and maintenance management method according to claim 1, characterized in that: The method of selecting a corresponding control strategy from a charging control strategy set based on the multi-dimensional state index and evaluating the adaptability of the stage control parameters set for different remaining power intervals in the control strategy with the current state index specifically includes: Based on the battery charging adaptability level and the charging pile load tolerance level in the multidimensional state index, calling the control strategy with the corresponding weight from multiple candidate control strategies; The stage control parameters corresponding to each remaining power segment of the control strategy are compared with the current state index item by item, and the adaptability score of the stage control parameters is calculated.

5. The charging pile intelligent operation and maintenance management method according to claim 4, characterized in that: The charging operation of the corresponding strategy is executed according to the result of the adaptability evaluation, specifically including: When the adaptability score meets a preset threshold, a charging operation of the corresponding stage control parameters is performed according to the control strategy; When the adaptability score is lower than a preset threshold, the power output upper limit, voltage setting value and / or phase switching timing in the current strategy are adjusted within a preset range, and the adjusted adaptability score is recalculated; When the adjusted fitness score does not reach the re-evaluation threshold, a suboptimal strategy is selected for re-adaptation, and corresponding stage control parameters are reconfigured.

6. The charging pile intelligent operation and maintenance management method according to claim 1, characterized in that: The charging pile intelligent operation and maintenance management method further includes: During the charging control execution process, collecting execution feedback information corresponding to the control strategy, the execution feedback information including charging efficiency changes, battery temperature rise response and execution time overhead; The mapping relationship between the multidimensional state index and the control strategy is adjusted and updated according to the execution feedback information.

7. A charging pile intelligent operation and maintenance management device, characterized in that: The device comprises: Data acquisition module, used to collect battery status parameters and charging pile operation status parameters; A trend modeling module is used to extract trends from the time series changes of the battery status parameters and the charging pile operating status parameters, perform correlation analysis, and generate a multidimensional status index representing the current operating status of the charging object and the charging equipment; a strategy matching module, configured to select a corresponding control strategy from a set of charging control strategies based on the multidimensional state index, and perform a compatibility evaluation between the stage control parameters set for different remaining power intervals in the control strategy and the current state index; The control execution module is used to execute the charging operation of the corresponding strategy according to the adaptability evaluation result.

8. The charging pile intelligent operation and maintenance management device according to claim 7, characterized in that: The trend modeling module specifically includes: A battery trend extraction submodule is used to set a sliding time window, extract the change slope and relative change rate of the voltage, SOC state value and temperature in the battery state parameters, and obtain a battery trend feature vector that characterizes the current response trend of the battery; The device load identification submodule is used to obtain a pile body trend feature vector representing the device load change characteristics by calculating the correlation index between the current fluctuation frequency and the temperature rise rate in the charging pile operation state parameters; The state index generation submodule is used to construct a state index structure containing multiple dimensions based on the battery trend feature vector and the pile trend feature vector, and perform index quantization encoding based on the threshold interval corresponding to the feature value of each dimension to obtain the multidimensional state index.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the charging pile intelligent operation and maintenance management method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the charging pile intelligent operation and maintenance management method according to any one of claims 1 to 6 are implemented.

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