New energy automobile charging platform management system, charging pile and storage medium

Abstract

The invention relates to the technical field of charging piles, in particular to a new energy automobile charging platform management system, a charging pile and a storage medium. According to the application, the charging matching degree is evaluated through the charging access identification module, then the operation data is collected by the charging process monitoring module, then the current state of the equipment is calculated by using the performance evaluation module, and finally the monitoring threshold value is dynamically updated by the early warning threshold value adjustment module according to the performance attenuation degree and the environmental condition. The characteristic that the equipment performance changes along with time is considered, and the dynamic influence of environmental factors is also considered, so that the accuracy and reliability of monitoring of the charging equipment are remarkably improved, and false alarms and missing alarms are reduced.

Description

Technical Field

[0001] The present application relates to the technical field of charging piles, and in particular to a new energy vehicle charging platform management system, a charging pile and a storage medium. Background Art

[0002] With the rapid development of the new energy vehicle industry, the scale of charging infrastructure construction continues to expand. The safe and stable operation of charging equipment is of great significance to ensuring the quality of new energy vehicle charging services. Therefore, establishing a complete charging platform management system is crucial to improving the operation and maintenance efficiency of charging equipment and the user charging experience.

[0003] Existing charging platform management systems usually use a fixed threshold monitoring method, which sets warning thresholds for charging voltage, current, temperature and other parameters, and triggers an alarm when the monitoring parameters exceed the threshold range. However, since the performance of charging equipment will gradually decay over time and environmental conditions will change dynamically, fixed warning thresholds cannot adapt to the actual situation of changes in equipment performance and operating environment, which can easily cause false alarms or missed alarms, reducing the accuracy and reliability of monitoring. This situation needs to be further improved. Summary of the invention

[0004] In order to solve the problem that the existing charging platform management system is prone to false alarms or missed alarms, the present application provides a new energy vehicle charging platform management system, a charging pile and a storage medium, which adopt the following technical solutions: In a first aspect, the present application provides a new energy vehicle charging platform management system, the charging platform management system comprising a charging device monitoring device, the charging device monitoring device comprising: A charging access identification module is used to respond to a new energy vehicle access instruction, obtain vehicle charging demand parameters and the working status of the charging device, input the charging demand parameters and the working status of the charging device into a preset matching evaluation model, and output a matching evaluation result; A charging process monitoring module, used to start charging process monitoring according to the matching evaluation result, record historical data of the charging process, monitor the charging status in real time, and send an evaluation instruction to the performance evaluation module when an abnormal state is detected; A performance evaluation module, configured to receive the evaluation instruction, calculate the performance parameters of the charging device based on the historical data, establish a device performance scoring model according to the performance parameters, and output the device performance attenuation coefficient to the early warning threshold adjustment module; An early warning threshold adjustment module is used to dynamically calculate and update the early warning threshold of the monitoring parameter according to the received performance attenuation coefficient and the real-time operating environment parameter, and send the updated early warning threshold to the monitoring execution module; The monitoring execution module is used to perform real-time monitoring according to the received warning threshold value, and when the detection parameter exceeds the warning threshold value, send a control instruction to the charging process monitoring module to trigger corresponding protection measures.

[0005] By adopting the above technical solution, the existing technology usually uses fixed thresholds for monitoring and early warning. However, the charging equipment will experience performance degradation during long-term use, such as reduced power conversion efficiency, increased heat generation, etc., which may cause the originally appropriate early warning threshold to no longer be applicable; the present application first evaluates the charging matching degree through the charging access identification module, and then the charging process monitoring module collects the operation data, and then uses the performance evaluation module to calculate the current status of the device, and finally the early warning threshold adjustment module dynamically updates the monitoring threshold according to the degree of performance degradation and environmental conditions, taking into account the characteristics of the change of equipment performance over time, and taking into account the dynamic influence of environmental factors, thereby significantly improving the accuracy and reliability of charging equipment monitoring and reducing false alarms and missed alarms.

[0006] Optionally, the charging access identification module includes: The parameter acquisition unit is used to respond to the new energy vehicle access command and obtain the vehicle charging demand parameters and the working status of the charging equipment; the load analysis unit is used to calculate the equipment load rate and power fluctuation index in different periods based on the historical load data of the charging equipment, and generate the dynamic load characteristics of the equipment; A temperature compensation unit, used to calculate the maximum available power and temperature margin of the device according to the heat dissipation characteristics of the charging device and the ambient temperature, and generate a temperature compensation coefficient; The matching degree calculation unit is used to substitute the vehicle charging demand parameter, the working state of the charging device, the dynamic load characteristics and the temperature compensation coefficient into a preset matching degree evaluation model, and output a matching degree evaluation result.

[0007] By adopting the above technical scheme, since the actual available power of the charging equipment is affected by many dynamic factors, relying solely on the rated parameters of the equipment for charging matching evaluation often cannot accurately reflect the real-time service capability of the equipment; for example, during the peak period of electricity consumption, multiple charging piles work simultaneously at a charging station, resulting in excessive load, or heat dissipation is limited in high temperature weather in summer. If new charging tasks are still connected according to the rated power, it is easy to cause equipment overload or overheating; the present application first collects basic data through the parameter acquisition unit, and then uses the load analysis unit to calculate the dynamic load characteristics of the equipment, and at the same time uses the temperature compensation unit to evaluate the impact of heat dissipation conditions on the available power. Finally, the matching degree calculation unit comprehensively considers these dynamic factors to perform matching degree evaluation. Through dynamic load analysis and temperature compensation calculation, the real-time service capability of the charging equipment is evaluated, the risk of overload and overheating is effectively avoided, and the use efficiency and safety of the charging equipment are improved.

[0008] Optionally, the performance evaluation module includes: A feature extraction unit, configured to receive the evaluation instruction, extract charging rate fluctuation features, power conversion features, and temperature change features from historical data, and generate standardized feature values ​​according to preset feature extraction rules; A dynamic weight calculation unit, used to query a preset weight mapping table according to the usage time and charging times of the charging device to obtain the contribution weight of each feature to the performance score; The performance attenuation calculation unit is used to substitute the standardized characteristic value and the contribution weight into a preset nonlinear attenuation model to calculate and output a device performance attenuation coefficient.

[0009] By adopting the above technical solutions, traditional methods often rely too much on a single performance indicator or adopt a simple linear attenuation model, which cannot truly reflect the performance change law of the equipment; for example, after a charging station's charging equipment has been used for one year, although the power conversion efficiency has only slightly decreased, the charging rate fluctuation has increased significantly, the temperature rise rate has accelerated, and the performance attenuation characteristics are difficult to accurately calculate. The present application first uses a feature extraction unit to extract the characteristics of the three dimensions of charging rate fluctuation, power conversion and temperature change from historical data, and performs standardized processing; then the dynamic weight calculation unit is used to adaptively adjust the weight contribution of each feature according to the operating conditions of the equipment; finally, the performance attenuation calculation unit is used to convert these features into performance attenuation coefficients through a nonlinear attenuation model, which can more accurately characterize the attenuation law of equipment performance over time and provide a decision-making basis for equipment maintenance and updates.

[0010] Optionally, the feature extraction unit includes: a historical data reading subunit, for extracting charging rate fluctuation characteristics, power conversion characteristics and temperature change characteristics from historical data in response to the evaluation instruction; An entropy value calculation subunit, used to calculate the information entropy values ​​of the charging rate fluctuation characteristics, power conversion characteristics and temperature change characteristics, and generate a characteristic entropy sequence; A time series analysis subunit is used to perform time series decomposition on the characteristic entropy sequence, extract the entropy value change trend characteristics and mutation characteristics, and establish an entropy value anomaly score; A feature enhancement subunit, used to fuse the entropy value anomaly score with the original feature to form an enhanced feature set; The normalization processing subunit is used to perform normalization processing on the enhanced feature set according to a preset feature extraction rule to generate a standardized feature value.

[0011] By adopting the above technical solution, since the performance degradation of charging equipment is often accompanied by small but continuous abnormal changes, for example, during the use of a certain charging pile, although indicators such as charging rate and power conversion efficiency are still within the normal range, the fluctuation pattern of these parameters has changed slightly. If it is not discovered in time, it may cause sudden failure of the equipment. The application first obtains basic features through the historical data reading subunit, and then uses the entropy value calculation subunit to convert these features into information entropy sequences. Then, the time series analysis subunit captures the changing trend and mutation characteristics of the entropy value, and then the feature enhancement subunit fuses the entropy value anomaly score with the original feature. Finally, the standardization processing subunit generates a normalized feature value. By capturing the inherent regular changes of the data, early identification of equipment performance degradation is achieved, and the sensitivity and early warning capability of feature extraction are improved.

[0012] Optionally, the warning threshold adjustment module includes: A parameter receiving unit, used to receive the performance attenuation coefficient and real-time operating environment parameters; Fault feature analysis unit, used to statistically analyze historical fault data, identify high-incidence fault types and fault occurrence patterns, and generate fault risk coefficients; The load status analysis unit is used to detect the charging load change trend in real time, determine the load mutation risk, and generate the load risk coefficient; A threshold calculation unit, used to dynamically update the warning threshold of the monitoring parameter according to the performance attenuation coefficient, the failure risk coefficient and the load risk coefficient; The threshold output unit is used to send the updated warning threshold to the monitoring execution module.

[0013] By adopting the above-mentioned technical scheme, the present application first obtains basic data through the parameter receiving unit, and then uses the fault feature analysis unit to mine historical fault modes and quantify them into risk coefficients. At the same time, the load status analysis unit evaluates the real-time load change trend. Finally, the threshold calculation unit comprehensively considers the information of the three dimensions of performance degradation, fault risk and load risk, and dynamically generates an updated early warning threshold, thereby realizing the intelligent adjustment of the early warning threshold, improving the accuracy and reliability of the early warning mechanism, and effectively reducing the operation risk of the equipment.

[0014] Optionally, the fault feature analysis unit includes: A fault record storage subunit is used to store historical fault data according to fault type and occurrence time; The fault frequency statistics subunit is used to calculate the occurrence frequency of different types of faults and generate a fault frequency distribution diagram; The fault level assessment subunit is used to determine the fault level according to the impact of the fault on the charging equipment and the maintenance cost; the environmental correlation analysis subunit is used to analyze the correlation between the fault and temperature and humidity to obtain the environmental correlation; The risk coefficient calculation subunit is used to generate a fault risk coefficient based on the fault frequency distribution diagram, the fault level and the environmental relevance.

[0015] By adopting the above technical scheme, traditional methods often only focus on fault frequency statistics, ignoring the severity of faults and the impact of environmental factors, resulting in one-sided risk assessment results. For example, a charging station frequently experiences insulation failures in the rainy season. Although the failure frequency is not high, the maintenance cost is high and there are safety hazards. In the high temperature season, over-temperature protection often occurs, with a high frequency but relatively small impact. The present application first establishes a structured fault database through a fault record storage subunit, and then uses a fault frequency statistics subunit to analyze the fault distribution law. At the same time, the fault severity is considered through a fault level assessment subunit, and the environmental correlation analysis subunit assesses the impact of environmental factors. Finally, the risk coefficient calculation subunit combines these features to generate a risk assessment result. Through the fusion analysis of multi-dimensional features, a comprehensive assessment of fault risks is achieved, the accuracy and pertinence of risk warnings are improved, and a more reliable basis is provided for equipment maintenance decisions.

[0016] Optionally, the threshold calculation unit includes: A risk factor normalization subunit, used to normalize the performance attenuation coefficient, the failure risk coefficient and the load risk coefficient to generate a standardized risk index; A seasonal characteristic compensation subunit is used to generate a seasonal correction factor according to the charging characteristic differences in different seasons; The time period correction subunit is used to calculate the time period adjustment coefficient based on the charging demand characteristics of different time periods in a day; A threshold comprehensive calculation subunit, used for weighted combination of the standardized risk index, the seasonal correction factor and the time period adjustment coefficient to generate a dynamic warning threshold; The threshold smoothing subunit is used to perform smoothing on the dynamic warning threshold.

[0017] By adopting the above-mentioned technical scheme, in order to make the warning threshold more in line with the actual operating characteristics of the charging equipment, the present application first unifies the dimensions through the risk factor normalization subunit, and then introduces the adjustment mechanism of the time and space dimensions by the seasonal characteristic compensation subunit and the time period correction subunit respectively. Then, the multi-factor fusion operation is performed through the threshold comprehensive calculation subunit, and finally, the mutation effect is eliminated through the threshold smoothing subunit, thereby realizing the adaptation of the warning threshold to the time and space characteristics and reducing the loss of operational efficiency caused by excessive warnings.

[0018] In a second aspect, the present application provides a charging pile, including the above-mentioned new energy vehicle charging platform management system, and further including: A charging control unit, used to perform a charging operation; A communication interface unit, used for data interaction with external devices; A power conversion unit, used for converting AC and DC power; A display control unit, used for displaying charging status information; The storage unit is used to store charging data and system parameters.

[0019] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for implementing the new energy vehicle charging platform management system according to any one of claims 1 to 7 includes: Responding to a new energy vehicle access instruction, obtaining vehicle charging demand parameters and a charging device working state, inputting the charging demand parameters and the charging device working state into a preset matching evaluation model, and outputting a matching evaluation result; Initiate charging process monitoring according to the matching evaluation result, record historical data of key parameters in the charging process, monitor the charging status in real time, and send an evaluation instruction to the performance evaluation module when an abnormal state is detected; receiving the evaluation instruction, calculating the performance parameters of the charging device based on the historical data, establishing a device performance scoring model according to the performance parameters, and outputting the device performance attenuation coefficient to the early warning threshold adjustment module; Dynamically calculate and update the warning threshold of the monitoring parameter according to the received performance attenuation coefficient and the real-time operating environment parameter, and send the updated warning threshold to the monitoring execution module; Real-time monitoring is performed according to the received warning threshold, and when the detection parameter exceeds the warning threshold, a control instruction is sent to the charging process monitoring module to trigger corresponding protection measures.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: 1. This application first evaluates the charging matching degree through the charging access identification module, then collects the operation data through the charging process monitoring module, and then uses the performance evaluation module to calculate the current status of the equipment. Finally, the warning threshold adjustment module dynamically updates the monitoring threshold according to the performance degradation degree and environmental conditions, taking into account the characteristics of the equipment performance changing over time, and taking into account the dynamic influence of environmental factors, thereby significantly improving the accuracy and reliability of charging equipment monitoring and reducing false alarms and missed alarms; 2. Since the actual available power of the charging equipment is affected by a variety of dynamic factors, charging matching evaluation based solely on the rated parameters of the equipment often cannot accurately reflect the real-time service capabilities of the equipment; for example, during the peak period of electricity consumption at a charging station, multiple charging piles work simultaneously, resulting in excessive load, or heat dissipation is limited in high temperature weather in summer. If new charging tasks are still connected according to the rated power, it is easy to cause equipment overload or overheating; this application first collects basic data through the parameter acquisition unit, and then uses the load analysis unit to calculate the dynamic load characteristics of the equipment, and at the same time uses the temperature compensation unit to evaluate the impact of heat dissipation conditions on available power. Finally, the matching degree calculation unit comprehensively considers these dynamic factors to perform matching degree evaluation. Through dynamic load analysis and temperature compensation calculation, the real-time service capability of the charging equipment is evaluated, the risk of overload and overheating is effectively avoided, and the use efficiency and safety of the charging equipment are improved; 3. Since the performance degradation of charging equipment is often accompanied by small but continuous abnormal changes, for example, during the use of a charging pile, although indicators such as charging rate and power conversion efficiency are still within the normal range, the fluctuation pattern of these parameters has changed slightly. If it is not discovered in time, it may lead to sudden failure of the equipment. This application first obtains basic features through the historical data reading subunit, and then uses the entropy calculation subunit to convert these features into information entropy sequences. Then, the time series analysis subunit captures the changing trend and mutation characteristics of the entropy value, and then the feature enhancement subunit fuses the entropy value anomaly score with the original feature. Finally, the standardization processing subunit generates a normalized feature value. By capturing the inherent regular changes of the data, early identification of equipment performance degradation is achieved, and the sensitivity and early warning capability of feature extraction are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a structural block diagram of a charging equipment monitoring device according to an embodiment of the present application; Figure 2 This is a structural block diagram of a charging access identification module according to an embodiment of the present application; Figure 3 is a structural block diagram of the performance evaluation module of an embodiment of the present application; Figure 4 is a structural block diagram of a feature extraction unit in an embodiment of the present application; Figure 5 It is a structural block diagram of the warning threshold adjustment module of the embodiment of the present application; Figure 6 is a structural block diagram of a fault feature analysis unit in an embodiment of the present application; Figure 7 is a structural block diagram of a threshold value calculation unit in an embodiment of the present application; Figure 8 This is a structural block diagram of a charging pile in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The new energy vehicle charging platform management system is an intelligent monitoring system for charging equipment. Through the coordinated work of five core modules, namely charging access identification, charging process monitoring, performance evaluation, warning threshold adjustment and monitoring execution, it realizes intelligent management of the entire process from vehicle access to charging completion. It can not only evaluate the matching degree between the vehicle and the charging equipment, but also perform performance evaluation, and through dynamic adjustment of the warning threshold, it can realize monitoring of the charging process and risk warning, thereby significantly improving the safety and reliability of charging facilities.

[0023] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0024] In one embodiment, Figure 1 As shown, the present application provides a new energy vehicle charging platform management system, the charging platform management system includes a charging device monitoring device, and the charging device monitoring device includes: The charging access identification module is used to respond to the new energy vehicle access instruction, obtain the vehicle charging demand parameters and the working status of the charging equipment, input the charging demand parameters and the working status of the charging equipment into a preset matching evaluation model, and output the matching evaluation result.

[0025] In this embodiment, the charging access identification module receives the access request signal of the new energy vehicle through the vehicle communication unit, obtains the vehicle charging demand parameters including the rated charging voltage, maximum charging current, remaining battery capacity, expected charging power, etc., and collects the working state parameters of the charging device such as the current output power, device temperature, idle state, etc. These parameters together constitute the basic evaluation data set of the charging process.

[0026] Specifically, the matching evaluation model uses a weighted scoring method to calculate the comprehensive matching degree based on the power matching degree, temperature margin and equipment status score. For example, when the vehicle requires 60kW charging power, the rated power of the charging equipment is 120kW, the equipment temperature is 35℃ (good temperature margin), and it is in an idle state, the calculated matching score is 0.92, indicating that the equipment can safely and stably meet the vehicle charging needs.

[0027] The charging process monitoring module is used to start the charging process monitoring according to the matching evaluation results, record the historical data of the charging process, monitor the charging status in real time, and send an evaluation instruction to the performance evaluation module when an abnormal state is detected.

[0028] In this embodiment, the charging process monitoring module starts the monitoring program after the matching score reaches a preset threshold (such as 0.8), establishes a real-time monitoring database including parameters such as charging current, charging voltage, charging power, and charging time, and continuously records the changes of these parameters at intervals of 1 second to form complete charging process history data.

[0029] Specifically, the monitoring module sets up multiple levels of abnormal state judgment criteria, including current mutation rate exceeding 20% / s, voltage fluctuation amplitude exceeding ±5% of the rated value, charging power instability exceeding 10%, etc. When any parameter is detected to be out of the normal range, the system will trigger the abnormal state identification and generate an evaluation instruction containing information such as abnormal type, occurrence time, parameter deviation, etc.

[0030] The performance evaluation module is used to receive evaluation instructions, calculate the performance parameters of the charging equipment based on historical data, establish an equipment performance scoring model according to the performance parameters, and output the equipment performance attenuation coefficient to the early warning threshold adjustment module.

[0031] In this embodiment, after receiving the evaluation instruction, the performance evaluation module calls the historical data of the last 30 days to extract key performance indicators such as charging efficiency, power stability, and temperature rise rate.

[0032] Specifically, the performance scoring model uses a nonlinear weighted algorithm to map the changing trends of various performance indicators to a scoring range of 0 to 1. For example, when the charging efficiency drops from the original 95% to 92% and the power fluctuation rate rises from 3% to 5%, the performance attenuation coefficient calculated by the model is 0.15, indicating that the device performance has slightly attenuated.

[0033] The warning threshold adjustment module is used to dynamically calculate and update the warning threshold of the monitoring parameter according to the received performance attenuation coefficient and real-time operating environment parameters, and send the updated warning threshold to the monitoring execution module.

[0034] In this embodiment, the warning threshold adjustment module comprehensively considers the influence of the performance attenuation coefficient and environmental parameters, and adopts an adaptive algorithm to dynamically adjust the warning threshold of the monitoring parameter.

[0035] Specifically, when the performance attenuation coefficient is 0.15 and the ambient temperature is 35°C, the system adjusts the warning threshold of the charging current from 95% of the rated value to 90%, and tightens the fluctuation tolerance of the charging power from ±5% to ±4%, thereby detecting potential risks in advance. This dynamic adjustment mechanism ensures the timeliness and accuracy of the warning.

[0036] The monitoring execution module is used to perform real-time monitoring according to the received warning threshold. When the detection parameter exceeds the warning threshold, a control instruction is sent to the charging process monitoring module to trigger corresponding protection measures.

[0037] In this embodiment, the monitoring execution module receives the dynamically updated warning threshold, establishes a multi-dimensional monitoring system including current monitoring, voltage monitoring, temperature monitoring, etc., and realizes continuous monitoring of the charging process by comparing the monitoring data and the warning threshold in real time.

[0038] Specifically, when a parameter is detected to exceed the warning threshold, such as the charging current exceeds the adjusted 90% threshold, the system will immediately generate a control instruction. The control instruction may include protective measures such as reducing the charging power, starting forced cooling, and suspending charging. The specific measures to be implemented depend on the type and degree of deviation of the over-limit parameter. For example, when the current exceeds the limit by 10%, the system first reduces the charging power; if it is still out of control, the charging suspension operation is executed.

[0039] In one embodiment, Figure 2 As shown, the charging access identification module includes: The parameter acquisition unit is used to respond to the new energy vehicle access instruction and obtain the vehicle charging demand parameters and the working status of the charging equipment.

[0040] In this embodiment, the parameter acquisition unit receives the charging request signal sent by the new energy vehicle through the communication channel of the vehicle CAN bus or the charging interface, and the signal contains basic information such as vehicle ID, battery type, remaining power, etc. At the same time, the unit also collects the current working status information of the charging device, including key parameters such as output voltage range, maximum output power, and equipment operation time.

[0041] Specifically, when a new energy vehicle is plugged into the charging gun, the system first verifies the communication handshake signal, and then completes parameter collection within 500ms. For example, for a vehicle equipped with a 100kWh ternary lithium battery, the system obtains charging demand parameters including: rated charging voltage 400V, maximum charging current 200A, current remaining power 20%, expected charging power 80kW, etc.; at the same time, the working status of the charging device is collected: current output power 0kW (idle state), device temperature 28℃, cumulative operating time 2000 hours, etc.

[0042] The load analysis unit is used to calculate the equipment load rate and power fluctuation index in different time periods based on the historical load data of the charging equipment, and generate the dynamic load characteristics of the equipment.

[0043] In this embodiment, the load analysis unit retrieves the equipment operation data of the last 7 days from the system database, divides each day into 96 15-minute periods, and counts the average load level and power fluctuation of each period.

[0044] Specifically, the load analysis unit uses a sliding time window method to calculate the load rate and power fluctuation index. For example, during the morning peak period of 8:00-9:00 on weekdays, the average load rate of the equipment reaches 85%, and the power fluctuation index is 0.15; while during the night period of 23:00-5:00 the next day, the average load rate is only 30%, and the power fluctuation index is 0.08.

[0045] The temperature compensation unit is used to calculate the maximum available power and temperature margin of the device according to the heat dissipation characteristics of the charging device and the ambient temperature, and generate a temperature compensation coefficient.

[0046] In this embodiment, the temperature compensation unit establishes a heat balance model based on the heat dissipation system parameters of the charging device (such as radiator area, fan speed, etc.) and real-time ambient temperature data.

[0047] Specifically, when the ambient temperature is 35°C, the system first calculates the maximum heat dissipation capacity of the heat dissipation system. For example, the maximum available power of a 120kW charging device at this temperature may need to be reduced to 100kW, and the temperature margin is 15°C. The temperature compensation coefficient is calculated using a piecewise function: when the ambient temperature is in the range of -10°C to 45°C, the compensation coefficient decreases by 0.05 for every 5°C increase, and finally a temperature compensation coefficient between 0 and 1 is generated.

[0048] The matching degree calculation unit is used to substitute the vehicle charging demand parameters, the working status of the charging equipment, the dynamic load characteristics and the temperature compensation coefficient into the preset matching degree evaluation model and output the matching degree evaluation result.

[0049] In this embodiment, the matching degree calculation unit adopts a multi-factor weighted scoring model to integrate data of multiple dimensions such as vehicle demand parameters, equipment status, load characteristics, and temperature compensation into a comprehensive score.

[0050] In one embodiment, Figure 3 As shown, the performance evaluation module includes: The feature extraction unit is used to receive the evaluation instruction, extract the charging rate fluctuation characteristics, power conversion characteristics and temperature change characteristics from the historical data, and generate a standardized feature value according to a preset feature extraction rule.

[0051] In this embodiment, after receiving the evaluation instruction, the feature extraction unit extracts the charging record data of the last 90 days from the system database. The unit uses a sliding window method to extract features from the data of three dimensions: charging rate, power conversion efficiency, and temperature change.

[0052] Specifically, during the feature extraction process, the charging rate fluctuation feature is obtained by calculating the standard deviation of the current change rate during the charging process. For example, the fluctuation value under normal conditions is within ±3%; the power conversion feature is represented by the average value of the input-output power ratio, which is usually maintained above 96% for new equipment; the temperature change feature is characterized by the temperature rise rate per unit time, and the normal value does not exceed 2°C / min. These original feature values ​​are normalized to the maximum and minimum values ​​and converted into standardized feature values ​​between 0 and 1.

[0053] The dynamic weight calculation unit is used to query a preset weight mapping table according to the usage time and charging times of the charging device to obtain the contribution weight of each feature to the performance score.

[0054] In this embodiment, the dynamic weight calculation unit dynamically adjusts the weight ratio of each feature in the performance evaluation according to the cumulative usage time and charging times of the charging device. The weight mapping table is constructed based on a large amount of experimental data, taking into account the changing rules of the importance of various performance parameters of the device at different usage stages, and realizing the dynamic adjustment of the evaluation criteria.

[0055] The performance attenuation calculation unit is used to substitute the standardized characteristic value and contribution weight into the preset nonlinear attenuation model to calculate and output the equipment performance attenuation coefficient.

[0056] In this embodiment, the performance attenuation calculation unit adopts an improved nonlinear attenuation model, where the model input includes standardized eigenvalues ​​and dynamic weights, and a performance attenuation coefficient between 0 and 1 is obtained through nonlinear mapping, where 0 indicates intact performance and 1 indicates complete performance degradation.

[0057] In one embodiment, Figure 4 As shown, the feature extraction unit includes: The historical data reading subunit is used to respond to the evaluation instruction and extract the charging rate fluctuation characteristics, power conversion characteristics and temperature change characteristics from the historical data.

[0058] In this embodiment, the historical data reading subunit adopts a distributed data reading solution to extract the equipment operation data of the latest 180 days from the real-time database and the historical database at the same time.

[0059] Specifically, for the charging rate fluctuation characteristics, the system records the mean and variance of the current change rate during each charging process; for the power conversion characteristics, the statistical distribution of the input-output power ratio is calculated; for the temperature change characteristics, the temperature gradient and change rate of different parts of the equipment are collected.

[0060] The entropy value calculation subunit is used to calculate the information entropy value of the charging rate fluctuation characteristics, power conversion characteristics and temperature change characteristics, and generate a characteristic entropy sequence.

[0061] In this embodiment, the entropy value calculation subunit uses a sliding window method to calculate the information entropy of each feature sequence. By setting 48 sample points as the window size, moving 4 sample points each time, and calculating the Shannon entropy of the data in the window, the uncertainty and complexity of the feature sequence are quantified.

[0062] Specifically, for each feature sequence, the data is first discretized into 10 equally spaced intervals, the probability distribution of each interval is calculated, and then substituted into the information entropy formula. For example, when the charging rate fluctuation data distribution is relatively concentrated, its entropy value is about 2.1; when the distribution becomes discrete, the entropy value may rise to 3.5, indicating that the system volatility has increased.

[0063] The time series analysis subunit is used to perform time series decomposition on the characteristic entropy sequence, extract the entropy value change trend characteristics and mutation characteristics, and establish the entropy value anomaly score.

[0064] In this embodiment, the time series analysis subunit uses the wavelet transform method to perform multi-scale decomposition on the entropy value sequence to extract trend items, period items, and random items. By analyzing the change characteristics of these components, abnormal patterns and mutation points in the entropy value sequence are identified, providing a basis for subsequent feature enhancement.

[0065] The feature enhancer unit is used to fuse the entropy anomaly score with the original features to form an enhanced feature set.

[0066] In this embodiment, the feature enhancement subunit adopts an adaptive weighted fusion strategy to combine the entropy value anomaly score with the original feature. Furthermore, the fusion weight is dynamically adjusted according to the credibility of the anomaly score.

[0067] The normalization processing subunit is used to normalize the enhanced feature set according to the preset feature extraction rules to generate a standardized feature value.

[0068] In this embodiment, the normalization processing subunit performs normalization processing on the enhanced feature set, not only considering the mean and standard deviation of the data, but also introducing a robustness factor to reduce the impact of outliers on the normalization results.

[0069] In one embodiment, Figure 5 As shown, the warning threshold adjustment module includes: The parameter receiving unit is used to receive the performance attenuation coefficient and the real-time operating environment parameters.

[0070] In this embodiment, the parameter receiving unit adopts a double buffer data receiving mechanism to collect the performance attenuation coefficient and environmental parameter data of the charging device in real time. The environmental parameters include key indicators such as ambient temperature, humidity, dust concentration, and grid voltage fluctuation.

[0071] Specifically, the system aligns and preprocesses the collected data according to timestamps, and after data smoothing, it serves as input for subsequent analysis.

[0072] The fault feature analysis unit is used to statistically analyze historical fault data, identify the types of frequent faults and the laws of fault occurrence, and generate fault risk coefficients.

[0073] In this embodiment, the fault feature analysis unit uses an improved association rule mining algorithm to perform data mining on the fault records of the last 12 months.

[0074] Specifically, through data analysis, it is found that when the ambient temperature exceeds 38°C and lasts for more than 4 hours, the probability of power module failure increases by 50%; when the grid voltage fluctuation exceeds ±5% and the equipment performance attenuation coefficient is greater than 0.3, the control system failure rate increases by 40%. Based on these rules, the system calculates the failure risk coefficient under the current working conditions.

[0075] The load status analysis unit is used to detect the charging load change trend in real time, determine the load mutation risk, and generate the load risk coefficient.

[0076] In this embodiment, the load state analysis unit uses a time series prediction model to monitor and predict the change trend of the charging load in real time.

[0077] Specifically, when the load is detected to increase by more than 50% within 15 minutes, or it is predicted that the load will continue to exceed 85% of the rated power within the next 2 hours, the system will increase the load risk factor. For example, during the morning peak period, the load of a fast charging station quickly increased from 30kW to 75kW, and the predicted trend is still rising. At this time, the calculated load risk factor is 0.78, indicating a high risk of load mutation.

[0078] The threshold calculation unit is used to dynamically update the warning threshold of the monitoring parameter according to the performance attenuation coefficient, the failure risk coefficient and the load risk coefficient.

[0079] In this embodiment, the threshold calculation unit uses an adaptive weighted fusion algorithm to comprehensively consider the performance attenuation coefficient, the fault risk coefficient and the load risk coefficient, and dynamically adjusts the warning threshold of each monitoring parameter.

[0080] The threshold output unit is used to send the updated warning threshold to the monitoring execution module.

[0081] In this embodiment, the threshold output unit adopts a real-time message push mechanism to send the updated warning threshold to the monitoring execution module through an encrypted channel. The system supports differentiated push strategies and adopts different update cycles for threshold adjustments of different importance levels.

[0082] In one embodiment, Figure 6 As shown, the fault feature analysis unit includes: The fault record storage subunit is used to store historical fault data according to fault type and occurrence time.

[0083] In this embodiment, the fault record storage subunit adopts a distributed time series database architecture to establish a multi-dimensional fault data storage structure. The system classifies and stores fault data according to fault type, occurrence time, duration, fault code, fault description and other dimensions, and establishes corresponding indexes to improve query efficiency.

[0084] The fault frequency statistics subunit is used to calculate the occurrence frequency of different types of faults and generate a fault frequency distribution diagram.

[0085] In this embodiment, the fault frequency statistics subunit uses the sliding time window method to perform statistical analysis on the fault frequency at different time scales. The system calculates the fault frequency of the day, week, month, and quarter respectively, and comprehensively evaluates the fault occurrence pattern by weighted average.

[0086] The fault level assessment subunit is used to determine the fault level according to the impact of the fault on the charging equipment and the repair cost.

[0087] In this embodiment, the fault level assessment subunit adopts a fuzzy comprehensive evaluation method to establish a fault level assessment system including multiple evaluation indicators such as equipment downtime, maintenance cost, safety risk, etc. The system divides fault levels into four levels: critical, severe, moderate and minor.

[0088] The environmental correlation analysis subunit is used to analyze the correlation between the fault and the temperature and humidity to obtain the environmental correlation.

[0089] In this embodiment, the environmental correlation analysis subunit uses a time series correlation analysis method. The system collects environmental parameter data 24 hours before and after the fault occurs, and establishes a mapping model between environmental factors and fault occurrence.

[0090] The risk coefficient calculation subunit is used to generate a fault risk coefficient based on the fault frequency distribution diagram, the fault level and the environmental relevance.

[0091] In this embodiment, the risk coefficient calculation subunit adopts a multi-factor weighted fusion algorithm to comprehensively calculate the fault frequency, level weight and environmental relevance to obtain the final fault risk coefficient. The system uses a nonlinear mapping function to normalize each indicator to the [0,1] interval.

[0092] In one embodiment, Figure 7 As shown, the threshold calculation unit includes: The risk factor normalization subunit is used to normalize the performance attenuation coefficient, the failure risk coefficient and the load risk coefficient to generate a standardized risk index.

[0093] The seasonal characteristic compensation subunit is used to generate a seasonal correction factor according to the differences in charging characteristics in different seasons.

[0094] In this embodiment, the seasonal characteristic compensation subunit uses time series decomposition technology to extract the seasonal pattern of the charging load, divides a year into four seasonal cycles, and models and analyzes the charging characteristics of each season.

[0095] Specifically, the compensation model considers the impact of seasonal factors on device performance: the compensation factor in summer is 0.85 because high temperature affects device heat dissipation; the compensation factor in winter is 0.9 because low temperature affects battery performance; the compensation factor in spring and autumn is 0.95 because environmental conditions are relatively ideal.

[0096] The time period correction subunit is used to calculate the time period adjustment coefficient based on the charging demand characteristics of different time periods in a day.

[0097] In this embodiment, the time period correction subunit adopts a dynamic time series clustering method to analyze the changing pattern of charging demand within 24 hours a day, identify the time distribution of peak period, off-peak period and valley period, and generate time period related adjustment coefficients.

[0098] Specifically, the system divides a day into six periods: morning peak, morning flat peak, noon trough, afternoon flat peak, evening peak, and night trough. For example, during the evening peak period, the threshold of a monitoring parameter will be adjusted by a factor of 0.85 to cope with the larger charging load.

[0099] The threshold comprehensive calculation subunit is used to perform weighted combination of standardized risk indicators, seasonal correction factors and time period adjustment coefficients to generate dynamic warning thresholds.

[0100] In this embodiment, the threshold comprehensive calculation subunit uses a hierarchical weighted fusion algorithm to combine the standardized risk index, seasonal correction factor and time period adjustment coefficient according to different weights.

[0101] The threshold smoothing subunit is used to smooth the dynamic warning threshold.

[0102] In this embodiment, the threshold smoothing processing subunit adopts an exponential smoothing algorithm to continuously process the calculated dynamic warning threshold to avoid the impact of sudden changes in the threshold on the system. The system controls the update ratio of the new and old thresholds by setting a smoothing coefficient.

[0103] Specifically, the smoothing process uses an exponential smoothing formula: S t =α×Y t+(1-α)×S t-1 , where α is 0.3, Y t is the currently calculated threshold, S t-1 is the smoothed value of the previous moment. For example, when the newly calculated threshold is 64.25A and the smoothed threshold of the previous moment is 70A, the new threshold after smoothing is 68.23A, which ensures the stability of the threshold adjustment and avoids frequent alarms of the monitoring system.

[0104] In one embodiment, Figure 8 As shown, the present application provides a charging pile, including a new energy vehicle charging platform management system that implements the above-mentioned system embodiments, and also includes a charging control unit, a communication interface unit, a power conversion unit, a display control unit and a storage unit. The charging control unit is used to perform charging operations; the communication interface unit is used to exchange data with external devices; the power conversion unit is used to perform AC and DC power conversion; the display control unit is used to display charging status information; and the storage unit is used to store charging data and system parameters.

[0105] Those of ordinary skill in the art can understand that all or part of the processes in each module of the above-mentioned embodiment system can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present 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 memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0106] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A new energy vehicle charging platform management system, characterized in that: The charging platform management system includes a charging equipment monitoring device, and the charging equipment monitoring device includes: A charging access identification module is used to respond to a new energy vehicle access instruction, obtain vehicle charging demand parameters and the working status of the charging device, input the charging demand parameters and the working status of the charging device into a preset matching evaluation model, and output a matching evaluation result; A charging process monitoring module, used to start charging process monitoring according to the matching evaluation result, record historical data of the charging process, monitor the charging status in real time, and send an evaluation instruction to the performance evaluation module when an abnormal state is detected; A performance evaluation module, configured to receive the evaluation instruction, calculate the performance parameters of the charging device based on the historical data, establish a device performance scoring model according to the performance parameters, and output the device performance attenuation coefficient to the early warning threshold adjustment module; An early warning threshold adjustment module is used to dynamically calculate and update the early warning threshold of the monitoring parameter according to the received performance attenuation coefficient and the real-time operating environment parameter, and send the updated early warning threshold to the monitoring execution module; The monitoring execution module is used to perform real-time monitoring according to the received warning threshold value, and when the detection parameter exceeds the warning threshold value, send a control instruction to the charging process monitoring module to trigger corresponding protection measures.

2. The new energy vehicle charging platform management system according to claim 1, characterized in that: The charging access identification module includes: A parameter acquisition unit, used to respond to the new energy vehicle access instruction and obtain the vehicle charging demand parameters and the working status of the charging equipment; A load analysis unit is used to calculate the equipment load rate and power fluctuation index in different time periods based on the historical load data of the charging equipment, and generate dynamic load characteristics of the equipment; A temperature compensation unit, used to calculate the maximum available power and temperature margin of the device according to the heat dissipation characteristics of the charging device and the ambient temperature, and generate a temperature compensation coefficient; The matching degree calculation unit is used to substitute the vehicle charging demand parameter, the working state of the charging device, the dynamic load characteristics and the temperature compensation coefficient into a preset matching degree evaluation model and output a matching degree evaluation result.

3. The new energy vehicle charging platform management system according to claim 1, characterized in that: The performance evaluation module includes: A feature extraction unit, configured to receive the evaluation instruction, extract charging rate fluctuation features, power conversion features, and temperature change features from historical data, and generate standardized feature values ​​according to preset feature extraction rules; A dynamic weight calculation unit, used to query a preset weight mapping table according to the usage time and charging times of the charging device to obtain the contribution weight of each feature to the performance score; The performance attenuation calculation unit is used to substitute the standardized characteristic value and the contribution weight into a preset nonlinear attenuation model to calculate and output a device performance attenuation coefficient.

4. The new energy vehicle charging platform management system according to claim 3 is characterized in that: The feature extraction unit comprises: a historical data reading subunit, for extracting charging rate fluctuation characteristics, power conversion characteristics and temperature change characteristics from historical data in response to the evaluation instruction; An entropy value calculation subunit, used to calculate the information entropy values ​​of the charging rate fluctuation characteristics, power conversion characteristics and temperature change characteristics, and generate a characteristic entropy sequence; A time series analysis subunit is used to perform time series decomposition on the characteristic entropy sequence, extract the entropy value change trend characteristics and mutation characteristics, and establish an entropy value anomaly score; A feature enhancement subunit, used to fuse the entropy value anomaly score with the original feature to form an enhanced feature set; The normalization processing subunit is used to perform normalization processing on the enhanced feature set according to a preset feature extraction rule to generate a standardized feature value.

5. The new energy vehicle charging platform management system according to claim 1, characterized in that: The warning threshold adjustment module includes: A parameter receiving unit, used to receive the performance attenuation coefficient and real-time operating environment parameters; Fault feature analysis unit, used to statistically analyze historical fault data, identify high-incidence fault types and fault occurrence patterns, and generate fault risk coefficients; The load status analysis unit is used to detect the charging load change trend in real time, determine the load mutation risk, and generate the load risk coefficient; A threshold calculation unit, used to dynamically update the warning threshold of the monitoring parameter according to the performance attenuation coefficient, the failure risk coefficient and the load risk coefficient; The threshold output unit is used to send the updated warning threshold to the monitoring execution module.

6. The new energy vehicle charging platform management system according to claim 5, characterized in that: The fault feature analysis unit comprises: A fault record storage subunit is used to store historical fault data according to fault type and occurrence time; The fault frequency statistics subunit is used to calculate the occurrence frequency of different types of faults and generate a fault frequency distribution diagram; A fault level assessment subunit, used to determine the fault level according to the impact of the fault on the charging equipment and the repair cost; The environmental correlation analysis subunit is used to analyze the correlation between the fault and temperature and humidity to obtain the environmental correlation; The risk coefficient calculation subunit is used to generate a fault risk coefficient based on the fault frequency distribution diagram, the fault level and the environmental relevance.

7. The new energy vehicle charging platform management system according to claim 5, characterized in that: The threshold calculation unit comprises: A risk factor normalization subunit, used to normalize the performance attenuation coefficient, the failure risk coefficient and the load risk coefficient to generate a standardized risk index; A seasonal characteristic compensation subunit is used to generate a seasonal correction factor according to the charging characteristic differences in different seasons; The time period correction subunit is used to calculate the time period adjustment coefficient based on the charging demand characteristics of different time periods in a day; A threshold comprehensive calculation subunit, used for weighted combination of the standardized risk index, the seasonal correction factor and the time period adjustment coefficient to generate a dynamic warning threshold; The threshold smoothing subunit is used to perform smoothing on the dynamic warning threshold.

8. A charging pile, characterized in that: The new energy vehicle charging platform management system according to any one of claims 1 to 7 further comprises: A charging control unit, used to perform a charging operation; A communication interface unit, used for data interaction with external devices; A power conversion unit, used for converting AC and DC power; A display control unit, used for displaying charging status information; The storage unit is used to store charging data and system parameters.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for implementing a new energy vehicle charging platform management system according to any one of claims 1 to 7 comprises: Responding to a new energy vehicle access instruction, obtaining vehicle charging demand parameters and a charging device working state, inputting the charging demand parameters and the charging device working state into a preset matching evaluation model, and outputting a matching evaluation result; Initiate charging process monitoring according to the matching evaluation result, record historical data of key parameters in the charging process, monitor the charging status in real time, and send an evaluation instruction to the performance evaluation module when an abnormal state is detected; receiving the evaluation instruction, calculating the performance parameters of the charging device based on the historical data, establishing a device performance scoring model according to the performance parameters, and outputting the device performance attenuation coefficient to the early warning threshold adjustment module; Dynamically calculate and update the warning threshold of the monitoring parameter according to the received performance attenuation coefficient and the real-time operating environment parameter, and send the updated warning threshold to the monitoring execution module; Real-time monitoring is performed according to the received warning threshold, and when the detection parameter exceeds the warning threshold, a control instruction is sent to the charging process monitoring module to trigger corresponding protection measures.

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