Intelligent charging pile operation safety management and control system

Abstract

The invention discloses an intelligent charging pile operation safety management and control system, and belongs to the technical field of charging pile safety management and control. The charging pile monitoring module is used for monitoring operation data of each charging pile, and the operation data comprises electrical data, environmental data and mechanical data; the safety state control model is used for obtaining the safety state control model of the charging pile through a neural network according to the operation data of the charging pile, simulating the stable state and the safety change trend of the charging pile under different working conditions, and predicting the operation state of the charging pile; meanwhile, an evaluation index of the operation data is set, a target threshold value is set for the evaluation index, and a safety strategy is preset for the evaluation index; therefore, the safety attribute of the charging pile is judged, and a safety strategy corresponding to the operation data is matched. The safety and stability of the charging pile are improved.

Description

Technical Field

[0001] The present application relates to an intelligent charging pile operation safety management and control system, which belongs to the technical field of charging pile safety management and control. Background Art

[0002] The charging pile safety management and control system is a comprehensive management system that integrates intelligent, networked, and information technologies. It aims to ensure the safe operation of charging piles, improve charging efficiency, and optimize user experience. With the rapid development of the new energy vehicle industry and the continuous growth in the number of charging piles, the intelligent charging pile operation safety management and control system meets and realizes comprehensive control of the operating status of charging piles through real-time monitoring, data analysis and prediction, and the execution of safety strategies. Therefore, it is a general trend to optimize the operation and decision-making of the system and improve efficiency and user experience through technologies such as artificial intelligence and big data. Summary of the invention

[0003] According to one aspect of the present application, a smart charging pile operation safety management and control system is provided, which comprehensively controls the operation status of the charging pile.

[0004] The intelligent charging pile operation safety management and control system is characterized by including: Multiple connected charging stations; A charging pile monitoring module, used to monitor the operating data of each charging pile, wherein the operating data includes electrical data, environmental data and mechanical data; A safety state control model is used to obtain the safety state control model of the charging pile through a neural network according to the operation data of the charging pile, simulate the stable state and safety change trend of the charging pile under different working conditions, and realize the prediction of the operation state of the charging pile; at the same time, set the evaluation index of the operation data, set the target threshold for the evaluation index, and preset the safety strategy for the evaluation index; thereby judging the safety attribute of the charging pile and matching the safety strategy corresponding to the operation data; The electrical data is used to ensure the electrical insulation performance of the charging pile, the environmental data is used to ensure that the charging pile meets different environmental standards, and the mechanical data is used to ensure the mechanical performance of the charging pile.

[0005] Furthermore, the evaluation indicators of the operating data include: Electrical performance indicators, including operating temperature indicators and current indicators; Environmental performance indicators, the environmental temperature indicators include environmental temperature indicators and environmental humidity indicators; Mechanical performance indicators, including vibration indicators and displacement indicators.

[0006] Furthermore, the method for constructing the safety state control model includes: Three model data sets were constructed based on electrical data, environmental data, and mechanical data; The three model data sets are divided into three types of base models; The outputs of the three types of base models are cross-modal feature fused through a hierarchical attention mechanism to obtain a safe state control model.

[0007] Furthermore, the outputs of the three base models are fused across modal features through a hierarchical attention mechanism, including: Align the timing sequences of electrical data, environmental data, and mechanical data based on dynamic time warping; Assign cross-modal attention weights based on the physical spatial correlation between electrical and mechanical data; The cross-modal attention weight calculation satisfies: ; In the formula, is the query matrix, is the key matrix, is the feature dimension, and the softmax function is used to convert the weight matrix into a probability distribution form; Use environmental data to generate gating coefficients and dynamically adjust cross-modal attention weights; The time-aligned features are fused with the weighted cross-modal features to generate a security status representation.

[0008] Furthermore, setting a target threshold for the evaluation indicator includes: Inputting the charging pile characteristic data in the operating data into the safety state control model to obtain a target prediction value corresponding to the operating data; Determine a first prediction value and a second prediction value of the target prediction value, and use an average of the first prediction value and the second prediction value as a reference threshold; If the change amount of the reference threshold is less than the first preset threshold, the reference threshold is used as the target threshold; Wherein, the first predicted value is the maximum value among the target predicted values; The second predicted value is the minimum value among the target predicted values; The first preset threshold is used to indicate the tolerance of the stability of the reference threshold.

[0009] Furthermore, a security strategy is preset for the evaluation index, including: Compare the electrical performance index with the corresponding target threshold, generate first adjustment information when the electrical performance index is within 5-10% of the target threshold, and stop the charging pile authorization charging action when the electrical performance index exceeds 10% of the target threshold; Compare the environmental performance index with the corresponding target threshold, generate second adjustment information when the environmental performance index is within 10-20% of the target threshold, and stop the charging pile authorization charging action when the environmental performance index exceeds 20% of the target threshold; The mechanical performance index is compared with the corresponding target threshold. When the mechanical performance index is within 3-5% of the target threshold, the third adjustment information is generated. When the mechanical performance index exceeds 5% of the target threshold, the charging pile authorization charging action is stopped.

[0010] Furthermore, the first adjustment information includes: Determine the current operating temperature or charging current level of the charging pile; Adjusting the operating temperature or charging current level of the charging pile at the next moment; Or, determine whether the current operating temperature exceeds a target threshold by 5-10%; If the current operating temperature does not exceed 5-10% of the target threshold, the current charging current level is reduced or increased by at least one charging current level; Determine the charging current gear of the charging pile at the next moment.

[0011] Furthermore, the second adjustment information includes: Cooling or heat-insulating the charging pile environment; Alternatively, the charging pile is subjected to dehumidification or humidification treatment.

[0012] Furthermore, the third adjustment information includes: Add a shock-absorbing design to the charging pile or reduce the external load on the charging pile.

[0013] Furthermore, it also includes: A resource allocation module, which determines whether the operation data of a certain charging pile meets the first condition, and if so, disables the charging pile in a corresponding period of time and allocates other charging piles for users to use; The first condition includes that the number of times the charging pile is used exceeds a first threshold and the number of times it is called exceeds a second threshold.

[0014] The beneficial effects of this application include: The intelligent charging pile operation safety management and control system provided in this application realizes comprehensive management and control of the operating status of the charging piles through real-time monitoring, data analysis and prediction, and the execution of safety strategies. The system not only improves the safety and stability of the charging piles, but also reduces management costs and maintenance difficulties, providing strong guarantees for the development of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a structural diagram of the smart charging pile operation safety management and control system in one implementation of the present application. DETAILED DESCRIPTION

[0016] The present application is described in detail below with reference to embodiments, but the present application is not limited to these embodiments.

[0017] See also Figure 1 , the intelligent charging pile operation safety management and control system is characterized by including: Multiple connected charging stations; A charging pile monitoring module, used to monitor the operating data of each charging pile, wherein the operating data includes electrical data, environmental data and mechanical data; A safety state control model is used to obtain the safety state control model of the charging pile through a neural network according to the operation data of the charging pile, simulate the stable state and safety change trend of the charging pile under different working conditions, and realize the prediction of the operation state of the charging pile; at the same time, set the evaluation index of the operation data, set the target threshold for the evaluation index, and preset the safety strategy for the evaluation index; thereby judging the safety attribute of the charging pile and matching the safety strategy corresponding to the operation data; The electrical data is used to ensure the electrical insulation performance of the charging pile, the environmental data is used to ensure that the charging pile meets different environmental standards, and the mechanical data is used to ensure the mechanical performance of the charging pile.

[0018] Specifically, the system includes multiple networked charging piles, which can transmit and communicate data through the network to achieve remote monitoring and management. The charging pile monitoring module can monitor the operating data of each charging pile in real time, including electrical data, environmental data and mechanical data. The safety state control model uses neural networks to process and analyze the operating data of the charging pile, simulate the stable state and safety change trend of the charging pile under different working conditions, and predict the operating state of the charging pile. The system sets the evaluation indicators and target thresholds of the operating data, and presets the safety strategy based on these evaluation indicators. When the operating data of the charging pile reaches or exceeds the target threshold, the system will automatically match the corresponding safety strategy to ensure the safe operation of the charging pile.

[0019] Among them, multiple networked charging piles provide charging services, transmit electric energy to electric vehicles, have network communication functions, and can upload operating data to the monitoring module in real time; the charging pile monitoring module monitors the operating data of each charging pile in real time. Electrical data such as current and power are used to ensure the electrical insulation performance and charging efficiency of the charging pile. Environmental data such as temperature and humidity are used to ensure the stability and safety of the charging pile under different environmental conditions. Mechanical data such as vibration and noise are used to evaluate the mechanical properties and durability of the charging pile. The safety state control model simulates the stable state and safety change trend of the charging pile through a neural network based on the operating data of the charging pile. Its working principle is specifically to receive the operating data uploaded by the charging pile monitoring module; use the neural network to process and analyze the data to establish a safety state control model for the charging pile; predict the operating state and safety change trend of the charging pile under different working conditions according to the model; set evaluation indicators and target thresholds, and match the corresponding safety strategy according to the operating data.

[0020] Through real-time monitoring and intelligent prediction, the system can promptly detect potential safety hazards of charging piles and adopt corresponding safety strategies to ensure the safe operation of charging piles. The system can automatically match safety strategies to improve the utilization efficiency and operation efficiency of charging piles.

[0021] The evaluation indicators of the operation data include: Electrical performance indicators, including operating temperature indicators and current indicators; Environmental performance indicators, the environmental temperature indicators include environmental temperature indicators and environmental humidity indicators; Mechanical performance indicators, including vibration indicators and displacement indicators.

[0022] Specifically, among the electrical performance indicators, temperature is one of the key factors affecting the performance and life of charging pile equipment. Too high or too low temperatures may cause equipment performance degradation or damage. The upper temperature limit specified in the national standard for charging piles is 50°C, and the lower limit may vary according to the specific standard (such as -30°C). In actual operation, it should be ensured that the operating temperature of the charging pile is within its design range. Current is a basic indicator for the operation of charging pile equipment, which directly affects the charging speed and safety. The current should be set according to the battery capacity and charging time, and is generally set between 0.5C-1C (C is the battery capacity, in ampere-hours Ah). For example, for a 60Ah battery, the charging current is generally set between 30A-60A.

[0023] Among the environmental performance indicators, ambient temperature directly affects the working performance and life of the charging pile. The charging pile should be able to work normally within a certain range of ambient temperature, such as -30℃~50℃ (the specific range may vary depending on the standard). Too high or too low humidity may cause damage to the circuit boards and components of the charging pile. The relative humidity requirement of the charging pile is usually between 5%-95%, and the ideal working environment humidity is 30%-50%.

[0024] Among the mechanical performance indicators, vibration affects the structural stability of the charging pile and the connection reliability of components. The charging pile should be able to withstand vibrations of a certain frequency and amplitude without affecting its normal operation. Displacement reflects the stability of the charging pile during installation or use. The charging pile should remain stable after installation, and there should be no obvious displacement or shaking.

[0025] The method for constructing the safety state control model includes: Three model data sets were constructed based on electrical data, environmental data, and mechanical data; The three model data sets are divided into three types of base models; Among them, the three base models are electrical data base model, environmental data base model and mechanical data base model; the electrical data base model can extract local features of current / voltage waveforms (such as harmonic distortion) through CNN, and GRU captures timing dependencies. The input features are normalized real-time current, voltage curves, and temperature gradients.

[0026] Specifically, the electrical data base model includes a data preprocessing module, a CNN (Convolutional Neural Network) feature extraction module, a GRU (Gated Recurrent Unit) timing modeling module, and a fully connected decision module. The data preprocessing module inputs the real-time current, voltage, and temperature gradient timing data, independently performs Z-score (standard score) or Min-Max standardization on each channel data, cuts the continuous data stream into a time window of fixed length (such as 100 sampling points), adds Gaussian noise or time distortion to enhance the robustness of the model, and outputs standardized timing data with a shape of (batch_size, T, 3); the CNN feature extraction module performs Z-score (standard score) or Min-Max standardization on the preprocessed timing window (T, 3) The input is taken, and the local features such as harmonic distortion are captured through multi-scale 1D convolution kernels (kernel_size=3,5). The maximum pooling downsampling (pool_size=2) retains the main features. At the same time, the residual block is selected to alleviate the gradient disappearance problem, thereby outputting the compressed feature sequence (T',C), where T' is the number of time steps after compression and C is the number of feature channels; the GRU time series modeling module inputs the feature sequence (T',C) output by CNN, and captures the previous and next time series dependencies (hidden_units=64-128), applies attention weights to the GRU output, strengthens the key time steps, and outputs the time series feature vector, which is configured to return the last moment state or all moment states; the fully connected decision module inputs the feature vector output by GRU, and optionally uses a multi-layer perceptron (MLP) for nonlinear feature transformation, linearly activates the output layer, and outputs the prediction result.

[0027] The environmental data-based model can predict environmental adaptability (such as the insulation performance attenuation curve under high humidity) through the coupled relationship between temperature and humidity-altitude. The input feature is the sliding window mean of the environmental sensor data.

[0028] Specifically, the environmental data base model inputs a multi-sensor sliding window mean sequence with a shape of (batch_size, T, 3), where batch_size represents the number of samples selected for one training, T is the time window length (such as a 7-day mean sequence), and 3 corresponds to the mean of temperature, humidity, and altitude. It predicts the insulation performance attenuation curve parameters, such as attenuation coefficient and half-life, and judges the environmental level, such as normal / warning / dangerous, and finally outputs the environmental adaptability index. Among them, the environmental data base model includes a data preprocessing module, a coupling relationship modeling module, and a nonlinear decision module. The data preprocessing module calculates the sliding window mean of the original sensor data, and can superimpose statistics such as variance and maximum value. The second-order / third-order interaction terms of temperature, humidity, and altitude are generated through polynomial features. The altitude threshold adjustment is introduced when humidity is >80%, and the Z-score is standardized according to the feature dimension to retain the distribution information. The problem of inconsistent sampling rates of multiple sensors is handled by interpolation; the coupling relationship modeling module captures the dynamic evolution mode of temperature, humidity-altitude, and processes long-range dependencies in parallel, which is suitable for the real-time requirements of industrial scenarios. Explicitly model the bidirectional effects of temperature and humidity → altitude, altitude → temperature and humidity, and assist in explaining the change of coupling strength over time; the nonlinear decision module uses a residual network structure (ResNet) to alleviate the disappearance of deep gradients. The Squeeze-and-Excitation (SE) module is introduced to adaptively adjust the importance of channels; multi-task output is performed to predict the parameters of the insulation performance attenuation curve, and the temperature, humidity change rate, and altitude anomaly probability are predicted at the same time.

[0029] The mechanical data-based model can use LSTM to model the long-term trend of the vibration signal, and peak detection to capture sudden mechanical shocks. The input features are vibration spectrum features, specifically the frequency domain energy distribution after FFT transformation.

[0030] Specifically, the continuous vibration signal is divided into short time frames with fixed lengths, and overlap is set between frames to avoid information loss. After framing, each time domain frame is windowed to reduce spectrum leakage, and each frame signal is transformed by FFT to obtain the power spectral density (PSD). According to the characteristic frequency distribution of mechanical component faults, the frequency bands are divided, and the energy of each frequency band is counted as the feature vector. The sliding window method is used to detect sudden changes in signal amplitude, and the adaptive threshold is calculated, such as the mean + 3 times the standard deviation. The peak points that exceed the threshold and meet the minimum interval are marked, the number of peaks per unit time is counted as the impact intensity indicator, the energy proportion of the high-frequency band is calculated, the dynamic threshold is set, and the energy surge event is marked as a potential precursor to failure. A two-layer LSTM (Long Short-Term Memory) network is constructed. The input layer receives the frequency domain feature sequence. The first layer LSTM (64 units) captures the short-term frequency domain pattern, and the second layer LSTM (32 units) extracts the long-term trend features. The Dropout layer (0.2 ratio) prevents overfitting; the peak feature is spliced ​​with the LSTM output and fused through the fully connected layer. The peak feature is standardized and input into the dense layer, spliced ​​with the LSTM output, and then the health probability is output through Sigmoid activation.

[0031] The outputs of the three types of base models are cross-modal feature fused through a hierarchical attention mechanism to obtain a safe state control model.

[0032] Specifically, after obtaining the three model data sets, they are input into the corresponding base models respectively. The base models extract key features from their respective data sets and make a preliminary assessment of the safety status based on these features. Among them, cross-modal feature fusion is a key step in the construction of the safety status control model. Since electrical, environmental and mechanical data come from different data sources, they have different feature representations and semantic information. In order to make full use of these data, an effective fusion method is needed. The hierarchical attention mechanism is an effective cross-modal feature fusion method. By applying the attention mechanism at different levels, important feature information can be adaptively selected and irrelevant or redundant information can be suppressed. Specifically, the hierarchical attention mechanism can first perform feature selection on the output of each base model, and then fuse these features at a higher level to obtain a more comprehensive and accurate safety status assessment result.

[0033] The safety status control model constructed by the above method can comprehensively consider electrical, environmental, mechanical and other data to conduct a comprehensive and accurate assessment of the system's safety status. It not only helps to timely discover and warn potential safety hazards, but also provides strong support for equipment maintenance and management. In addition, the model can be expanded and optimized according to actual needs to meet the safety status assessment needs in different scenarios.

[0034] Furthermore, the outputs of the three base models are fused across modal features through a hierarchical attention mechanism, including: Align the timing sequences of electrical data, environmental data, and mechanical data based on dynamic time warping; Assign cross-modal attention weights based on the physical spatial correlation between electrical and mechanical data; The cross-modal attention weight calculation satisfies: ; In the formula, is the query matrix, is the key matrix, is the feature dimension, and the softmax function is used to convert the weight matrix into a probability distribution form; Use environmental data to generate gating coefficients and dynamically adjust cross-modal attention weights; The time-aligned features are fused with the weighted cross-modal features to generate a security status representation.

[0035] Specifically, different data all have time series attributes. The dynamic laws of the data that need to be analyzed with the charging cycle, environmental changes or mechanical wear are all related to the overall operating status of the charging pile, and outliers may reflect safety hazards. Since electrical data, environmental data and mechanical data have different sampling frequencies and timing characteristics, they first need to be aligned based on dynamic time warping (DTW) or other timing alignment methods. DTW is a method for measuring the similarity between two time series, which can handle the nonlinear distortion of time series on the time axis. Through alignment, it can be ensured that different data sources have comparable feature values ​​at the same time point. In cross-modal feature fusion, the attention mechanism is used to dynamically assign weights between different data sources to emphasize the features that are more important for safety status assessment. According to the physical spatial correlation between electrical data and mechanical data, a cross-modal attention mechanism can be designed to assign weights.

[0036] Among them, the spatial correlation weights of electrical data and mechanical data are calculated. In the calculation process, electrical data is used as the source mode and mechanical data is used as the target mode, which is expressed as: ; In the formula, is the query matrix after the electrical data features are linearly transformed; is the key matrix after the linear transformation of mechanical data features, with a dimension of [d×d], extracted from electrical features through linear transformation; d is the feature dimension; is a scaling factor used to prevent the softmax gradient from disappearing due to the dot product result being too large; Mechanical data is used as the source mode and electrical data is used as the target mode, which is expressed as: ; In the formula, is the query matrix after the mechanical data features are linearly transformed; is the key matrix after linear transformation of electrical data features, with a dimension of [d×d], extracted from mechanical features through linear transformation; d is the feature dimension; is a scaling factor used to prevent the softmax gradient from disappearing due to the dot product result being too large; Calculate the fused spatial correlation: ; In the formula, represents the spatial attention matrix, with a dimension of [N×N], where N is the number of sensor nodes, Represents element-wise multiplication.

[0037] Environmental data (such as temperature, humidity, etc.) may have a significant impact on the operation of electrical and mechanical systems. Therefore, environmental data can be used to generate a gating coefficient to dynamically adjust the cross-modal attention weights. The gating coefficient can be adjusted according to the real-time changes in environmental data, thereby achieving dynamic adjustment of the weights between different data sources.

[0038] Among them, environmental data can suppress unreliable attention weights, generate gating coefficients through environmental data, and control the fusion ratio of electrical and mechanical features; Gating coefficient: ; In the formula, represents the gating network weight matrix, with dimension [2d×d], which maps the concatenated features to the gating space; Represents a vector concatenation operation; Represents the gating network bias vector, with a dimension of [d×1]; represents the environmental feature vector, with a dimension of [d×1], Represents the preliminary fusion features, with a dimension of [d×1].

[0039] Dynamic weight adjustment: ; ; In the formula, is the temperature coefficient, which controls the sharpness of the attention distribution; represents the gated attention weight matrix; represents the final attention weight matrix.

[0040] Finally, the time-aligned features are fused with the weighted cross-modal features. This can be achieved through simple concatenation, weighted summation, or other feature fusion methods. The fused feature vector contains comprehensive information of electrical, environmental, and mechanical data, which can be used to generate accurate safety status representation.

[0041] Among them, cross-modal weighted fusion is realized to obtain the feature fusion weight: ; In the formula, Represents matrix multiplication; and Represent the electrical and mechanical feature vectors, respectively, with a dimension of [d×1]; The feature fusion weights are obtained and finally the safety status representation is generated.

[0042] Safety status characterization can be used for subsequent decision support, fault diagnosis or early warning systems to help users identify and deal with potential safety hazards in a timely manner.

[0043] Setting target thresholds for the evaluation indicators includes: Inputting the charging pile characteristic data in the operating data into the safety state control model to obtain a target prediction value corresponding to the operating data; Determine a first prediction value and a second prediction value of the target prediction value, and use an average of the first prediction value and the second prediction value as a reference threshold; If the change amount of the reference threshold is less than the first preset threshold, the reference threshold is used as the target threshold; Wherein, the first predicted value is the maximum value among the target predicted values; The second predicted value is the minimum value among the target predicted values; The first preset threshold is used to indicate the tolerance of the stability of the reference threshold.

[0044] Specifically, real-time operating data is obtained from the charging pile monitoring module. These data include electrical data, environmental data, and mechanical data, which together constitute the characteristic data set of the charging pile. The collected charging pile characteristic data is input into the pre-trained safety state control model. The model is based on a neural network and can simulate the stable state and safety change trend of the charging pile under different working conditions. The safety state control model outputs the corresponding prediction value based on the input characteristic data. The prediction value reflects the operating state that the charging pile may reach in the future.

[0045] Among them, among the obtained target prediction values, the maximum value is identified as the first prediction value, and the minimum value is identified as the second prediction value. These two prediction values ​​represent the highest and lowest operating states that the charging pile may reach, respectively. The average of the first prediction value and the second prediction value is used as the reference threshold. The reference threshold represents an intermediate level that the charging pile may reach under normal operating conditions. If the change in the reference threshold is less than the first preset threshold, it means that the reference threshold is relatively stable and can be used as the target threshold. This means that when the operating state of the charging pile fluctuates around the target threshold, it can be considered safe.

[0046] It is worth noting that the reference threshold is calculated multiple times in a row (for example, multiple predictions are set within a time window) and its change is observed. The change reflects the stability of the reference threshold. The calculated reference threshold change is compared with a preset first preset threshold. The first preset threshold is a tolerance indicator used to measure the acceptable range of the reference threshold change.

[0047] Furthermore, the setting of the first preset threshold value needs to be determined according to the actual operating conditions and safety requirements of the charging pile. Under certain working conditions, it should be small enough to ensure the stability of the target threshold; at the same time, under certain working conditions, it should be large enough to accommodate normal operating fluctuations. In practical applications, it is necessary to continuously monitor the operating data of the charging pile and adjust the target threshold value according to actual conditions. This ensures that the target threshold value is always synchronized with the actual operating status of the charging pile. Through the above steps, a reasonable target threshold value can be set for the operating data evaluation index of the charging pile, thereby realizing accurate monitoring and management of the operating status of the charging pile.

[0048] Preset security policies for the evaluation indicators, including: Compare the electrical performance index with the corresponding target threshold, generate first adjustment information when the electrical performance index is within 5-10% of the target threshold, and stop the charging pile authorization charging action when the electrical performance index exceeds 10% of the target threshold; Compare the environmental performance index with the corresponding target threshold, and when the environmental performance index is within 10-20% of the target threshold, generate second adjustment information, and when the environmental performance index exceeds 20% of the target threshold, stop the charging pile authorization charging action; The mechanical performance index is compared with the corresponding target threshold. When the mechanical performance index is within 3-5% of the target threshold, the third adjustment information is generated. When the mechanical performance index exceeds 5% of the target threshold, the charging pile authorization charging action is stopped.

[0049] Specifically, the electrical performance indicators of the charging pile, such as operating temperature indicators and current indicators, are monitored in real time. These indicators are compared with the pre-set target thresholds. When the electrical performance indicators are within the range of 5%-10% of the target threshold, it indicates that the electrical performance of the charging pile has begun to show slight abnormalities, but has not yet reached a dangerous level. At this time, the system generates the first adjustment information, prompting the operation and maintenance personnel to pay attention to and check the electrical system of the charging pile, and may take preliminary adjustment measures, such as adjusting the charging current or heat dissipation strategy. When the electrical performance indicators exceed 10% of the target threshold, it indicates that the electrical performance of the charging pile has seriously deviated from the normal range and there are safety hazards. At this time, the system immediately stops the authorized charging action of the charging pile to prevent further safety risks.

[0050] Monitor the ambient temperature and humidity indicators of the environment in which the charging pile is located in real time. Compare these indicators with the pre-set target thresholds. When the environmental performance indicators are within the range of 10%-20% of the target threshold, it indicates that the environment in which the charging pile is located begins to become unfavorable for its normal operation. At this time, the system generates a second adjustment information to prompt the operation and maintenance personnel to pay attention to environmental changes and may take environmental control measures, such as turning on a dehumidifier. When the environmental performance indicators exceed 20% of the target threshold, it indicates that the environment in which the charging pile is located has seriously deteriorated and its safe operation cannot be guaranteed. At this time, the system also immediately stops the authorized charging action of the charging pile.

[0051] Monitor the mechanical performance indicators of the charging pile, such as vibration and displacement indicators, in real time. Compare these indicators with the pre-set target thresholds. When the mechanical performance indicators are within the range of 3%-5% of the target threshold, it indicates that the mechanical properties of the charging pile begin to fluctuate slightly or become abnormal. At this point, the system generates a third adjustment message, prompting the operation and maintenance personnel to check the mechanical structure of the charging pile and possibly take reinforcement or adjustment measures. When the mechanical performance indicators exceed 5% of the target threshold, it indicates that the mechanical properties of the charging pile have been severely damaged and there are safety hazards. At this point, the system also immediately stops the authorized charging action of the charging pile.

[0052] Among them, in actual applications, the specific parameters of the safety strategy (such as 5%-10%, 10%-20%, 3%-5%, etc.) may need to be adjusted according to the specific model of the charging pile, the use environment and the operation and maintenance experience. The system should be able to continuously monitor the operation data of the charging pile and generate adjustment information or stop charging authorization in a timely manner according to the safety strategy. At the same time, the operation and maintenance personnel should be able to respond quickly to the system's prompts and alarms to ensure the safe operation of the charging pile. Through the implementation of the above safety strategy, accurate monitoring and management of the operating status of the charging pile can be achieved, effectively preventing potential safety risks.

[0053] The first adjustment information includes: Determine the current operating temperature or charging current level of the charging pile; Adjusting the operating temperature or charging current level of the charging pile at the next moment; Or, determine whether the current operating temperature exceeds a target threshold by 5-10%; If the current operating temperature does not exceed 5-10% of the target threshold, the current charging current level is reduced or increased by at least one charging current level; Determine the charging current gear of the charging pile at the next moment.

[0054] Specifically, in one embodiment, the system monitors the current operating temperature or charging current level of the charging pile in real time. According to the maximum / minimum operating temperature limit, the maximum / minimum charging current limit, etc., the operating temperature or charging current level of the charging pile at the next moment is adjusted. When adjusting the temperature, according to the actual situation, the system adjusts the operating temperature by adjusting the speed of the cooling fan, enabling or disabling additional cooling equipment, etc.

[0055] In another embodiment, these real-time data are compared with a preset target threshold. At the current operating temperature, if the current charging current level does not match the current range associated with the target threshold (for example, the current is too high or too low), the system can determine whether the charging current level needs to be adjusted. The system adjusts the charging current level by adjusting the current controller inside the charging pile or sending instructions to the battery management system (BMS) of the electric vehicle.

[0056] Among them, the system first determines whether the current operating temperature exceeds the target threshold by 5-10%. If the current operating temperature does not exceed the target threshold by 5-10%, but the charging current gear is high (which may cause overheating risk), the system considers lowering the current charging current gear by at least one gear. On the contrary, if the current operating temperature is within the safe range and the charging current gear is low (which may cause low charging efficiency), the system considers increasing the current charging current gear by at least one gear. After completing the adjustment of the charging current gear, the system determines the charging current gear of the charging pile at the next moment and sends the corresponding instructions to the BMS of the charging pile or electric vehicle.

[0057] The second adjustment information includes: Cooling or heat-insulating the charging pile environment; Alternatively, the charging pile is subjected to dehumidification or humidification treatment.

[0058] Specifically, the second adjustment information makes necessary adjustments to the environment in which the charging pile is located according to the environmental performance indicators to ensure that it operates under suitable environmental conditions. The system monitors the temperature of the environment in which the charging pile is located in real time and compares it with the preset target threshold. If the ambient temperature is higher than a certain range of the target threshold (such as exceeding 10%-20% of the target threshold), the system determines that cooling treatment is required. Cooling measures include starting air conditioning equipment, fans or spray systems near the charging pile to reduce the ambient temperature. In some cases, the system may also assist in cooling by adjusting the heat dissipation strategy of the charging pile itself. If the ambient temperature is lower than a certain range of the target threshold, the system determines that insulation treatment is required. Insulation measures include turning off ventilation equipment near the charging pile, activating heating equipment, or taking other insulation measures.

[0059] The system monitors the humidity of the environment where the charging pile is located in real time and compares it with the preset target threshold. If the ambient humidity is higher than a certain range of the target threshold, the system determines that dehumidification is required. Dehumidification measures include starting a dehumidifier, increasing ventilation, or using other dehumidification methods. If the ambient humidity is lower than a certain range of the target threshold, the system determines that humidification is required. Humidification measures may include using a humidifier, reducing ventilation, or taking other humidification measures.

[0060] The system selects and executes appropriate adjustment strategies based on the real-time monitoring results of ambient temperature and humidity. The execution of the adjustment strategy involves the control system of the charging pile itself, and also needs to be linked with other environmental control systems (such as air conditioners, dehumidifiers, etc.).

[0061] The third adjustment information includes: Add a shock-absorbing design to the charging pile or reduce the external load on the charging pile.

[0062] Specifically, the third adjustment information improves the structural stability and safety of the charging pile, mainly by increasing the shock absorption design or reducing the external load on the charging pile. First, identify the key parts of the charging pile that are susceptible to vibration or impact, such as connectors, support structures, etc. Introduce shock-absorbing materials, such as rubber pads, spring shock absorbers, etc., in key parts to absorb and disperse vibration energy. At the same time, consider using shock absorption methods such as dynamic shock absorption systems to improve the shock absorption effect. Optimize the overall structure of the charging pile, increase redundant support, improve the rigidity and stability of the structure, ensure that the shock absorption design is coordinated with the overall structure of the charging pile, and avoid introducing new safety hazards. Second, conduct a comprehensive assessment of the external loads borne by the charging pile, including wind loads, human factors, etc. According to the evaluation results, determine which loads can be reduced through design or management measures. Reduce the external load on the charging pile by optimizing the design. For example, use a streamlined design to reduce wind resistance, or increase snow protection measures to reduce snow loads. Formulate management measures to limit the external load on the charging pile to ensure that it does not cause additional loads on the charging pile.

[0063] Also includes: A resource allocation module, which determines whether the operation data of a certain charging pile meets the first condition, and if so, disables the charging pile in a corresponding period of time and allocates other charging piles for users to use; The first condition includes that the number of times the charging pile is used exceeds a first threshold and the number of times it is called exceeds a second threshold.

[0064] Specifically, the resource allocation module understands the current operating status, usage frequency, and potential problems of the charging pile based on the operating data of each charging pile, including key indicators such as the number of uses, the number of calls, the charging power, and the charging time. According to the preset first condition, the resource allocation module will determine whether a charging pile has reached the disabling standard. The first condition includes two key elements: the number of uses exceeding the first threshold indicates that the charging pile has been frequently used in a short period of time and may have reached the limit of its design life or is close to a state requiring maintenance. The number of calls exceeding the second threshold indicates that although the charging pile is frequently requested for use, it has not been able to successfully meet all charging needs due to various reasons (such as failures, maintenance, occupancy, etc.). When these two conditions are met at the same time, the resource allocation module will consider that the charging pile has a high safety risk or maintenance requirement, so it needs to consider disabling it during the corresponding period until the first condition is lifted.

[0065] It is worth noting that once a charging pile is determined to meet the disabling conditions, the resource allocation module will immediately mark it as unavailable and announce or notify it in the system. At the same time, the module will intelligently reallocate other available charging piles for users to use based on the current distribution of charging piles, user needs, charging efficiency and other factors. Ensure that users can quickly find available charging piles when they need to charge, avoiding charging interruptions or long waiting times. After disabling the charging pile, the resource allocation module will continue to monitor its status and the use of the charging pile of the reallocated user; if it is found that the status of the disabled charging pile has improved (such as returning to normal after maintenance), or the reallocated charging pile cannot meet user needs (such as long queuing time, low charging efficiency, etc.), the module will adjust the strategy in time to ensure the reasonable allocation and efficient use of resources.

[0066] The above are only a few embodiments of the present application and do not constitute any form of limitation to the present application. Although the present application is disclosed as above with preferred embodiments, it is not intended to limit the present application. Any technician familiar with the profession, without departing from the scope of the technical solution of the present application, using the technical content disclosed above to make slight changes or modifications are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. Intelligent charging pile operation safety management and control system, characterized by: include: Multiple connected charging stations; A charging pile monitoring module, used to monitor the operating data of each charging pile, wherein the operating data includes electrical data, environmental data and mechanical data; A safety state control model is used to obtain the safety state control model of the charging pile through a neural network according to the operation data of the charging pile, simulate the stable state and safety change trend of the charging pile under different working conditions, and realize the prediction of the operation state of the charging pile; at the same time, set the evaluation index of the operation data, set the target threshold for the evaluation index, and preset the safety strategy for the evaluation index; thereby judging the safety attribute of the charging pile and matching the safety strategy corresponding to the operation data; The electrical data is used to ensure the electrical insulation performance of the charging pile, the environmental data is used to ensure that the charging pile meets different environmental standards, and the mechanical data is used to ensure the mechanical performance of the charging pile.

2. The intelligent charging pile operation safety management and control system according to claim 1 is characterized in that: The evaluation indicators of the operation data include: Electrical performance indicators, including operating temperature indicators and current indicators; Environmental performance indicators, the environmental temperature indicators include environmental temperature indicators and environmental humidity indicators; Mechanical performance indicators, including vibration indicators and displacement indicators.

3. The intelligent charging pile operation safety management and control system according to claim 1 is characterized in that: The method for constructing the safety state control model includes: Three model data sets were constructed based on electrical data, environmental data, and mechanical data; The three model data sets are divided into three types of base models; The outputs of the three types of base models are cross-modal feature fused through a hierarchical attention mechanism to obtain a safe state control model.

4. The intelligent charging pile operation safety management and control system according to claim 3 is characterized in that: The outputs of the three base models are fused across modal features through a hierarchical attention mechanism, including: Align the timing sequences of electrical data, environmental data, and mechanical data based on dynamic time warping; Assign cross-modal attention weights based on the physical spatial correlation between electrical and mechanical data; The cross-modal attention weight calculation satisfies: ； In the formula, is the query matrix, is the key matrix, is the feature dimension, and the softmax function is used to convert the weight matrix into a probability distribution form; Use environmental data to generate gating coefficients and dynamically adjust cross-modal attention weights; The time-aligned features are fused with the weighted cross-modal features to generate a security status representation.

5. The intelligent charging pile operation safety management and control system according to claim 2 is characterized in that: Setting target thresholds for the evaluation indicators includes: Inputting the charging pile characteristic data in the operating data into the safety state control model to obtain a target prediction value corresponding to the operating data; Determine a first prediction value and a second prediction value of the target prediction value, and use an average of the first prediction value and the second prediction value as a reference threshold; If the change amount of the reference threshold is less than the first preset threshold, the reference threshold is used as the target threshold; Wherein, the first predicted value is the maximum value among the target predicted values; The second predicted value is the minimum value among the target predicted values; The first preset threshold is used to indicate the tolerance of the stability of the reference threshold.

6. The intelligent charging pile operation safety management and control system according to claim 5 is characterized in that: Preset security policies for the evaluation indicators, including: Compare the electrical performance index with the corresponding target threshold, generate first adjustment information when the electrical performance index is within 5-10% of the target threshold, and stop the charging pile authorization charging action when the electrical performance index exceeds 10% of the target threshold; Compare the environmental performance index with the corresponding target threshold, generate second adjustment information when the environmental performance index is within 10-20% of the target threshold, and stop the charging pile authorization charging action when the environmental performance index exceeds 20% of the target threshold; The mechanical performance index is compared with the corresponding target threshold. When the mechanical performance index is within 3-5% of the target threshold, the third adjustment information is generated. When the mechanical performance index exceeds 5% of the target threshold, the charging pile authorization charging action is stopped.

7. The intelligent charging pile operation safety management and control system according to claim 6, characterized in that: The first adjustment information includes: Determine the current operating temperature or charging current level of the charging pile; Adjusting the operating temperature or charging current level of the charging pile at the next moment; Or, determine whether the current operating temperature exceeds a target threshold by 5-10%; If the current operating temperature does not exceed 5-10% of the target threshold, the current charging current level is reduced or increased by at least one charging current level; Determine the charging current gear of the charging pile at the next moment.

8. The intelligent charging pile operation safety management and control system according to claim 6, characterized in that: The second adjustment information includes: Cooling or heat-insulating the charging pile environment; Alternatively, the charging pile is subjected to dehumidification or humidification treatment.

9. The intelligent charging pile operation safety management and control system according to claim 6, characterized in that: The third adjustment information includes: Add a shock-absorbing design to the charging pile or reduce the external load on the charging pile.

10. The intelligent charging pile operation safety management and control system according to claim 1, characterized in that: Also includes: A resource allocation module, which determines whether the operation data of a certain charging pile meets the first condition, and if so, disables the charging pile in a corresponding period of time and allocates other charging piles for users to use; The first condition includes that the number of times the charging pile is used exceeds a first threshold and the number of times it is called exceeds a second threshold.

Images