A charging data anomaly detection and safety processing system

Abstract

The present invention discloses a charging data anomaly detection and safety processing system, including a data acquisition module, an anomaly detection module, a safety processing module and a control center, wherein the data acquisition module, the anomaly detection module and the safety processing module are all connected to the control center in communication. The data acquisition module of the present invention dynamically adjusts the acquisition frequency according to the different stages of the charging process, and improves the acquisition frequency to ensure the accuracy and timeliness of the data. The anomaly detection module uses an intelligent algorithm to identify complex charging data patterns, so that it can automatically identify abnormal charging data patterns, thereby improving the accuracy and intelligence level of anomaly detection. The safety processing module adopts corresponding processing strategies based on the results of the abnormal judgment, and the processing is timely and targeted. At the same time, the system continuously monitors the changes in abnormal conditions to ensure the effectiveness of the processing measures. When the abnormal conditions are controlled, the system will enter the abnormal recovery stage to ensure the normal continuity of the charging process.

Description

Technical Field

[0001] The present invention relates to the field of charging technology, and in particular to a charging data anomaly detection and safety processing system. Background Art

[0002] With the popularity of electric devices such as electric cars, the demand for charging facilities is growing. However, during the charging process, various data anomalies may occur, such as excessive current, unstable voltage, and high temperature. These anomalies not only affect the charging efficiency, but may also pose a serious threat to the safety of equipment and users. At present, although there are some charging management systems, they are still insufficient in terms of the accuracy of charging data anomaly detection and the timeliness of safety processing. Summary of the invention

[0003] The purpose of the present invention is to provide a charging data anomaly detection and safety processing system to solve the deficiencies of the prior art.

[0004] The present invention is implemented by the following technical solution: a charging data anomaly detection and safety processing system, including a data acquisition module, an anomaly detection module, a safety processing module and a control center, wherein the data acquisition module, the anomaly detection module and the safety processing module are all connected to the control center in communication, wherein:

[0005] The data acquisition module is used to collect various data in the charging process in real time, including but not limited to current, voltage, temperature, power, and power;

[0006] The anomaly detection module analyzes the collected data according to preset rules and algorithms.

[0007] Determine whether there are any abnormal conditions;

[0008] When the safety processing module detects an abnormal situation, it takes corresponding safety measures, including but not limited to cutting off the power supply and sounding an alarm;

[0009] The control center is responsible for coordinating the work of each module and realizing the overall function of the system.

[0010] Furthermore, the data acquisition module includes a standard interface and a compatible interface, which collects various data during the charging process in real time, and transmits them to the abnormality detection module and the control center via wired or wireless means, and dynamically adjusts the acquisition frequency according to the different stages of the charging process: in the early and late stages of charging, the acquisition frequency is reduced; in the middle stage of charging, when the battery status changes rapidly, the acquisition frequency is increased.

[0011] Furthermore, the anomaly detection module analyzes the collected data according to preset rules and algorithms to determine whether there are any abnormal conditions, wherein the rules include:

[0012] (1) Current fluctuation rule: The current value should fluctuate within a certain range. If the current value exceeds the preset range of ±5%, it is judged as abnormal;

[0013] (2) Voltage fluctuation rule: The voltage value should fluctuate within a certain range. If the voltage value exceeds the preset range of ±3%, it is judged as abnormal;

[0014] (3) Temperature change rule: During the charging process, the battery temperature change rate should be within a certain range. If the temperature change rate exceeds 1°C per minute, it is considered abnormal.

[0015] (4) Electricity change rule: During the charging process, the electricity change should follow a linear or nearly linear rule. If the rate of change of electricity suddenly increases or decreases by more than 20%, it is considered abnormal.

[0016] (5) Power fluctuation rule: The power value should fluctuate within a certain range. If the power value exceeds the preset range of ±10%, it is judged as abnormal;

[0017] The algorithms mainly include the following three:

[0018] (1) Sliding window algorithm: used to monitor the changes of parameters including current, voltage, and temperature in real time. By setting a sliding window of fixed length, the mean and standard deviation of the data in the window are continuously calculated. If the data at a certain moment exceeds ±3 times the standard deviation of the window mean, it is judged as abnormal.

[0019] (2) Time series prediction algorithm: Use the autoregressive integrated moving average model time series model to predict the data value at the next time point. If the deviation between the actual data and the predicted value exceeds the preset threshold, it is judged as abnormal;

[0020] (3) Clustering algorithm: Use the K-means clustering algorithm to classify historical data. If the current data point is far away from any known cluster center, it is judged as an anomaly.

[0021] Furthermore, the control center automatically allocates corresponding processing strategies to the security processing module according to the abnormality judgment result of the abnormality detection module. The allocation of processing strategies is based on a comprehensive evaluation of the abnormality type and severity, specifically:

[0022] (1) Quantitative evaluation of abnormal characteristics:

[0023] By monitoring various charging parameters including but not limited to current, voltage, temperature, and charging rate, the deviation value from the normal working state is calculated, and the abnormal type is quantified according to the degree of deviation. The specific quantification standards are as follows:

[0024] Abnormal current (ΔI):

[0025] Slight abnormality: |ΔI|≤5%, indicating that the current fluctuation is within the normal range;

[0026] Severe abnormality: |ΔI|>20%, indicating that the current is significantly beyond the normal value;

[0027] Voltage abnormality (ΔV):

[0028] Slight abnormality: |ΔV|≤5%, indicating that the voltage fluctuation is within the normal range;

[0029] Severe abnormality: |ΔV|>15%, indicating that the voltage is beyond the normal working range;

[0030] Temperature anomaly (ΔT):

[0031] Slight abnormality: |ΔT|≤3℃, indicating slight temperature fluctuation;

[0032] Severe abnormality: |ΔT|>10℃, indicating that the temperature deviates from the normal working value;

[0033] Abnormal charging rate:

[0034] Slight abnormality: charging rate variation is within ±10%;

[0035] Severe abnormality: The charging rate changes by more than ±20%;

[0036] (2) Comprehensive abnormality severity score:

[0037] A comprehensive abnormality severity score is calculated based on the quantitative scores of each abnormality. Assume that the scores of each abnormality are S 电流 , S 电压 , S 温度 , S 充电速率 , the comprehensive score can be expressed as:

[0038] Comprehensive score = w1×S 电流 +w2×S 电压 +w3×S 温度 +w4×S 充电速率

[0039] Among them, w1, w2, w3, and w4 are the weights of various abnormal indicators, which are adjusted according to the actual situation;

[0040] (3) Allocation of exception handling strategies:

[0041] Comprehensive score ≤ 5: indicates a slight abnormality, and the following strategies are implemented: trigger an alarm to notify relevant personnel to pay attention to the abnormality; continue to monitor the abnormality and assess whether it will deteriorate further; do not take intervention measures unless the abnormality further worsens;

[0042] 5<Comprehensive score≤8: indicates moderate abnormality, and the following strategies are implemented: trigger an alarm and automatically notify relevant personnel; implement some protective measures, such as reducing the charging rate and limiting the charging power; continue to monitor abnormal changes and evaluate whether further intervention is needed;

[0043] Comprehensive score>8: indicates serious abnormality, immediately implement the following strategy: immediately cut off the power

[0044] Source, to prevent equipment damage or safety accidents; activate the safety protection mechanism of the emergency cooling device;

[0045] Automatically notify relevant personnel for on-site processing.

[0046] Furthermore, it also includes an exception handling monitoring module, which is used to continuously monitor the changes of the abnormal situation after the safety processing module handles it. If the handling measures fail to effectively solve the abnormal situation, the handling strategy is automatically adjusted, including:

[0047] Threshold adjustment: If the abnormal situation cannot be effectively resolved under the currently set threshold, the threshold of the corresponding parameters of current, voltage and temperature will be automatically adjusted;

[0048] Time interval adjustment: Automatically adjust the monitoring and processing time interval according to the severity of the abnormal situation.

[0049] Furthermore, it also includes an abnormality recovery module, which is used to control the control center to enter the abnormality recovery stage after the abnormality processing monitoring module monitors and ensures that the abnormal situation is resolved.

[0050] Furthermore, it also includes an exception recording module for recording the exception handling and recovery process and sending notifications to relevant personnel.

[0051] Furthermore, the data acquisition module uses data compression and encryption technology when collecting data, specifically:

[0052] (1) Lossless compression technology:

[0053] Huffman coding: Generates a corresponding variable-length code for each character by counting the frequency of the characters.

[0054] (2) Data encryption technology:

[0055] Symmetric encryption algorithm: uses a 128-bit block cipher with a key length of 128, 192 or 256 bits.

[0056] Furthermore, the anomaly detection module performs real-time analysis on the collected data and determines whether there is an anomaly. Specifically, a comparative analysis is performed based on the historical data of the charging device and the statistical data of the same type of equipment. If the current charging data is significantly different from the historical trend or the performance of similar equipment, an abnormal alarm is triggered. At the same time, monitoring of parameters including but not limited to charging time, charging power change rate, and battery internal resistance change is introduced.

[0057] Furthermore, the safety processing module takes corresponding safety measures according to the alarm signal, triggers and notifies the mechanism, and triggers the alarm if it exceeds the maximum threshold, is lower than the minimum threshold, or is within the alarm threshold range. At the same time, the notification personnel and notification methods are set to notify the alarm, and the notification methods are not limited to SMS, email, and WeChat; the emergency control equipment is operated including but not limited to cutting off the power supply and reducing the current, wherein the specific algorithm for setting the threshold is as follows: Assuming that a certain monitoring indicator is x(t), the standard value is x norm , then the calculation formula of deviation Δx(t) is:

[0058]

[0059] Determine the severity of the anomaly based on the size of the deviation and the corresponding threshold:

[0060] Minor abnormality: An alarm is triggered when Δx(t) is within the alarm threshold range and does not exceed the maximum threshold or the minimum threshold;

[0061] Severe anomaly: When Δx(t) exceeds the maximum threshold or is lower than the minimum threshold, it is considered a severe anomaly;

[0062] Then, each anomaly is quantified and a weighted score is calculated. Assuming there are multiple monitoring parameters x1(t), x2(t),…, x n (t), the corresponding standard values ​​are x norm1 ,x norm2 ,…,x normn , then the comprehensive abnormality score S total It can be expressed as:

[0063] S total =w1×S1+w2×S2+…+w n ×S n

[0064] Among them, S i is the abnormal score of the i-th parameter, w i is the weight of this indicator.

[0065] Advantages of the present invention:

[0066] The data acquisition module of the present invention dynamically adjusts the acquisition frequency according to the different stages of the charging process, and improves the acquisition frequency to ensure the accuracy and timeliness of the data. The anomaly detection module uses an intelligent algorithm to identify complex charging data patterns, so that it can automatically identify abnormal charging data patterns, thereby improving the accuracy and intelligence level of anomaly detection. The safety processing module adopts corresponding processing strategies based on the results of abnormal judgment, and the processing is timely and targeted. At the same time, the system continuously monitors changes in abnormal conditions to ensure the effectiveness of the processing measures. When the abnormal conditions are under control, the system will enter the abnormal recovery stage to ensure the normal continuity of the charging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 This is a schematic diagram of the overall architecture of the system according to an embodiment of the present invention;

[0069] Figure 2 The figure is a flowchart of abnormal charging data processing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Embodiment 1: The present invention proposes a charging data anomaly detection and safety processing system, including a data acquisition module, an anomaly detection module, a safety processing module and a control center, wherein the data acquisition module, the anomaly detection module and the safety processing module are all connected to the control center in communication, wherein:

[0072] The data acquisition module is used to collect various data in the charging process in real time, including but not limited to current, voltage, temperature, power, and power;

[0073] The anomaly detection module analyzes the collected data according to preset rules and algorithms to determine whether there are any abnormal situations;

[0074] The five main rules are as follows:

[0075] Current fluctuation rule: The current value should fluctuate within a certain range. If the current value exceeds the preset range of ±5%, it is judged as abnormal.

[0076] Voltage fluctuation rule: The voltage value should fluctuate within a certain range. If the voltage value exceeds the preset range of ±3%, it is judged as abnormal.

[0077] Temperature change rule: During charging, the battery temperature change rate should be within a certain range. If the temperature change rate exceeds 1°C per minute, it is considered abnormal.

[0078] Electricity change rule: During the charging process, the electricity change should follow a linear or nearly linear rule. If the rate of change of electricity suddenly increases or decreases by more than 20%, it is considered abnormal.

[0079] Power fluctuation rule: The power value should fluctuate within a certain range. If the power value exceeds the preset range of ±10%, it is judged as abnormal.

[0080] The algorithms mainly include the following three:

[0081] Sliding window algorithm: used to monitor the changes of current, voltage, temperature and other parameters in real time. By setting a sliding window of fixed length, the mean and standard deviation of the data in the window are continuously calculated. If the data at a certain moment exceeds ±3 times the standard deviation of the window mean, it is judged as abnormal.

[0082] Time series prediction algorithm: Use the ARIMA (Autoregressive Integrated Moving Average) time series model to predict the data value at the next time point. If the deviation between the actual data and the predicted value exceeds the preset threshold, it is judged as an anomaly.

[0083] Clustering algorithm: Use K-means clustering algorithm to classify historical data. If the current data point is far away from any known cluster center, it is judged as abnormal.

[0084] When the safety processing module detects an abnormal situation, it takes corresponding safety measures, including but not limited to cutting off the power supply and sounding an alarm;

[0085] The control center is responsible for coordinating the work of each module and realizing the overall function of the system.

[0086] In this embodiment, the data acquisition module includes a standard interface and a compatible interface, which collects various data during the charging process in real time and transmits them to the abnormality detection module and the control center via wired or wireless means, and dynamically adjusts the acquisition frequency according to the different stages of the charging process: in the early and late stages of charging, the acquisition frequency is reduced; in the middle stage of charging, when the battery status changes rapidly, the acquisition frequency is increased.

[0087] In this embodiment, the anomaly detection module establishes multiple anomaly detection models, including threshold-based detection, statistical analysis-based detection, preset detection rules and algorithms, sets corresponding detection thresholds and alarm rules according to different abnormal situations, performs real-time analysis on the collected data and determines whether there are anomalies, and uses multi-model fusion to fuse the results of different algorithms and use weighted fusion to improve the accuracy and robustness of detection.

[0088] The preset detection rules are as follows:

[0089] (1) Current fluctuation rules:

[0090] Erase detection threshold: The current value exceeds the preset range of ±5%.

[0091] Alarm rules: When the current value exceeds the preset range, the system will immediately sound an alarm and record the time and current value of the abnormality.

[0092] (2) Voltage fluctuation rules:

[0093] Detection threshold: Voltage value exceeds the preset range of ±3%.

[0094] Alarm rules: When the voltage value exceeds the preset range, the system will immediately sound an alarm and record the time and voltage value of the abnormality.

[0095] (3) Temperature change rules:

[0096] Detection threshold: Temperature change rate exceeds 1°C per minute.

[0097] Alarm rules: When the temperature change rate exceeds the preset range, the system will immediately issue an alarm and record the time and temperature value of the abnormality.

[0098] (4) Electricity change rules:

[0099] Detection threshold: The rate of change of charge suddenly increases or decreases by more than 20%.

[0100] Alarm rules: When the rate of change of power exceeds the preset range, the system will immediately issue an alarm and record the time and power value of the abnormality.

[0101] (5) Power fluctuation rules:

[0102] Detection threshold: Power value outside the preset range of ±10%.

[0103] Alarm rules: When the power value exceeds the preset range, the system will immediately issue an alarm and record the time and power value of the abnormality.

[0104] The preset detection algorithms are as follows:

[0105] (1) Sliding Window Algorithm:

[0106] Detection threshold: set at ±3 times the standard deviation.

[0107] Alarm rules: When the data exceeds ±3 times the standard deviation of the window mean, the system immediately issues an alarm and records the time and data value of the abnormality.

[0108] (2) Time series prediction algorithm:

[0109] Detection threshold: set at ±15% of the predicted value.

[0110] Alarm rules: When the deviation between the actual data and the predicted value exceeds ±15%, the system will immediately issue an alarm and record the time and data value of the abnormality.

[0111] (3) Clustering algorithm:

[0112] Detection threshold: set the distance between the data point and the nearest cluster center to be greater than 2 times the average distance.

[0113] Alarm rules: When a data point is far away from any known cluster center, the system immediately issues an alarm and records the time and data value of the anomaly.

[0114] In this embodiment, the control center automatically assigns the corresponding processing strategy to the security processing module according to the abnormality judgment result of the abnormality detection module. The allocation of the processing strategy is based on the evaluation of the abnormality type and severity, where:

[0115] Minor abnormalities: Only an alarm is issued to notify relevant personnel to pay attention and continue to monitor whether the abnormal situation will deteriorate further.

[0116] Severe abnormalities: situations that may cause equipment damage or safety accidents, and immediate hard control measures must be taken to control them, including cutting off power and activating emergency cooling devices.

[0117] The evaluation method for abnormality type and severity is:

[0118] (1) Quantitative evaluation of abnormal characteristics:

[0119] By monitoring various charging parameters (such as current, voltage, temperature, charging rate, etc.), the deviation value from the normal working state is calculated, and the abnormal type is quantified according to the degree of deviation. The specific quantification standards are as follows:

[0120] Abnormal current (ΔI):

[0121] Slight abnormality: |ΔI|≤5% (current fluctuation is within the normal range)

[0122] Severe abnormality: |ΔI|>20% (the current obviously exceeds the normal value)

[0123] Voltage abnormality (ΔV):

[0124] Slight abnormality: |ΔV|≤5% (voltage fluctuation is within the normal range)

[0125] Severe abnormality: |ΔV|>15% (voltage exceeds normal operating range)

[0126] Temperature anomaly (ΔT):

[0127] Slight abnormality: |ΔT|≤3℃ (slight temperature fluctuation)

[0128] Severe abnormality: |ΔT|>10℃ (temperature deviates from normal working value)

[0129] Abnormal charging rate:

[0130] Slight abnormality: Charging rate variation within ±10%

[0131] Severe abnormality: Charging rate changes by more than ±20%

[0132] (2) Comprehensive abnormality severity score:

[0133] Based on the severity of each anomaly, the control center makes a reasonable judgment and calculates a comprehensive anomaly severity score based on the quantitative scores of each anomaly. Assume that the scores of each anomaly are s 电流 、s 电压 , S 温度 , S 充电速率 , the comprehensive score can be expressed as:

[0134] Comprehensive score = w1×S 电流 +w2×S 电压 +w3×S 温度 +w4×S 充电速率

[0135] Among them, w1, w2, w3, and w4 are the weights of various abnormal indicators and need to be adjusted according to actual conditions.

[0136] Based on the value of the comprehensive score, the control center allocates the following exception handling strategies.

[0137] (3) Allocation of exception handling strategies:

[0138] Comprehensive score ≤ 5: indicates a slight abnormality, and the system implements the following strategies: trigger an alarm to notify relevant personnel to pay attention to the abnormality; continue to monitor the abnormal situation to assess whether it will further deteriorate; do not take intervention measures unless the abnormality further worsens.

[0139] 5<Comprehensive score≤8: indicates a moderate abnormality. The system implements the following strategies: trigger an alarm and automatically notify relevant personnel; implement some protective measures, such as reducing the charging rate and limiting the charging power; continue to monitor abnormal changes and evaluate whether further intervention is needed.

[0140] Comprehensive score > 8: indicates a serious abnormality, and the system immediately executes the following strategies: immediately cut off the power supply to prevent equipment damage or safety accidents; activate safety protection mechanisms such as emergency cooling devices; automatically notify relevant personnel to conduct on-site processing to ensure equipment safety.

[0141] In this embodiment, an exception handling monitoring module is also included, which is used to continuously monitor the changes of the abnormal situation after the safety processing module handles it. If the processing measures fail to effectively solve the abnormal situation, the processing strategy is automatically adjusted, which mainly includes:

[0142] Threshold adjustment: If the abnormal situation cannot be effectively solved under the currently set threshold, the system automatically adjusts the corresponding threshold, such as the threshold of current, voltage, temperature and other parameters to adapt to different abnormal situations.

[0143] Time interval adjustment: The system automatically adjusts the monitoring and processing time intervals according to the severity of the abnormal situation to respond to the abnormality more quickly.

[0144] In this embodiment, an abnormality recovery module is also included, which is used to control the control center to enter the abnormality recovery stage after the abnormality processing monitoring module monitors and ensures that the abnormal situation is resolved.

[0145] In this embodiment, an exception recording module is also included, which is used to record the exception handling and recovery process and send notifications to relevant personnel.

[0146] In this embodiment, the data collection module uses data compression and encryption technology when collecting data, specifically:

[0147] (1) Lossless compression technology:

[0148] Huffman coding: By counting the frequency of characters, a variable-length code is generated for each character to achieve data compression. This coding method can ensure the integrity and accuracy of the data.

[0149] (2) Data encryption technology:

[0150] Symmetric encryption algorithm: AES (Advanced Encryption Standard): uses a 128-bit block cipher with a key length of 128, 192 or 256 bits. The AES algorithm has a wide range of applications and high security.

[0151] In this embodiment, the anomaly detection module performs real-time analysis on the collected data and determines whether there are any anomalies. Specifically, a comparative analysis is performed based on the historical data of the charging device and the statistical data of the same type of equipment. If the current charging data is significantly different from the historical trend or the performance of similar equipment, an abnormal alarm is triggered. At the same time, monitoring of parameters including but not limited to charging time, charging power change rate, and battery internal resistance change is introduced.

[0152] In this embodiment, the safety processing module takes corresponding safety measures according to the alarm signal, triggers and notifies the mechanism. If the maximum threshold is exceeded, the minimum threshold is lower than, or within the alarm threshold range, an alarm is triggered, and the notification personnel and notification method are set to send an alarm notification. The notification method includes but is not limited to SMS, email, and WeChat; for emergency control equipment, operations including but not limited to cutting off power supply and reducing current are performed.

[0153] The anomaly detection module monitors charging parameters such as current, voltage, and temperature, and quantifies anomalies based on preset maximum thresholds, minimum thresholds, and alarm thresholds.

[0154] (1) Threshold setting

[0155] Set different thresholds to quantify the degree of anomaly and trigger corresponding processing strategies. Thresholds are divided into the following categories:

[0156] Max Threshold: The maximum value that the device can withstand. If this value is exceeded, the device is considered to have a serious abnormality and must be handled urgently.

[0157] Min Threshold: The minimum value that the device can withstand. Values ​​below this value are considered abnormal and require emergency processing.

[0158] Warning Threshold: The deviation of the equipment within the normal operating range. When the monitored value exceeds the warning threshold but does not exceed the maximum or minimum threshold, the system will issue an alarm and notify personnel.

[0159] Abnormal quantification formula:

[0160] The anomaly detection module quantifies the degree of anomaly by calculating the deviation between the actual monitoring value and the standard value. Assume that a monitoring indicator is x(t) and the standard value is x norm , then the calculation formula of deviation Δx(t) is:

[0161]

[0162] Determine the severity of the anomaly based on the size of the deviation and the corresponding threshold:

[0163] Minor abnormality (Warning): An alarm is triggered when Δx(t) is within the alarm threshold range and does not exceed the maximum threshold or the minimum threshold.

[0164] Critical: When Δx(t) exceeds the maximum threshold or is lower than the minimum threshold, it is considered a critical abnormality and triggers emergency processing immediately.

[0165] (2) Comprehensive anomaly scoring algorithm:

[0166] In order to comprehensively judge the impact of various abnormal factors on the system, the control center quantifies each abnormality and calculates a weighted score to obtain a comprehensive abnormality score. Assume that there are multiple monitoring parameters x1(t), x2(t),…, x n (t), the corresponding standard values ​​are x norm1 ,x norm2 ,…,x normn , then the comprehensive abnormality score S total It can be expressed as:

[0167] S total =w1×S1+w2×S2+…+w n ×S n

[0168] Among them, S i is the abnormal score of the i-th parameter, w i is the weight of the indicator, indicating the importance of different abnormal indicators to the overall safety. Generally, current, voltage, etc. have a greater impact on equipment safety, and their weight should be relatively high.

[0169] Embodiment 2: The present invention proposes a charging data anomaly detection and safety processing system, which mainly includes a data acquisition module, an anomaly detection module, a safety processing module and a control center. The data acquisition module is used to collect various data in the charging process in real time, such as current, voltage, temperature, power, etc. The anomaly detection module analyzes the collected data according to preset rules and algorithms to determine whether there is an abnormal situation. When the safety processing module detects an abnormal situation, it takes corresponding safety measures, such as cutting off the power supply and sounding an alarm. The control center is responsible for coordinating the work of each module to realize the overall function of the system. The specific functional principles of each module are as follows:

[0170] 1. Data acquisition module

[0171] When the system is connected to a charging pile, the charging monitoring module collects multi-dimensional data such as current, voltage, and temperature in real time.

[0172] Compatibility and expansion: When designing the data acquisition module, consider compatibility with different types of charging equipment and management systems. Through standardized interfaces and protocols, it can be easily integrated into existing charging facilities without large-scale transformation and upgrading. At the same time, expansion interfaces are reserved to allow new functional modules to be added as needed in the future. For example, air quality sensors, noise sensors, etc. can be easily connected to further enrich the monitoring data of the charging environment.

[0173] 2. Anomaly Detection Module

[0174] Establish a variety of anomaly detection models, including threshold-based detection, statistical analysis-based detection, etc.

[0175] According to different abnormal situations, set corresponding detection thresholds and alarm rules.

[0176] It can analyze the collected data in real time and quickly and accurately determine whether there are any abnormal conditions.

[0177] 3. Security Processing Module

[0178] When an abnormal situation is detected, take corresponding safety measures immediately, such as cutting off power supply, sounding an alarm, etc.

[0179] It has a fault diagnosis function, can analyze and diagnose abnormal situations, and provide reference for maintenance personnel.

[0180] It can communicate with the user's mobile device to promptly notify the user of abnormal charging conditions.

[0181] 4. Control Center

[0182] Coordinate the work of each module to realize the overall function of the system.

[0183] The collected data is stored and managed to facilitate subsequent analysis and query.

[0184] Provides a user interface through which users can view information such as charging status, abnormal conditions, and processing results.

[0185] Embodiment 3: Attachment Figure 1 This is a schematic diagram of the overall system architecture, showing the overall structure of the charging data anomaly detection and safety processing system.

[0186] Equipment layer: The equipment layer mainly includes various charging devices and hardware facilities such as sensors connected to them. Charging equipment can be charging piles, charging guns, car chargers, etc. They are the basis of the entire system. Sensors are used to monitor various operating parameters of charging equipment in real time, such as current, voltage, temperature, power, etc.

[0187] Communication layer: The communication layer is responsible for transmitting data and instructions between various modules of the system. The communication layer adopts a variety of technical means such as Ethernet and wireless communication. It also uses encryption technology and data verification mechanism to ensure security. It also has good compatibility and scalability, which facilitates data transmission to charging equipment.

[0188] Data collection: Data collection is mainly completed by sensors and related data collection equipment at the device layer. It can sense various physical quantities and state parameters in the charging process in real time, such as current, voltage, temperature, power, charging time, etc. The data collection process has the characteristics of high precision and high frequency. It can detect potential abnormal situations in a timely and accurate manner.

[0189] Anomaly detection module: The anomaly detection module analyzes the collected charging data to determine whether there are any abnormal conditions, and obtains abnormal conditions through thresholds, historical record analysis, algorithms, etc.

[0190] Abnormality handling module: When the abnormality detection module finds abnormal charging data, it enters the abnormality handling module. It classifies the abnormality according to its type and severity, and notifies or automatically recovers according to the established abnormality handling strategy.

[0191] Control center: Responsible for receiving data and information from various modules, analyzing and processing the information. It maintains close communication connections with the data acquisition module, anomaly detection module, safety processing, and charging equipment at the device layer.

[0192] Attached Figure 2 The charging data exception handling process is demonstrated.

[0193] Abnormal signal trigger: When the charging data is abnormal, the abnormal detection module in the system will immediately capture these abnormal signals. These abnormal signals may come from the current, voltage, temperature and other parameters collected by the data acquisition module exceeding the normal range, or the abnormal extension of charging time, unstable charging power and other situations.

[0194] Abnormal judgment: Once an abnormal signal is triggered, the system will quickly enter the abnormal judgment stage. In this stage, the system will use pre-set judgment rules and algorithms to analyze the abnormal signal and judge the severity and type of the abnormality.

[0195] Assign processing strategies and conduct processing: Based on the results of the abnormality judgment, the system will automatically assign the corresponding processing strategy. The allocation of processing strategies is based on a comprehensive consideration of the type and severity of the abnormality. For minor abnormalities, it is only necessary to issue an alarm, notify relevant personnel to pay attention, and continuously monitor whether the abnormal situation will deteriorate further. For serious abnormalities, such as those that may cause equipment damage or safety accidents, the system will immediately take measures to control them, such as cutting off power supply, starting emergency cooling devices, etc. The system sets up an abnormality processing monitoring module. During the processing process, the system will continuously monitor changes in abnormal conditions to ensure the effectiveness of the processing measures. If the processing measures fail to effectively resolve the abnormal situation, the system may automatically adjust the processing strategy or request manual intervention for processing.

[0196] The system also has an abnormal recovery module. When the treatment measures successfully control the abnormal situation, the control center controls the abnormal recovery stage. In this stage, the system will conduct a comprehensive inspection of the charging equipment. At the same time, the system also has an abnormal recording module to record the abnormal handling and recovery process and send a notification to relevant personnel.

[0197] The specific application of the present invention is as follows:

[0198] 1. System installation and configuration

[0199] Match the acquisition equipment to the system, configure the detection rules and thresholds of the anomaly detection module, and the security measures of the security processing module. Install the control center software, and connect and debug with each module.

[0200] 2. Data Collection and Transmission

[0201] The data acquisition module collects various data during the charging process in real time and transmits them to the abnormality detection module and the control center via wired or wireless means.

[0202] Real-time, high-resolution monitoring of charging data, dynamically adjusting the collection frequency according to the different stages of the charging process. In the early and late stages of charging, the collection frequency is reduced to save resources; in the middle of charging, when the battery status changes rapidly, the collection frequency is increased to ensure the accuracy and timeliness of the data.

[0203] Enhance the remote communication function of the data acquisition module so that it can transmit the collected data to the anomaly detection module and control center in real time through wired networks and wireless networks (such as 4G and 5G).

[0204] Data compression and encryption technologies are used to ensure the security and reliability of remotely transmitted data, while reducing bandwidth usage and latency of data transmission.

[0205] The control center stores and manages the collected data for subsequent analysis and query.

[0206] 3. Anomaly detection and handling

[0207] The anomaly detection module performs real-time analysis on the collected data and immediately sends an alarm signal to the security processing module when an abnormal situation is detected.

[0208] Combine the historical data of the charging equipment with the statistical data of the same type of equipment for comparative analysis. If the current charging data is significantly different from the historical trend or the performance of similar equipment, an abnormal alarm will be triggered. In addition to detecting conventional parameters such as current, voltage, and temperature, the monitoring of parameters such as charging time, charging power change rate, and battery internal resistance change is introduced. For example, when the charging time is significantly beyond the normal range or the charging power change rate fluctuates abnormally, it is judged that there may be problems such as charging equipment failure or battery aging. Use intelligent algorithms to identify complex charging data patterns. It enables it to automatically identify abnormal charging data patterns and improve the accuracy and intelligence level of abnormal detection.

[0209] The safety processing module takes corresponding safety measures according to the alarm signal, triggers and notifies the mechanism. If the maximum threshold is exceeded, the minimum threshold is lower than, or within the alarm threshold range, an alarm can be triggered. At the same time, the notification personnel and notification methods (SMS, email, WeChat, etc.) can be set for alarm notification. In case of emergency, the equipment can be controlled, such as cutting off the power supply and reducing the current.

[0210] 4. System maintenance and upgrade

[0211] Regularly maintain and inspect the system to ensure the normal operation of each module. According to actual needs, upgrade and improve the system to improve the performance and function of the system.

[0212] In summary, the data acquisition module of the present invention dynamically adjusts the acquisition frequency according to the different stages of the charging process, and improves the acquisition frequency to ensure the accuracy and timeliness of the data. The anomaly detection module uses an intelligent algorithm to identify complex charging data patterns, so that it can automatically identify abnormal charging data patterns, thereby improving the accuracy and intelligence level of anomaly detection. The safety processing module adopts corresponding processing strategies based on the results of the abnormal judgment, and the processing is timely and targeted. At the same time, the system continuously monitors changes in abnormal conditions to ensure the effectiveness of the processing measures. When the abnormal conditions are under control, the system will enter the abnormal recovery stage to ensure the normal continuity of the charging process.

[0213] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging data anomaly detection and safety processing system, characterized in that: It includes a data acquisition module, an anomaly detection module, a safety processing module and a control center, wherein the data acquisition module, the anomaly detection module and the safety processing module are all connected to the control center in communication, wherein: The data acquisition module is used to collect various data in the charging process in real time, including but not limited to current, voltage, temperature, power, and power; The anomaly detection module analyzes the collected data according to preset rules and algorithms to determine whether there are any abnormal conditions; When the safety processing module detects an abnormal situation, it takes corresponding safety measures, including but not limited to cutting off the power supply and sounding an alarm; The control center is responsible for coordinating the work of each module and realizing the overall function of the system.

2. A charging data anomaly detection and safety processing system according to claim 1, characterized in that: The data acquisition module includes a standard interface and a compatible interface, collects various data during the charging process in real time, and transmits them to the abnormality detection module and the control center via wired or wireless means, and dynamically adjusts the acquisition frequency according to different stages of the charging process: at the beginning and end of charging, the acquisition frequency is reduced; in the middle of charging, when the battery state changes rapidly, the acquisition frequency is increased.

3. A charging data anomaly detection and safety processing system according to claim 1, characterized in that: The anomaly detection module analyzes the collected data according to preset rules and algorithms to determine whether there are any abnormal conditions, wherein the rules include: (1) Current fluctuation rules: The current value should fluctuate within a certain range. If the current value exceeds (2) If it is out of the preset range of ±5%, it is considered abnormal; (3) Voltage fluctuation rule: The voltage value should fluctuate within a certain range. If the voltage value exceeds the preset range of ±3%, it is judged as abnormal; (4) Temperature change rule: During the charging process, the battery temperature change rate should be within a certain range. If the temperature change rate exceeds 1°C per minute, it is considered abnormal. (5) Electricity change rule: During the charging process, the electricity change should follow a linear or nearly linear rule. If the rate of change of electricity suddenly increases or decreases by more than 20%, it is considered abnormal. (6) Power fluctuation rule: The power value should fluctuate within a certain range. If the power value exceeds the preset range of ±10%, it is judged as abnormal; The algorithms mainly include the following three: (1) Sliding window algorithm: used to monitor the changes of parameters including current, voltage, and temperature in real time. By setting a sliding window of fixed length, the mean and standard deviation of the data in the window are continuously calculated. If the data at a certain moment exceeds ±3 times the standard deviation of the window mean, it is judged as abnormal. (2) Time series prediction algorithm: Use the autoregressive integrated moving average model time series model to predict the data value at the next time point. If the deviation between the actual data and the predicted value exceeds the preset threshold, it is judged as abnormal; (3) Clustering algorithm: Use the K-means clustering algorithm to classify historical data. If the current data point is far away from any known cluster center, it is judged as an anomaly.

4. A charging data anomaly detection and safety processing system according to claim 1, characterized in that: The control center automatically assigns the corresponding processing strategy to the security processing module according to the abnormality judgment result of the abnormality detection module. The allocation of the processing strategy is based on a comprehensive evaluation of the abnormality type and severity, specifically: (1) Quantitative evaluation of abnormal characteristics: By monitoring various charging parameters including but not limited to current, voltage, temperature, and charging rate, the deviation value from the normal working state is calculated, and the abnormal type is quantified according to the degree of deviation. The specific quantification standards are as follows: Abnormal current (ΔI): Slight abnormality: |ΔI|≤5%, indicating that the current fluctuation is within the normal range; Severe abnormality: |ΔI|>20%, indicating that the current is significantly beyond the normal value; Voltage abnormality (ΔV): Slight abnormality: |ΔV|≤5%, indicating that the voltage fluctuation is within the normal range; Severe abnormality: |ΔV|>15%, indicating that the voltage is beyond the normal working range; Temperature anomaly (ΔT): Slight abnormality: |ΔT|≤3℃, indicating slight temperature fluctuation; Severe abnormality: |ΔT|>10℃, indicating that the temperature deviates from the normal working value; Abnormal charging rate: Slight abnormality: charging rate variation is within ±10%; Severe abnormality: The charging rate changes by more than ±20%; (2) Comprehensive abnormality severity score: A comprehensive abnormality severity score is calculated based on the quantitative scores of each abnormality. Assume that the scores of each abnormality are S 电流 , S 电压 , S 温度 , S 充电速率 , the comprehensive score can be expressed as: Comprehensive score = w1×S 电流 +w2×S 电压 +w3×S 温度 +w4×S 充电速率 Among them, w1, w2, w3, and w4 are the weights of various abnormal indicators, which are adjusted according to the actual situation; (3) Allocation of exception handling strategies: Comprehensive score ≤ 5: indicates a slight abnormality, and the following strategies are implemented: trigger an alarm to notify relevant personnel to pay attention to the abnormality; continue to monitor the abnormality and assess whether it will deteriorate further; do not take intervention measures unless the abnormality further worsens; 5<Comprehensive score≤8: indicates moderate abnormality, and the following strategies are implemented: trigger an alarm and automatically notify relevant personnel; implement some protective measures, such as reducing the charging rate and limiting the charging power; continue to monitor abnormal changes and evaluate whether further intervention is needed; Comprehensive score > 8: indicates a serious abnormality, and the following strategies should be implemented immediately: immediately cut off the power supply to prevent equipment damage or safety accidents; activate the safety protection mechanism of the emergency cooling device; automatically notify relevant personnel for on-site processing.

5. A charging data anomaly detection and safety processing system according to claim 1, characterized in that: It also includes an exception handling monitoring module, which is used to continuously monitor the changes in the abnormal situation after the safety processing module handles it. If the handling measures fail to effectively solve the abnormal situation, the handling strategy is automatically adjusted, including: Threshold adjustment: If the abnormal situation cannot be effectively resolved under the currently set threshold, the threshold of the corresponding parameters of current, voltage and temperature will be automatically adjusted; Time interval adjustment: Automatically adjust the monitoring and processing time interval according to the severity of the abnormal situation.

6. A charging data anomaly detection and safety processing system according to claim 5, characterized in that: It also includes an abnormality recovery module, which is used to control the control center to enter the abnormality recovery stage after the abnormality processing monitoring module monitors and ensures that the abnormal situation is resolved.

7. A charging data anomaly detection and safety processing system according to claim 6, characterized in that: It also includes an exception recording module, which is used to record the exception handling and recovery process and send notifications to relevant personnel.

8. A charging data anomaly detection and safety processing system according to claim 2, characterized in that: The data acquisition module uses data compression and encryption technology when collecting data, specifically: (1) Lossless compression technology: Huffman coding: Generates a corresponding variable-length code for each character by counting the frequency of the characters. (2) Data encryption technology: Symmetric encryption algorithm: uses a 128-bit block cipher with a key length of 128, 192 or 256 bits.

9. A charging data anomaly detection and safety processing system according to claim 3, characterized in that: The anomaly detection module performs real-time analysis on the collected data and determines whether there is an anomaly. Specifically, it performs comparative analysis based on the historical data of the charging device and the statistical data of the same type of equipment. If the current charging data is significantly different from the historical trend or the performance of similar equipment, an abnormal alarm is triggered. At the same time, it introduces monitoring of parameters including but not limited to charging time, charging power change rate, and battery internal resistance change.

10. A charging data anomaly detection and safety processing system according to claim 1, characterized in that: The safety processing module takes corresponding safety measures according to the alarm signal, triggers and notifies the mechanism. If the maximum threshold is exceeded, the minimum threshold is lower than, or within the alarm threshold range, an alarm is triggered. At the same time, the notification personnel and notification methods are set for alarm notification. The notification methods include but are not limited to SMS, email, and WeChat. For emergency control equipment, operations including but not limited to cutting off the power supply and reducing the current are performed. The specific algorithm for setting the threshold is as follows: Assuming that a certain monitoring indicator is x(t), the standard value is x norm , then the calculation formula of deviation Δx(t) is: Determine the severity of the anomaly based on the size of the deviation and the corresponding threshold: Minor abnormality: An alarm is triggered when Δx(t) is within the alarm threshold range and does not exceed the maximum threshold or the minimum threshold; Severe anomaly: When Δx(t) exceeds the maximum threshold or is lower than the minimum threshold, it is considered a severe anomaly; Then, each anomaly is quantified and a weighted score is calculated. Assuming there are multiple monitoring parameters x1(t), x2(t),…, x n (t), the corresponding standard values ​​are x norm1 ,x norm2 ,…,x normn , then the comprehensive abnormality score S total It can be expressed as: S total =w1×S1+w2×S2+…+w n ×S n Among them, S i is the abnormal score of the i-th parameter, w i is the weight of this indicator.

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