Fault detection method and system for battery charging cabinet of electric bicycle

Abstract

The invention provides a fault detection method and system for a battery charging cabinet of an electric bicycle, and relates to the technical field of charging fault detection, and the method comprises the steps: dividing a day into four time intervals according to high and low peak power consumption, and setting different weight coefficients; acquiring load power, voltage, current and temperature time sequence data, and calibrating the load power by using the voltage, the current and the temperature; calculating the average power, the maximum power and the minimum power of each time interval; acquiring load power, voltage, current and temperature data of the to-be-detected charging cabinet; calibrating load power and calculating average power, maximum power and minimum power of the load power; and combining the weight coefficient, the average power, the maximum power and the minimum power to generate an abnormal fault index, comparing the index with a preset threshold value, and judging whether the charging cabinet has a fault or not. According to the method, the abnormal fault index is generated by collecting and calibrating the time sequence data of each parameter and combining the time interval and the weight coefficient so as to judge whether the electric bicycle battery charging cabinet breaks down or not.

Description

Technical Field

[0001] The invention relates to the technical field of charging fault detection, and in particular to a fault detection method and system for an electric bicycle battery charging cabinet. Background Art

[0002] Electric bicycles have become a very popular means of transportation worldwide. With the acceleration of urbanization and the enhancement of environmental awareness, more and more people choose electric bicycles as the preferred means of daily commuting and short-distance travel. Electric bicycle battery charging cabinets provide a convenient charging solution, which can effectively solve the problem of inconvenience in charging at home or in the workplace. However, if there is a fault in the charging cabinet, it will cause battery overcharging, unstable voltage or incorrect charging mode, which will damage the battery of the electric bicycle. Battery damage will not only shorten the battery life, but also cause battery performance to decline, affect the endurance of the electric bicycle, and increase the user's replacement cost. The most serious consequence is safety accidents such as fire or explosion. If the control system fails, the temperature is too high or a short circuit occurs during the battery charging process, it may cause thermal runaway of the battery, causing a fire or explosion. This situation not only endangers the charging cabinet equipment itself, but also poses a serious threat to the surrounding environment and personal safety.

[0003] In the prior art, publication number CN114545268A discloses a battery charging cabinet fire detection method, system, and battery charging cabinet, which obtains the external temperature of the shell of the charged battery, obtains the slope of the curve of the external temperature change of the shell according to the external temperature of the shell, and judges whether the slope of the curve matches the slope of the temperature curve when the battery is thermal runaway; if so, executes the setting operation in response to fire extinguishing, uses the external temperature of the shell as the data source for judgment, and uses the slope of the curve to judge the thermal runaway, and uses the slope value as the trigger condition to achieve accurate judgment of the battery thermal runaway.

[0004] Insufficient existing technology: In the prior art, judgment is mainly based on the slope of the temperature change curve on the outside of the battery casing, without comprehensive consideration of multi-dimensional data such as load power, voltage, and current. This may lead to inaccurate judgment. Changes in external ambient temperature or other factors may also affect the external temperature of the casing, making it impossible to fully monitor the status of the charging cabinet, resulting in false alarms or missed alarms.

[0005] The existing technology does not take into account the power data of the charging cabinet and its weight coefficient in different time intervals, which may lead to ignoring the impact of load changes in risk assessment in different time periods, lack of calibration and comparison of historical data, and reduce the comprehensiveness and accuracy of fault detection.

[0006] Therefore, it is necessary to provide a fault detection method and system for an electric bicycle battery charging cabinet to solve the above problem.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0008] The purpose of the present invention is to provide a fault detection method and system for an electric bicycle battery charging cabinet to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions: A fault detection method for an electric bicycle battery charging cabinet, the specific steps comprising: Step 1: Based on peak and off-peak electricity consumption, divide a day into four time intervals and set different weight coefficients; Step 2: Collect the load power time series data of the charging cabinet, the voltage time series data of the battery, the current time series data, and the temperature time series data in the cabinet in the past seven days on a daily basis, and calibrate the load power according to the voltage, current, and temperature; Step 3: Divide the calibrated load power time series data into corresponding time intervals, and calculate the average power, maximum power and minimum power of each time interval; Step 4: Collect the load power time series data of the charging cabinet to be tested, the voltage time series data of the battery, the current time series data, and the temperature time series data in the cabinet in real time. Based on the latest collection time, filter out the time series data in the same time interval as the latest collection time, and calibrate using the same method as step 2; Step 5: Based on the load power time series data calibrated in step 4, calculate the average power, maximum power and minimum power of the charging cabinet to be monitored, and combine the weight coefficient, the average power, maximum power and minimum power in step 3 to generate an abnormal fault index; Step 6: Compare the abnormal fault index with a preset threshold value, and determine whether the electric bicycle battery charging cabinet has a fault based on the comparison result.

[0010] Furthermore, a day is divided into four time intervals and different weight coefficients are set, based on the following method: The first time interval is set from 00:00-06:00, and the weight coefficient is set ; The second time interval is set from 06:00-12:00, and the weight coefficient is set ; The third time interval is set from 12:00-18:00, and the weight coefficient is set ; The fourth time interval is set from 18:00-24:00, and the weight coefficient is set ; Mark the time interval range as , , Respectively time interval lower and upper limits, and , and the first The weight coefficient corresponding to the time interval is calibrated as ,and .

[0011] Furthermore, the specific logic of calibrating the load power according to voltage, current and temperature is: ; in, Indicates that the battery is Voltage time series data for the day, Indicates The time series data of the cabinet temperature for the day, , Indicates days of calibration coefficient time series data, is a positive integer, and A variable representing the time of day; ; in, Indicates the calibrated Daily load power time series data, Indicates Daily load power time series data, Represents a time variable.

[0012] Furthermore, the specific logic for calculating the average power, maximum power and minimum power is: ; ; ; in, After calibration, Load power time series data in the jth time interval of the day, , , Respectively The average power, maximum power, and minimum power in a time interval.

[0013] Furthermore, the specific logic for generating the abnormal fault index is: ; Among them, the time interval of the latest collection time is marked as time intervals, and , , , Indicates Average power, maximum power, minimum power within a time interval, , , Indicates that the charging cabinet to be monitored is Average power, maximum power, minimum power within a time interval, , , represents the corresponding weight coefficient, which is used to adjust the influence of each item in abnormal failure, and .

[0014] Furthermore, the abnormal fault index is compared with a preset threshold value, and whether the electric bicycle battery charging cabinet has a fault is determined based on the comparison result, according to the method based on: Setting an upper fault threshold , Fault threshold lower limit ,and , the synthetically generated abnormal fault index and the fault threshold , For comparison, if , it indicates that the charging cabinet is operating normally; if or , it indicates that the charging cabinet has potential hardware failure or electrical system aging failure, which requires detection and appropriate maintenance measures.

[0015] The present invention further provides a fault detection system for an electric bicycle battery charging cabinet, the detection system being used to execute any one of the above-mentioned fault detection methods for an electric bicycle battery charging cabinet, comprising: A time interval division module, which is used to divide a day into four time intervals based on peak and low peak electricity consumption and set different weight coefficients; The data acquisition and calibration module is used to collect the load power time series data of the charging cabinet, the voltage time series data of the battery, the current time series data and the temperature time series data in the cabinet in the past seven days, and calibrate the load power according to the voltage, current and temperature: A power analysis module, which is used to divide the calibrated load power time series data into corresponding time intervals and calculate the average power, maximum power and minimum power of each time interval; A real-time monitoring module, which is used to collect the load power time series data of the charging cabinet to be tested, the voltage time series data of the battery, the current time series data, and the temperature time series data in the cabinet in real time, and based on the latest collection time, filter out the time series data in the same time interval as the latest collection time, and calibrate using the same method as step 2; An abnormality assessment module, which is used to calculate the average power, maximum power and minimum power of the charging cabinet to be monitored based on the load power time series data calibrated in step 4, and generate an abnormal fault index by combining the weight coefficient, the average power, maximum power and minimum power in step 3; A fault alarm module is used to compare the abnormal fault index with a preset threshold value, and determine whether the electric bicycle battery charging cabinet has a fault based on the comparison result.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses multi-dimensional data acquisition and calibration processing of the data acquisition and calibration module to accurately obtain key operating parameters of the charging cabinet, and adjust the data by generating calibration coefficients, thereby eliminating data deviations caused by environmental changes or measurement errors. In addition, through time interval division and weight coefficient setting, the system can adapt to changes in the working state of the charging cabinet in different time periods, ensuring the comprehensiveness and pertinence of monitoring, so that the charging cabinet can dynamically adapt to different working states, and improve the accuracy and real-time performance of fault detection. The present invention uses multi-parameter fusion to enable the system to comprehensively consider multiple factors when making fault judgments, thereby reducing the false alarm rate caused by fluctuations in a single parameter, improving the reliability of detection, and comparing the abnormal fault index with a preset threshold to promptly determine whether the operating status of the charging cabinet is abnormal. When an abnormal situation is detected, the system can quickly issue an alarm message to remind relevant personnel to conduct inspections and maintenance to avoid possible safety hazards. The present invention effectively improves the efficiency and reliability of charging cabinet fault detection through precise monitoring, real-time calibration and comprehensive analysis, and ensures the safe operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0018] Figure 2 It is a schematic diagram of the system module flow of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Example: See also Figure 1 , a fault detection method for an electric bicycle battery charging cabinet, the specific steps comprising: Step 1: Based on peak and off-peak electricity consumption, divide a day into four time intervals and set different weight coefficients; Step 2: Collect the load power time series data of the charging cabinet, the voltage time series data of the battery, the current time series data, and the temperature time series data in the cabinet in the past seven days on a daily basis, and calibrate the load power according to the voltage, current, and temperature; Step 3: Divide the calibrated load power time series data into corresponding time intervals, and calculate the average power, maximum power and minimum power of each time interval; Step 4: Collect the load power time series data of the charging cabinet to be tested, the voltage time series data of the battery, the current time series data, and the temperature time series data in the cabinet in real time. Based on the latest collection time, filter out the time series data in the same time interval as the latest collection time, and calibrate using the same method as step 2; Step 5: Based on the load power time series data calibrated in step 4, calculate the average power, maximum power and minimum power of the charging cabinet to be monitored, and combine the weight coefficient, the average power, maximum power and minimum power in step 3 to generate an abnormal fault index; Step 6: Compare the abnormal fault index with a preset threshold value, and determine whether the electric bicycle battery charging cabinet has a fault based on the comparison result.

[0022] It should be noted that the load and power demand of the power grid fluctuate greatly in different time periods of the day. The power demand is different during peak and off-peak hours, and the electricity price and load characteristics will also change. Dividing a day into multiple time intervals and setting weight coefficients for different intervals can more accurately reflect the load characteristics and energy costs of the power system. By dividing the time intervals and setting weights, the load data of different time periods can be distinguished and analyzed in detail, so as to better understand the energy consumption pattern of the charging cabinet, optimize the charging strategy, reduce operating costs, and improve the overall efficiency and reliability of the system.

[0023] Therefore, it is necessary to divide a day into four time intervals and set different weight coefficients, based on the following method: The first time interval is set from 00:00-06:00, and the weight coefficient is set ; The second time interval is set from 06:00-12:00, and the weight coefficient is set ; The third time interval is set from 12:00-18:00, and the weight coefficient is set ; The fourth time interval is set from 18:00-24:00, and the weight coefficient is set ; Mark the time interval range as , , Respectively time interval lower and upper limits, and , and the first The weight coefficient corresponding to the time interval is calibrated as ,and ; Among them, at night 00:00-06:00, the corresponding weight is , this time period is usually the off-peak period for charging of electric bicycle batteries, so a lower weight coefficient is set. This is because the usage frequency and load of the charging cabinet are relatively low at this time, and the probability and impact of failure are relatively small; in the morning to noon 06:00-12:00, the corresponding weight is , charging activities gradually increase from morning to noon, and the frequency of use of battery charging cabinets increases, so the weight coefficient is slightly higher. Considering the increase in charging demand, more faults may occur; in the afternoon to evening 12:00-18:00, the corresponding weight is , afternoon to evening is the peak period for the use of electric bicycles. At this time, the load of the charging cabinet is relatively large, so a higher weight coefficient is set to detect faults more sensitively under high load conditions; in the evening from 18:00 to 24:00, the corresponding weight is ,Evening is the main time period for electric bicycles to charge, and the load of the charging cabinet is usually the highest, so the highest weight coefficient is set to ensure high sensitivity and accuracy to faults.

[0024] It should be noted that by continuously collecting load power, voltage, current and temperature data for a week, the operating characteristics of the equipment under different conditions can be obtained. These data help identify the normal operating mode and potential abnormal mode of the load. The purpose of calibration is to eliminate data deviations caused by environmental changes or sensor errors to ensure the accuracy of analysis. The accuracy and integrity of data are crucial for fault detection. Through calibration, the influence of external factors on power data is eliminated, the accuracy of analysis is improved, and reliable basic data is provided for subsequent abnormal detection and fault prediction.

[0025] Therefore, the specific logic for calibrating the load power based on voltage, current and temperature is: ; in, Indicates that the battery is Voltage time series data for the day, Indicates The time series data of the cabinet temperature for the day, , Indicates days of calibration coefficient time series data, is a positive integer, and Represents the time variable within a day; in the above formula, the temperature is expressed in piecewise function form. It is divided into three different intervals, each with a different calibration method: When the temperature is close to 25℃, the influence of calibration is smaller. When the temperature is low, the performance of load power may be poor. Adjustment to correct power data can smooth power fluctuations under low temperature conditions and ensure data stability; when , that is, at normal temperature, the power does not require particularly strong correction, the voltage and current By adding temperature After that, the calibration coefficient is directly processed in a logarithmic way. Can better reflect the impact of temperature changes on power; when In a high temperature environment, the battery may overheat and affect the power performance. In order to correct the adverse effect of high temperature on power, the Adjustments were made to emphasize the need for calibration at high temperatures, and in this way the negative effects of high temperatures on power data can be smoothed out.

[0026] ; in, Indicates the calibrated Daily load power time series data, Indicates Daily load power time series data, Represents a time variable.

[0027] It should be noted that the load power of the charging cabinet exhibits different characteristics in different time intervals. By calculating the average power, maximum power and minimum power in each interval, a benchmark can be established for each time period. These benchmark values ​​will be used as reference standards for judging abnormalities in subsequent detections. Establishing a reference benchmark for load power helps to identify abnormal patterns. For example, if the power in a specific time period exceeds the expected range, it may mean a potential risk of failure. Interval division and power statistical analysis provide key indicators for anomaly detection.

[0028] Therefore, the specific logic for calculating the average power, maximum power and minimum power is as follows: ; ; ; in, After calibration, Load power time series data in the jth time interval of the day, , , Respectively The average power, maximum power, and minimum power in a time interval; in the above formula, calculate It is through the Power data for each day in the time interval Perform integral calculation to obtain the overall power level during this time period; calculate the The maximum power in the time interval The minimum power is to capture the The peak and minimum load of power in a time interval helps to identify whether there is an overload or underutilization of power.

[0029] It should be noted that real-time data collection can provide the latest equipment operating status and compare it with previously collected data. Divide the data into corresponding time intervals to ensure that comparative analysis is performed under the same time conditions to obtain more timely and targeted test results. Real-time data collection and calibration are the core steps to ensure that the detection system can detect problems in a timely manner. By comparing with historical data, current abnormal situations can be effectively identified and corresponding measures can be taken to avoid potential fault expansion.

[0030] Therefore, the specific logic for generating the abnormal fault index is as follows: ; Among them, the time interval of the latest collection time is marked as time intervals, and , , , Indicates Average power, maximum power, minimum power within a time interval, , , Indicates that the charging cabinet to be monitored is Average power, maximum power, minimum power within a time interval, , , represents the corresponding weight coefficient, which is used to adjust the influence of each item in abnormal failure, and ; In the above formula, if A small value indicates that the system's current power performance is close to normal operation and there is no obvious abnormality. If the value is large, it means that the system has a large deviation in average power, maximum power or minimum power, and there may be a fault, which requires further investigation and processing; since average power is the typical power performance of the system in a certain period of time, it can represent the overall energy consumption level. If the average power deviates significantly, it often means that the system as a whole is unstable or abnormal. Therefore, the deviation of average power is crucial for fault detection, and the weight is is set larger; the maximum power and minimum power are often reflected in local or instantaneous performance, which means that there is an indirect fault, and its impact is slightly smaller, so the weight and Set equal to and less than .

[0031] It should be noted that by calculating the key power indicators of the current time series data and comparing them with the historical benchmarks, combined with the weight coefficient, the current operating status of the charging cabinet can be quantified. The abnormal fault index is a key indicator that comprehensively reflects whether the equipment is abnormal. The abnormal fault index provides a quantitative basis for fault detection and can be used in automated monitoring and early warning systems. When the index exceeds the normal range, it indicates that there may be a problem with the equipment, which requires attention and further inspection.

[0032] Therefore, it is necessary to compare the abnormal fault index with the preset threshold value, and judge whether the electric bicycle battery charging cabinet has a fault according to the comparison result, and the method is based on: Setting an upper fault threshold , Fault threshold lower limit , the synthetically generated abnormal fault index and the fault threshold , For comparison, if , it indicates that the charging cabinet is operating normally; if or , it indicates that the charging cabinet has potential hardware failure or electrical system aging failure, which requires detection and appropriate maintenance measures.

[0033] See also Figure 2 The present invention further provides a fault detection system for an electric bicycle battery charging cabinet, the detection system is used to execute any one of the above-mentioned fault detection methods for an electric bicycle battery charging cabinet, comprising: A time interval division module, which is used to divide a day into four time intervals based on peak and low peak electricity consumption and set different weight coefficients; The data acquisition and calibration module is used to collect the load power time series data of the charging cabinet, the voltage time series data of the battery, the current time series data and the temperature time series data in the cabinet in the past seven days, and calibrate the load power according to the voltage, current and temperature: A power analysis module, which is used to divide the calibrated load power time series data into corresponding time intervals and calculate the average power, maximum power and minimum power of each time interval; A real-time monitoring module, which is used to collect the load power time series data of the charging cabinet to be tested, the voltage time series data of the battery, the current time series data, and the temperature time series data in the cabinet in real time, and based on the latest collection time, filter out the time series data in the same time interval as the latest collection time, and calibrate using the same method as step 2; An abnormality assessment module, which is used to calculate the average power, maximum power and minimum power of the charging cabinet to be monitored based on the load power time series data calibrated in step 4, and generate an abnormal fault index by combining the weight coefficient, the average power, maximum power and minimum power in step 3; A fault alarm module is used to compare the abnormal fault index with a preset threshold value, and determine whether the electric bicycle battery charging cabinet has a fault according to the comparison result. The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0034] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0035] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0036] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A fault detection method for an electric bicycle battery charging cabinet, characterized in that: The specific steps include: Step 1: Based on peak and off-peak electricity consumption, divide a day into four time intervals and set different weight coefficients; Step 2: Collect the load power time series data of the charging cabinet, the voltage time series data of the battery, the current time series data, and the temperature time series data in the cabinet in the past seven days on a daily basis, and calibrate the load power according to the voltage, current, and temperature; Step 3: Divide the calibrated load power time series data into corresponding time intervals, and calculate the average power, maximum power and minimum power of each time interval; Step 4: Collect the load power time series data of the charging cabinet to be tested, the voltage time series data of the battery, the current time series data, and the temperature time series data in the cabinet in real time. Based on the latest collection time, filter out the time series data in the same time interval as the latest collection time, and calibrate using the same method as step 2; Step 5: Based on the load power time series data calibrated in step 4, calculate the average power, maximum power and minimum power of the charging cabinet to be monitored, and combine the weight coefficient, the average power, maximum power and minimum power in step 3 to generate an abnormal fault index; Step 6: Compare the abnormal fault index with a preset threshold value, and determine whether the electric bicycle battery charging cabinet has a fault based on the comparison result.

2. A fault detection method for an electric bicycle battery charging cabinet according to claim 1, characterized in that: The method of dividing a day into four time intervals and setting different weight coefficients is based on: The first time interval is set from 00:00-06:00, and the weight coefficient is set ; The second time interval is set from 06:00-12:00, and the weight coefficient is set ; The third time interval is set from 12:00-18:00, and the weight coefficient is set ; The fourth time interval is set from 18:00-24:00, and the weight coefficient is set ; Mark the time interval range as , , Respectively time interval lower and upper limits, and , and the first The weight coefficient corresponding to the time interval is calibrated as ,and .

3. A fault detection method for an electric bicycle battery charging cabinet according to claim 2, characterized in that: The specific logic for calibrating load power based on voltage, current and temperature is: ； in, Indicates that the battery is Voltage time series data for the day, Indicates The time series data of the cabinet temperature for the day, , Indicates days of calibration coefficient time series data, is a positive integer, and A variable representing the time of day; ； in, Indicates the calibrated Daily load power time series data, Indicates Daily load power time series data, Represents a time variable.

4. A fault detection method for an electric bicycle battery charging cabinet according to claim 3, characterized in that: The specific logic for calculating the average power, maximum power and minimum power is: ； ； ； in, After calibration, Load power time series data in the jth time interval of the day, , , Respectively The average power, maximum power, and minimum power in a time interval.

5. A fault detection method for an electric bicycle battery charging cabinet according to claim 4, characterized in that: The specific logic for generating the abnormal fault index is: ； Among them, the time interval of the latest collection time is marked as time intervals, and , , , Indicates Average power, maximum power, minimum power within a time interval, , , Indicates that the charging cabinet to be monitored is Average power, maximum power, minimum power within a time interval, , , represents the corresponding weight coefficient, which is used to adjust the influence of each item in abnormal failure, and .

6. The fault detection method for an electric bicycle battery charging cabinet according to claim 1, characterized in that: The abnormal fault index is compared with a preset threshold value, and whether the electric bicycle battery charging cabinet has a fault is determined based on the comparison result. The specific method is as follows: Setting an upper fault threshold , Fault threshold lower limit , the synthetically generated abnormal fault index and the fault threshold , For comparison, if , it indicates that the charging cabinet is operating normally; if or , it indicates that the charging cabinet has potential hardware failure or electrical system aging failure, which requires detection and appropriate maintenance measures.

7. A fault detection system for an electric bicycle battery charging cabinet, the detection system being used to execute the fault detection method for an electric bicycle battery charging cabinet according to any one of claims 1 to 6, comprising: A time interval division module, which is used to divide a day into four time intervals based on peak and low peak electricity consumption and set different weight coefficients; The data acquisition and calibration module is used to collect the load power time series data of the charging cabinet, the voltage time series data of the battery, the current time series data and the temperature time series data in the cabinet in the past seven days, and calibrate the load power according to the voltage, current and temperature: A power analysis module, which is used to divide the calibrated load power time series data into corresponding time intervals and calculate the average power, maximum power and minimum power of each time interval; A real-time monitoring module, which is used to collect the load power time series data of the charging cabinet to be tested, the voltage time series data of the battery, the current time series data, and the temperature time series data in the cabinet in real time, and based on the latest collection time, filter out the time series data in the same time interval as the latest collection time, and calibrate using the same method as step 2; An abnormality assessment module, which is used to calculate the average power, maximum power and minimum power of the charging cabinet to be monitored based on the load power time series data calibrated in step 4, and generate an abnormal fault index by combining the weight coefficient, the average power, maximum power and minimum power in step 3; A fault alarm module is used to compare the abnormal fault index with a preset threshold value, and determine whether the electric bicycle battery charging cabinet has a fault based on the comparison result.

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