Solar energy storage battery system electrical connection point fault time prediction method and system

Abstract

A method and system for predicting the time point of fault at the electrical connection point of a solar energy storage battery system. In the method, real-time data of all electrical connection points are obtained, and all real-time data are respectively input into a data analysis function to obtain a number of detection scores corresponding to all real-time data; when it is determined that there is no detection score greater than a preset danger value among the several detection scores, the real-time data is preprocessed to obtain preprocessed data; the preprocessed data is processed by a moving average algorithm to obtain real real-time data; a state trend curve is obtained by fitting the real real-time data; a fault prediction time point is obtained according to the state trend curve; and the fault prediction time point is sent to a detection terminal. Predictions are made based on the real-time data of the electrical connection points to obtain the fault prediction time point, thereby avoiding the problem of being difficult to save when a fault occurs at the electrical connection point and reducing the loss of the battery system.

Description

Technical Field

[0001] The present application belongs to the field of battery system detection, and in particular, relates to a method and system for predicting the time point of failure of an electrical connection point of a solar energy storage battery system. Background Art

[0002] The solar energy storage battery system is a system that can convert solar energy into electrical energy and store it. The battery system has the characteristics of independence, scalability, long life and reliability. It can operate independently of the power grid and can respond to emergencies such as power outages to ensure the continuity of power supply.

[0003] The battery system is composed of multiple stacked battery packs, and there is an electrical connection between each battery pack. The connection point between each battery pack is an electrical connection point. The stability of the electrical connection point is crucial, and all electrical connection points of the battery system need to be tested regularly. In the related art, all electrical connection points are generally measured when the battery system is static. Specifically, the AC resistance of each battery pack is measured first. If the AC resistance meets the qualified standard, the battery system is charged and discharged, and the voltage difference of the entire group of battery packs during short-term charging or discharging at the required SOC is measured. If the pressure difference meets the qualified standard, the DC internal resistance of the electrical circuit is calculated. If the DC internal resistance meets the qualified standard, the battery system connection is reliable.

[0004] The above-mentioned electrical connection point detection method only detects the electrical connection point at the current moment, and can only detect the status of the electrical connection point at the current moment, but cannot obtain the status of the electrical connection point at subsequent times, resulting in difficulty in rescuing the electrical connection point when a fault occurs, which will cause damage to the battery system. Summary of the invention

[0005] The present application provides a method and system for predicting the time point of fault at the electrical connection point of a solar energy storage battery system, which is used to make predictions based on real-time data of the electrical connection point to obtain the predicted time point of the fault, thereby avoiding the problem of being difficult to remedy when a fault occurs at the electrical connection point and reducing the loss of the battery system.

[0006] In a first aspect, the present application provides a method for predicting the time point of a fault at an electrical connection point of a solar energy storage battery system, obtaining real-time data of all electrical connection points, and inputting all the real-time data into a data analysis function to obtain a number of detection points corresponding to all the real-time data, wherein the real-time data includes the temperature, current and resistance of the electrical connection points;

[0007] When it is determined that there is no test score greater than a preset risk value among the test scores, preprocessing the real-time data to obtain preprocessed data, where the preprocessed data is data after outliers are deleted from the real-time data;

[0008] The pre-processed data is processed by a moving average algorithm to obtain real real-time data, where the real real-time data is the data obtained by taking the average value of a number of real-time data;

[0009] A state trend curve is obtained by fitting the real real-time data, and the state trend curve is used to represent the state trend of the electrical connection point;

[0010] When it is determined that the slope of the state trend curve is less than a preset value, a polynomial regression algorithm is used to determine a functional formula of the state trend curve;

[0011] Determine, according to the functional formula, a fault prediction time point corresponding to when the detection score corresponding to the electrical connection point is greater than the preset risk value;

[0012] The fault prediction time point is sent to the detection terminal.

[0013] By adopting the above technical solution, the real-time data of all electrical connection points are obtained, and the real-time data is input into the data analysis function for processing to obtain the detection score corresponding to the real-time data. In the absence of a detection score greater than the preset danger value, the real-time data is preprocessed to obtain preprocessed data. Then the preprocessed data is smoothed by the moving average algorithm to obtain real real-time data. The state trend curve is fitted according to the real real-time data, and the fault prediction time point is obtained. Finally, the fault prediction time point is sent to the detection terminal. The prediction of the state of the electrical connection point of the household off-grid solar energy storage battery system is realized. By fitting the state trend curve and the fault prediction time point, the fault situation of the electrical connection point can be predicted, and the relevant personnel can be notified in time for processing, and the detection personnel can take measures in advance for repair or replacement. The problem of being difficult to save when a fault occurs at the electrical connection point is avoided, and the loss of the battery system is reduced.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, when it is determined that there is no detection score greater than a preset risk value among the plurality of detection scores, preprocessing the real-time data to obtain preprocessed data specifically includes:

[0015] Generate a real-time data three-dimensional coordinate system according to the real-time data, the real-time data including temperature, current and resistance, the real-time data three-dimensional coordinate system is a coordinate system with temperature, current and resistance as horizontal axis, vertical axis and vertical axis respectively;

[0016] According to the real-time data, all the electrical connection points are marked one by one in the real-time data three-dimensional coordinate system;

[0017] The electrical connection points whose distance from the preset coordinates exceeds a preset threshold are deleted to obtain preprocessed data.

[0018] By adopting the above technical solution, a real-time data three-dimensional coordinate system is generated according to the real-time data, and the coordinate axes are temperature, current and resistance respectively. Then all electrical connection points are marked in the coordinate system. Then, according to the preset threshold, the electrical connection points whose distance from the preset coordinate exceeds the threshold are deleted to obtain preprocessed data. This method for generating preprocessed data can effectively screen out electrical connection points that meet the requirements, reduce the impact of unreal data, improve the accuracy of data, and provide a more reliable data basis for subsequent data processing and fault prediction.

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, before acquiring real-time data of all electrical connection points and inputting all the real-time data into a data analysis function to obtain a plurality of detection scores corresponding to all the electrical connection points, the method further includes:

[0020] Acquire historical power consumption data, and determine a number of power-consuming devices according to the historical power consumption data, wherein the historical power consumption data includes a number of the power-consuming devices and a number of powers corresponding to the power-consuming devices in a one-to-one manner;

[0021] Generate an electric device selection page, the electric device selection page including a plurality of the electric devices and a plurality of controls corresponding to the plurality of the electric devices one by one;

[0022] In response to a user clicking on a plurality of selection controls, determining a plurality of selected electrical devices corresponding to the plurality of selection controls, wherein the number of the selection controls is not greater than the number of the controls;

[0023] A first instruction is sent to the battery system, so that the battery system supplies power to the selected electrical devices according to the first instruction.

[0024] By adopting the above technical solution, historical power consumption data is obtained and power consumption equipment is determined, and a power consumption equipment selection page is generated. Users can intuitively select the required power supply equipment and confirm the selected power consumption equipment by clicking the selection control, thereby realizing power supply control of the battery system. The actual situation of the battery system being used can be simulated according to the equipment selected by the user, providing accurate data reference for subsequent fault prediction of electrical connection points.

[0025] In conjunction with some embodiments of the first aspect, in some embodiments, after sending the fault prediction time point to the detection terminal, the method further includes:

[0026] In the case where it is determined that there is a detection score greater than the preset danger value among the plurality of detection scores, the electrical connection point corresponding to the detection score greater than the preset danger value is determined as a dangerous electrical connection point;

[0027] Determine the location information of the dangerous electrical connection point;

[0028] Generate a test result and add the test score and location information of the dangerous electrical connection point to the test result;

[0029] Setting the detection score and the location information to a first preset format;

[0030] Setting the detection scores corresponding to all electrical connection points except the dangerous electrical connection point to a second preset format;

[0031] The detection result is sent to the detection terminal.

[0032] By adopting the above technical solution, the electrical connection point corresponding to the detection score greater than the preset danger value is determined as a dangerous electrical connection point and the position information of the dangerous electrical connection point is determined, and then the detection result is generated and the detection score and position information of the dangerous electrical connection point are added to the detection result, and then the detection score and position information are set to a first preset format, and the detection scores corresponding to all electrical connection points except the dangerous electrical connection point are set to a second preset format, and finally the detection result is sent to the detection terminal, so that the detection personnel can clearly understand the specific information of the dangerous electrical connection point, avoid the problem of being difficult to save when a fault occurs in the electrical connection point, and reduce the loss of the battery system.

[0033] In conjunction with some embodiments of the first aspect, in some embodiments, after setting the detection scores corresponding to all electrical connection points except the dangerous electrical connection point to a second preset format, the method further includes:

[0034] sending a second instruction to the battery system so that the battery system stops supplying power according to the second instruction;

[0035] A third instruction is sent to the alarm device so that the alarm device turns on a preset alarm mode according to the third instruction.

[0036] By adopting the above technical solution, a second instruction is sent to the battery system to stop supplying power, and a third instruction is sent to the alarm device to turn on the preset alarm mode. In this way, when there is a dangerous electrical connection point in the detection result, the battery system can immediately stop supplying power and turn on the alarm mode, which can avoid the loss of the battery system and protect the safety of household electricity.

[0037] In conjunction with some embodiments of the first aspect, in some embodiments, after setting the detection scores corresponding to all electrical connection points except the dangerous electrical connection point to a second preset format, the method further includes:

[0038] Extracting real-time data of the dangerous electrical connection point, the real-time data including temperature, current and resistance;

[0039] Comparing the real-time data with normal data corresponding to the dangerous electrical connection point to obtain dangerous data;

[0040] The dangerous data is added to the detection result and set to a third preset format.

[0041] By adopting the above technical solution, when it is determined that there is a detection score greater than a preset danger value among several detection scores, the electrical connection point is determined as a dangerous electrical connection point, and the real-time data of the dangerous electrical connection point is extracted. By comparing the real-time data with the normal data corresponding to the dangerous electrical connection point, the dangerous data can be obtained and added to the detection results. This processing method can help the detection personnel to timely understand the fault information related to the dangerous electrical connection point, enable the detection personnel to repair the electrical connection point in time according to the fault information, and reduce the loss of the battery system.

[0042] In a second aspect, an embodiment of the present application provides a solar energy storage battery system electrical connection point fault time prediction system, the system comprising: a data acquisition module, for acquiring real-time data of all electrical connection points, and inputting all the real-time data into a data analysis function respectively, to obtain a number of detection points corresponding to all the real-time data, the real-time data including the temperature, current and resistance of the electrical connection points;

[0043] A preprocessing module, for preprocessing the real-time data to obtain preprocessed data when it is determined that there is no test score greater than a preset risk value among the test scores, wherein the preprocessed data is data after outliers are deleted from the real-time data;

[0044] A smoothing module is used to process the pre-processed data through a moving average algorithm to obtain real real-time data, where the real real-time data is the data obtained by taking the average value of a number of real-time data;

[0045] A fitting module, used for fitting a state trend curve according to the real real-time data, wherein the state trend curve is used for representing the state trend of the electrical connection point;

[0046] A functional formula determination module, used to determine the functional formula of the state trend curve using a polynomial regression algorithm when it is determined that the slope of the state trend curve is less than a preset value;

[0047] A prediction module, used to determine, according to the functional formula, a fault prediction time point corresponding to when the detection score corresponding to the electrical connection point is greater than the preset danger value;

[0048] The data output module is used to send the fault prediction time point to the detection terminal.

[0049] In a third aspect, an embodiment of the present application provides a solar energy storage battery system electrical connection point fault time prediction system, the system comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, the one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0050] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0051] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0052] 1. The present application provides a method for predicting the time point of faults at the electrical connection points of a solar energy storage battery system, obtains real-time data of all electrical connection points, and inputs the real-time data into a data analysis function for processing to obtain a detection score corresponding to the real-time data. In the absence of a detection score greater than a preset danger value, the real-time data is preprocessed to obtain preprocessed data. The preprocessed data is then smoothed by a moving average algorithm to obtain real real-time data. A state trend curve is fitted based on the real real-time data, and the fault prediction time point is obtained therefrom. Finally, the fault prediction time point is sent to a detection terminal. The prediction of the state of the electrical connection points of a household off-grid solar energy storage battery system is realized. By fitting the state trend curve and the fault prediction time point, the fault condition of the electrical connection point can be predicted, and relevant personnel can be notified in time for processing, and the detection personnel can take measures in advance for repair or replacement. The problem of being difficult to save when a fault occurs at the electrical connection point is avoided, and the loss of the battery system is reduced.

[0053] 2. The present application provides a method for predicting the time point of failure of electrical connection points of a solar energy storage battery system, and generates a real-time data three-dimensional coordinate system based on real-time data, with the coordinate axes being temperature, current, and resistance. Then all electrical connection points are marked in the coordinate system. Then, according to a preset threshold, the electrical connection points whose distance from the preset coordinates exceeds the threshold are deleted to obtain preprocessed data. This method for generating preprocessed data can effectively screen out electrical connection points that meet the requirements, reduce the impact of unreal data, improve the accuracy of data, and provide a more reliable data basis for subsequent data processing and fault prediction.

[0054] 3. The present application provides a method for predicting the time point of failure of an electrical connection point of a solar energy storage battery system. When a detection score greater than a preset danger value is determined among several detection scores, the electrical connection point is determined as a dangerous electrical connection point, and real-time data of the dangerous electrical connection point is extracted. By comparing the real-time data with the normal data corresponding to the dangerous electrical connection point, the dangerous data can be obtained and added to the test results. This processing method can help the detection personnel to timely understand the fault information related to the dangerous electrical connection point, and enable the detection personnel to repair the electrical connection point in time according to the fault information, thereby reducing the loss of the battery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a method for predicting the time point of a fault at an electrical connection point of a solar energy storage battery system in an embodiment of the present application.

[0056] Figure 2 It is another flow chart of a method for predicting the time point of a fault at an electrical connection point of a solar energy storage battery system in an embodiment of the present application.

[0057] Figure 3 It is a schematic diagram of the functional module structure of a solar energy storage battery system electrical connection point failure time point prediction system provided in an embodiment of the present application.

[0058] Figure 4 It is a schematic diagram of the physical device structure of a solar energy storage battery system electrical connection point failure time point prediction system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0060] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0061] The following describes a scenario in which this application can be applied:

[0062] A user has an off-grid solar energy storage battery system at home. One day, there was a power outage in the area where the user lived, and the user needed to activate the off-grid solar energy storage battery system to power home devices. At this time, the relevant inspection personnel came to the user's home to inspect the electrical connection points of the off-grid solar energy storage battery system. After the inspection was completed at the current moment, the inspection personnel found that there was no fault, so they informed the user that it could be used normally. The user started the off-grid solar energy storage battery system. After a while, he found that the voltage of the home equipment was unstable and the voltage of the off-grid solar energy storage battery system suddenly dropped. The user immediately shut down the off-grid solar energy storage battery system, which made it impossible to continue using other devices.

[0063] In order to solve the above problems, the present application provides a method and system for predicting the fault time point of the electrical connection point of a solar energy storage battery system, which is used to make predictions based on the real-time data of the electrical connection point to obtain the fault prediction time point, avoid the problem that it is difficult to remedy the fault when the electrical connection point fails, and reduce the loss of the battery system.

[0064] Combine the following Figure 1 , a method for predicting the time point of a fault at an electrical connection point of a solar energy storage battery system in an embodiment of the present application is described:

[0065] See also Figure 1 , is a flow chart of a method for predicting the time point of a fault at an electrical connection point of a solar energy storage battery system in an embodiment of the present application.

[0066] S101, obtaining historical power consumption data, and determining a number of power-consuming devices according to the historical power consumption data, wherein the historical power consumption data includes a number of power-consuming devices and a number of powers corresponding to the number of power-consuming devices;

[0067] The server obtains historical power consumption data to understand the user's power consumption and determines the corresponding power-consuming equipment based on the historical power consumption data. For example, the server can obtain power consumption data for the past month, including power data of power-consuming equipment such as refrigerators, air conditioners, and televisions. Assume that the historical power consumption data shows that the average power of refrigerators is 200W, the average power of air conditioners is 1000W, and the average power of televisions is 150W.

[0068] The server can use data collection technology, such as sensors or smart meters, to obtain historical power consumption data. The collected data can be stored in a database for subsequent analysis. The server can use data analysis technology, such as data mining or machine learning algorithms, to process the historical power consumption data to determine the power consumption devices and their corresponding power. For example, a clustering algorithm can be used to classify the power consumption data and identify different power consumption devices and their power.

[0069] S102, generating an electrical equipment selection page;

[0070] The server generates an electric device selection page based on the determined electric devices, for the user to select the electric devices to be monitored. The electric device selection page includes several electric devices and several controls corresponding to the several electric devices. For example, the server can generate an interface that lists electric devices such as refrigerators, air conditioners, and televisions, and provides controls corresponding to these devices for the user to select. The user can make a selection based on his or her own needs, and the power in step S101 can be used as one of the references for selection.

[0071] The server can use Web development technology to generate an electrical equipment selection page, which can include a list of electrical equipment and controls such as check boxes, for users to select electrical equipment to be monitored.

[0072] S103, in response to the user clicking on a number of selection controls, determining a number of selected electrical devices corresponding to the number of selection controls;

[0073] The server determines the electric device selected by the user based on the user's operation on the electric device selection page. For example, if the user clicks on the controls corresponding to the refrigerator and the air conditioner, the server determines that the electric devices to be turned on are the refrigerator and the air conditioner based on the user's selection.

[0074] The server can use front-end development technology, such as JavaScript, to respond to the user's operation on the power device selection page. By monitoring the user's click event, the server can obtain the control selected by the user and determine the power device to be turned on based on the selected control.

[0075] S104, sending a first instruction to the battery system, so that the battery system supplies power to a plurality of selected electrical devices according to the first instruction;

[0076] The server sends instructions to the battery system, causing the battery system to supply power to the power-consuming device selected by the user, so as to simulate the actual situation of the user using the battery system.

[0077] The server can communicate with the battery system using network communication technology, such as HTTP protocol. The server sends instructions to the battery system, and can use RESTful API or other suitable communication protocols to enable the battery system to power the power-consuming device selected by the user.

[0078] S105, acquiring real-time data of all electrical connection points, and inputting all real-time data into a data analysis function respectively, to obtain a number of detection scores corresponding to all real-time data;

[0079] The server obtains real-time data of all electrical connection points, including the temperature, current, and resistance of the electrical connection points. Assume that the real-time data obtained by the server is: temperature 1 is 40°C, current 1 is 2A, and resistance 1 is 110Ω; temperature 2 is 60°C, current 2 is 4A, and resistance 2 is 60Ω.

[0080] Then, the server inputs these real-time data into the data analysis function for calculation to obtain the detection score corresponding to the real-time data.

[0081] For example, through the calculation of the above data analysis function, the following detection scores are obtained:

[0082] Electrical connection point 1: 3 points;

[0083] Electrical connection point 2: 9 points;

[0084] Electrical connection point 3: 4 points;

[0085] Electrical connection point 4: 8 points;

[0086] For the sake of convenience in explanation, the above data are listed in this embodiment. In actual situations, there may be more data, which is not limited here.

[0087] The server can use data acquisition technology, such as sensors or smart meters, to obtain real-time data of all electrical connection points. For example, the temperature of the electrical connection point can be obtained using a temperature sensor installed inside the battery system. The server can input the acquired real-time data into a data analysis function for processing. The data analysis function can use statistical analysis, machine learning, or artificial intelligence techniques to analyze the real-time data and obtain detection scores that correspond to the real-time data one by one. For example, a timing analysis algorithm can be used to detect the status of the electrical connection point.

[0088] S106. When it is determined that there is no test score greater than a preset risk value among the plurality of test scores, preprocessing the real-time data to obtain preprocessed data;

[0089] The server determines whether there is a detection score greater than the preset danger value. If there is no detection score greater than the preset danger value, the server will pre-process the real-time data to obtain more accurate data. For example, the real-time data is filtered, denoised, etc. to eliminate noise interference and obtain pre-processed data. Specifically:

[0090] Generate a real-time data three-dimensional coordinate system according to the real-time data, the real-time data including temperature, current and resistance, the real-time data three-dimensional coordinate system is a coordinate system with temperature, current and resistance as horizontal axis, vertical axis and vertical axis respectively;

[0091] Mark all electrical connection points one by one in the real-time data three-dimensional coordinate system according to the real-time data;

[0092] The electrical connection points whose distance from the preset coordinates exceeds a preset threshold are deleted to obtain preprocessed data.

[0093] The server can use data processing techniques, such as filtering algorithms or anomaly detection algorithms, to preprocess real-time data. The purpose of preprocessing is to remove noise or outliers to improve the accuracy of the data. For example, a moving average or median filter algorithm can be used to smooth real-time data.

[0094] S107, processing the preprocessed data by a moving average algorithm to obtain real real-time data;

[0095] The server uses the moving average algorithm to process the preprocessed data to obtain smoother real-time data. The real-time data is the average of several real-time data. The moving average algorithm performs a weighted average of the preprocessed data and the data of adjacent time periods to eliminate instantaneous fluctuations and noise.

[0096] The server can use a moving average algorithm to process the preprocessed data to obtain smoother real-time data. The moving average algorithm can reduce the impact of instantaneous fluctuations and noise by calculating the moving average of the data and obtain more stable data. For example, a weighted moving average algorithm can be used to calculate real-time data.

[0097] S108, obtaining a state trend curve according to the real real-time data fitting;

[0098] The server obtains a state trend curve by fitting the real real-time data to understand the state change trend of the electrical connection point. The state trend curve is used to represent the state trend of the electrical connection point. The server can use regression analysis, curve fitting and other technologies to fit the state trend curve.

[0099] The purpose of fitting is to find a curve function that can best approximate the actual data points in order to better understand the state changes of the electrical connection points.

[0100] For example, suppose there is a set of real-time data: [221, 225, 230], which represents the state values ​​of the electrical connection points at different time points. Regression analysis or curve fitting technology can be used to fit these data to find the most suitable curve function.

[0101] A commonly used curve fitting method is polynomial fitting. Through polynomial fitting, a polynomial function can be obtained to minimize the error between it and the actual data point. For the above data, a quadratic polynomial can be used for fitting, for example: f(x) = ax 2 +bx+c.

[0102] By mathematical methods, the coefficients a, b, and c of the polynomial can be adjusted to make the fitted curve function closest to the actual data. In this example, through polynomial fitting, we may get a value similar to f(x) = 0.1x 2 The curve function of +2.3x+215.

[0103] The server can use mathematical modeling and curve fitting techniques, such as linear regression or polynomial fitting, to fit the state trend curve of the electrical equipment based on real-time data. The state trend curve can be used to analyze the state change trend of the electrical equipment, such as the on / off state of the equipment, energy consumption changes, etc. The state trend curve obtained by fitting can better understand the working state of the electrical equipment.

[0104] S109, when it is determined that the slope of the state trend curve is less than a preset value, using a polynomial regression algorithm to determine a functional formula of the state trend curve;

[0105] The server can obtain the slope of the state trend curve according to calculation, and when the slope of the state trend curve is less than the preset value, it means that its state trend is declining, that is, the electrical connection point will fail within a period of time, and the preset value is a data reference for judging the state trend of the electrical connection point. The server uses a polynomial regression algorithm to determine the functional form of the state trend curve, such as the example listed in the above step S108, to obtain the functional form of the state trend curve.

[0106] S111, determining, according to a function, a fault prediction time point corresponding to when a detection score corresponding to the electrical connection point is greater than a preset risk value;

[0107] The server determines the fault prediction time point based on the state trend curve analysis. According to the function of the curve, the time point at which the electrical connection point may fail can be predicted. The detection score corresponding to the fault prediction time point must be greater than the preset danger value. For example, according to the increasing trend curve, it can be predicted that the electrical connection point may fail in the next few hours. The specific fault prediction time point needs to be calculated according to the actual situation, which will not be elaborated here.

[0108] S111. Send the fault prediction time point to the detection terminal.

[0109] The server sends the fault prediction time point to the detection terminal so that the detection personnel can prevent the fault of the electrical connection point in advance according to the fault prediction time point.

[0110] The above embodiments have the following beneficial effects:

[0111] The real-time data of all electrical connection points are obtained, including the temperature, current and resistance of the electrical connection points, and the real-time data is input into the data analysis function for processing to obtain the detection score corresponding to the real-time data. In the absence of a detection score greater than the preset danger value, the real-time data is preprocessed to obtain preprocessed data. Then the preprocessed data is smoothed by the moving average algorithm to obtain the real real-time data. The state trend curve is fitted according to the real real-time data, and the fault prediction time point is obtained. Finally, the fault prediction time point is sent to the detection terminal. The prediction of the state of the electrical connection point of the household off-grid solar energy storage battery system is realized. By fitting the state trend curve and the fault prediction time point, the fault situation of the electrical connection point can be predicted, and the relevant personnel can be notified in time for processing, and the detection personnel can take measures in advance for repair or replacement. The problem of being difficult to save when a fault occurs at the electrical connection point is avoided, and the loss of the battery system is reduced.

[0112] This function calculates the detection score through a mathematical formula. The various parameters in the formula are related to various indicators of the battery system, such as remaining power, current, resistance and voltage. By calculating the real-time data, the detection score related to the status of the electrical connection point can be obtained. It can more accurately evaluate the operation of the electrical connection point, improve the accuracy and reliability of the detection, and provide accurate data support for the subsequent prediction of the status of the electrical connection point.

[0113] A real-time data three-dimensional coordinate system is generated based on the real-time data, with the coordinate axes being temperature, current, and resistance. All electrical connection points are then marked in the coordinate system. Then, based on the preset threshold, the electrical connection points whose distance from the preset coordinate exceeds the threshold are deleted to obtain preprocessed data. This method for generating preprocessed data can effectively screen out electrical connection points that meet the requirements, reduce the impact of unreal data, improve data accuracy, and provide a more reliable data basis for subsequent data processing and fault prediction.

[0114] Obtain historical power consumption data and determine the power consumption equipment, generate a power consumption equipment selection page, and users can intuitively select the required power supply equipment and confirm the selected power consumption equipment by clicking the selection control, thereby realizing the power supply control of the battery system. It can simulate the actual situation of the battery system being used according to the equipment selected by the user, and provide accurate data reference for the subsequent fault prediction of the electrical connection point.

[0115] In addition, after predicting the fault prediction time point, the inspection personnel may also need to understand the specific situation of the dangerous electrical connection point in order to subsequently repair the dangerous electrical connection point. Figure 2 , describes how to enable detection personnel to more clearly understand the relevant information of dangerous electrical connection points in the embodiments of the present application:

[0116] See also Figure 2, is another flow chart of a method for predicting the time point of a fault at an electrical connection point of a solar energy storage battery system in an embodiment of the present application.

[0117] S201, when it is determined that there is a detection score greater than a preset danger value among the plurality of detection scores, an electrical connection point corresponding to the detection score greater than the preset danger value is determined as a dangerous electrical connection point;

[0118] The server determines whether there is a dangerous electrical connection point by comparing the detection data with the preset danger value. For example, assuming that the detection score is [5,7 9,12], and the preset danger value is 8, then the values ​​of the third and fourth detection scores are greater than the preset danger value, so the electrical connection point corresponding to the detection score is determined to be a dangerous electrical connection point.

[0119] S202, determining the location information of the dangerous electrical connection point;

[0120] The server needs to determine the location information of the dangerous electrical connection point so as to subsequently repair it. For example, the server can obtain the location information of the electrical connection point from a database or other data source. Assume that the location information of the dangerous electrical connection point is "location 1, location 2".

[0121] S203, generating a test result and adding the test score and location information of the dangerous electrical connection point to the test result;

[0122] The server generates a data structure containing the test results and adds the test scores and location information of the dangerous electrical connection points to the data structure. For example, the generated test result data structure is as follows:

[0123] "Dangerous electrical connection point 1";

[0124] “Detection score”: [9],

[0125] "Location information": Location 1;

[0126] "Dangerous electrical connection point 2";

[0127] “Detection score”:

[12] ,

[0128] "Location information": Location 2;

[0129] The above detection result data structure is only one of all the examples, and other data structures are not described here.

[0130] S204, setting the detection score and location information to a first preset format;

[0131] The server sets the detection score and location information of the dangerous electrical connection point to a first preset format. The preset format can be a specific data format or standard for subsequent processing and transmission. For example, the detection score and location information are set to a bold format.

[0132] It is understandable that the specific format of the first preset format may be other formats, such as changing the font color or adding an underline, etc., which is not limited here.

[0133] S205, setting the detection scores corresponding to all electrical connection points except the dangerous electrical connection points to a second preset format;

[0134] The server sets the detection scores corresponding to all electrical connection points except the dangerous electrical connection points to a second preset format, which can help distinguish dangerous and normal electrical connection points. The specific format of the second preset format is not described in detail here.

[0135] S206, sending a second instruction to the battery system so that the battery system stops supplying power according to the second instruction;

[0136] The server sends a command to the battery system to cause the battery system to stop supplying power to all devices.

[0137] The server can communicate with the battery system using network communication technology, such as HTTP protocol. The server sends instructions to the battery system, and can use RESTful API or other suitable communication protocols to enable the battery system to power the power-consuming device selected by the user.

[0138] S207, sending a third instruction to the alarm device so that the alarm device turns on a preset alarm mode according to the third instruction;

[0139] The server sends instructions to the alarm device, requiring it to work according to the preset alarm mode. This can remind the inspection personnel or related personnel of the existence of dangerous electrical connection points.

[0140] The preset alarm mode may be an audible and visual alarm or a vibration alarm, etc., which is not limited here.

[0141] S208, extracting real-time data of dangerous electrical connection points, the real-time data including temperature, current and resistance;

[0142] The server extracts real-time data from dangerous electrical connection points, including temperature, current, resistance, etc. For example, suppose the real-time data of dangerous electrical connection points are as follows:

[0143] Temperature: 42°C

[0144] Current: 5A

[0145] Resistance: 10Ω

[0146] S209, comparing the real-time data with the normal data corresponding to the dangerous electrical connection point to obtain dangerous data;

[0147] The server compares the real-time data of the dangerous electrical connection point with the normal data of the connection point to detect whether there is an abnormal or dangerous situation. For example, if the normal temperature range of the dangerous electrical connection point is 20℃ to 30℃, and the real-time temperature is 42℃, it can be judged that the temperature exceeds the normal range, which is dangerous data.

[0148] S210, adding the dangerous data to the detection result and setting it into a third preset format;

[0149] The server adds the dangerous data to the detection result and sets it to a third preset format. This helps to record and handle problems with dangerous electrical connection points. The third preset format can be a change in font color or underline, etc., which is not limited here.

[0150] S211, sending the detection result to the detection terminal.

[0151] The server sends the detection results to the detection terminal so that the detection personnel can understand the specific situation of the dangerous connection point.

[0152] The above embodiments have the following beneficial effects:

[0153] An electrical connection point corresponding to a detection score greater than a preset danger value is determined as a dangerous electrical connection point and the location information of the dangerous electrical connection point is determined. A detection result is then generated and the detection score and location information of the dangerous electrical connection point are added to the detection result. The detection score and location information are then set to a first preset format, and the detection scores corresponding to all electrical connection points except the dangerous electrical connection point are set to a second preset format. Finally, the detection result is sent to the detection terminal, so that the detection personnel can clearly understand the specific information of the dangerous electrical connection point, avoid the problem of being difficult to save when a fault occurs in the electrical connection point, and reduce the loss of the battery system.

[0154] A second instruction is sent to the battery system to stop supplying power, and a third instruction is sent to the alarm device to activate a preset alarm mode. In this way, when a dangerous electrical connection point is found in the detection result, the battery system can immediately stop supplying power and activate the alarm mode, thereby avoiding loss of the battery system and protecting the safety of household electricity use.

[0155] When it is determined that there is a detection score greater than a preset danger value among several detection scores, the electrical connection point is determined as a dangerous electrical connection point, and the real-time data of the dangerous electrical connection point is extracted. By comparing the real-time data with the normal data corresponding to the dangerous electrical connection point, the dangerous data can be obtained and added to the detection results. This processing method can help the detection personnel to timely understand the fault information related to the dangerous electrical connection point, enable the detection personnel to repair the electrical connection point in time according to the fault information, and reduce the loss of the battery system.

[0156] The following describes the system in the embodiment of the present application from a module perspective:

[0157] See also Figure 3 , is a schematic diagram of the functional module structure of a solar energy storage battery system electrical connection point failure time point prediction system provided in an embodiment of the present application.

[0158] The system includes:

[0159] The data acquisition module 301 is used to acquire the real-time data of all electrical connection points, and input all the real-time data into the data analysis function to obtain a number of detection points corresponding to all the real-time data, the real-time data including the temperature, current and resistance of the electrical connection points;

[0160] A preprocessing module 302 is used to preprocess the real-time data to obtain preprocessed data when it is determined that there is no detection score greater than a preset risk value among the detection scores, where the preprocessed data is data after outliers are deleted from the real-time data;

[0161] The smoothing module 303 is used to process the pre-processed data by a moving average algorithm to obtain real real-time data, where the real real-time data is the data obtained by taking the average value of a number of real-time data;

[0162] A fitting module 304, used for fitting a state trend curve according to the real real-time data, where the state trend curve is used to represent the state trend of the electrical connection point;

[0163] A functional formula determination module 305, configured to determine the functional formula of the state trend curve using a polynomial regression algorithm when it is determined that the slope of the state trend curve is less than a preset value;

[0164] Prediction module 306, used to determine the fault prediction time point corresponding to when the detection score corresponding to the electrical connection point is greater than a preset danger value according to the function;

[0165] The data output module 307 is used to send the fault prediction time point to the detection terminal.

[0166] In some embodiments, the pre-processing module 302 specifically includes:

[0167] A three-dimensional coordinate system generating unit, used for generating a real-time data three-dimensional coordinate system according to the real-time data, wherein the real-time data includes temperature, current and resistance, and the real-time data three-dimensional coordinate system is a coordinate system with temperature, current and resistance as horizontal axis, vertical axis and vertical axis respectively;

[0168] A marking unit, used for marking all the electrical connection points one by one in the three-dimensional coordinate system of the real-time data according to the real-time data;

[0169] The deleting unit is used to delete the electrical connection points whose distance from the preset coordinates exceeds a preset threshold value to obtain preprocessed data.

[0170] In some embodiments, the system further comprises:

[0171] An electric device determination submodule is used to obtain historical electric device data and determine a number of electric devices according to the historical electric device data, wherein the historical electric device data includes a number of the electric devices and a number of powers corresponding to the electric devices in a one-to-one manner;

[0172] The device selection page generation submodule is used to generate an electric device selection page, wherein the electric device selection page includes a plurality of electric devices and a plurality of controls corresponding to the plurality of electric devices one by one;

[0173] A selected power-consuming device determination submodule, configured to determine a number of selected power-consuming devices corresponding to a number of selection controls in response to a user clicking on the number of selection controls, wherein the number of the selection controls is not greater than the number of the controls;

[0174] The first instruction sending submodule is used to send a first instruction to the battery system, so that the battery system supplies power to the selected electrical devices according to the first instruction.

[0175] In some embodiments, the system further comprises:

[0176] a dangerous electrical connection point determination submodule, for determining, when it is determined that there is a detection score greater than the preset danger value among the plurality of detection scores, an electrical connection point corresponding to the detection score greater than the preset danger value as a dangerous electrical connection point;

[0177] A location information determination submodule, used to determine the location information of the dangerous electrical connection point;

[0178] An adding submodule is used to generate a detection result and add the detection score and location information of the dangerous electrical connection point to the detection result;

[0179] A first preset format setting submodule, used to set the detection score and the position information to a first preset format;

[0180] A second preset format setting submodule, used for setting the detection scores corresponding to all electrical connection points except the dangerous electrical connection point to a second preset format;

[0181] The data sending submodule is used to send the detection result to the detection terminal.

[0182] In some embodiments, the system further comprises:

[0183] A second instruction sending submodule, used for sending a second instruction to the battery system so that the battery system stops supplying power according to the second instruction;

[0184] The third instruction sending submodule is used to send a third instruction to the alarm device so that the alarm device turns on a preset alarm mode according to the third instruction.

[0185] In some embodiments, the system further comprises:

[0186] An extraction submodule, used for extracting real-time data of the dangerous electrical connection point, the real-time data including temperature, current and resistance;

[0187] A comparison submodule, used for comparing the real-time data with normal data corresponding to the dangerous electrical connection point to obtain dangerous data;

[0188] The third preset format setting submodule is used to add the dangerous data to the detection result and set it to a third preset format.

[0189] The above describes the system in the embodiment of the present application from the perspective of modular functional entities. The following describes the system in the embodiment of the present application from the perspective of hardware processing. Figure 4 , which is a schematic diagram of the physical device structure of a solar energy storage battery system electrical connection point failure time point prediction system provided in an embodiment of the present application.

[0190] It should be noted that Figure 4 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0191] like Figure 4As shown, the server includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method in the above embodiment. In RAM 403, various programs and data required for system operation are also stored. CPU 401, ROM 402 and RAM 403 are connected to each other through bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0192] The following components are connected to the I / O interface 405: an input section 406 including a camera, an infrared sensor, etc.; an output section 407 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0193] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the present invention are performed.

[0194] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0195] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0196] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0197] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0198] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0199] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0200] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for predicting the time point of failure of an electrical connection point of a solar energy storage battery system, characterized in that: include: Acquire real-time data of all electrical connection points, and input all the real-time data into a data analysis function to obtain a number of detection points corresponding to all the real-time data, wherein the real-time data includes temperature, current and resistance of the electrical connection points; When it is determined that there is no test score greater than a preset risk value among the plurality of test scores, preprocessing the real-time data to obtain preprocessed data, wherein the preprocessed data is data after outliers are deleted from the real-time data; Processing the preprocessed data by a moving average algorithm to obtain real real-time data, wherein the real real-time data is data obtained by taking the average value of a number of real-time data; A state trend curve is obtained by fitting the real real-time data, wherein the state trend curve is used to represent the state trend of the electrical connection point; When it is determined that the slope of the state trend curve is less than a preset value, a polynomial regression algorithm is used to determine a functional formula of the state trend curve; Determine, according to the functional formula, a fault prediction time point corresponding to when the detection score corresponding to the electrical connection point is greater than the preset risk value; The fault prediction time point is sent to the detection terminal.

2. The method according to claim 1, characterized in that In the case where it is determined that there is no detection score greater than the preset risk value among the plurality of detection scores, preprocessing the real-time data to obtain preprocessed data specifically includes: Generate a real-time data three-dimensional coordinate system according to the real-time data, wherein the real-time data three-dimensional coordinate system is a coordinate system with temperature, current and resistance as the horizontal axis, vertical axis and vertical axis respectively; Mark all the electrical connection points one by one in the real-time data three-dimensional coordinate system according to the real-time data; The real-time data corresponding to the electrical connection point whose distance from the preset coordinate exceeds the preset threshold is determined as an abnormal value and deleted to obtain preprocessed data.

3. The method according to claim 1, characterized in that Before acquiring the real-time data of all the electrical connection points and inputting all the real-time data into the data analysis function to obtain a number of detection scores corresponding to all the electrical connection points, the method further includes: Acquire historical power consumption data, and determine a number of power-consuming devices according to the historical power consumption data, wherein the historical power consumption data includes a number of the power-consuming devices and a number of powers corresponding to the power-consuming devices in a one-to-one manner; Generate an electric device selection page, the electric device selection page including a plurality of the electric devices and a plurality of controls corresponding to the plurality of the electric devices one by one; In response to a user clicking on a plurality of selection controls, determining a plurality of selected electrical devices corresponding to the plurality of selection controls, wherein the number of the selection controls is not greater than the number of the controls; A first instruction is sent to the battery system, so that the battery system supplies power to the selected electrical devices according to the first instruction.

4. The method according to claim 1, characterized in that: After sending the fault prediction time point to the detection terminal, the method further includes: In the case where it is determined that there is a detection score greater than the preset danger value among the plurality of detection scores, the electrical connection point corresponding to the detection score greater than the preset danger value is determined as a dangerous electrical connection point; Determining the location information of the dangerous electrical connection point; generating a detection result and adding the detection score and location information of the dangerous electrical connection point to the detection result; Setting the detection score and the location information to a first preset format; Setting the detection scores corresponding to all electrical connection points except the dangerous electrical connection point to a second preset format; The detection result is sent to the detection terminal.

5. The method according to claim 4, characterized in that After setting the detection scores corresponding to all electrical connection points except the dangerous electrical connection point to a second preset format, the method further includes: sending a second instruction to the battery system so that the battery system stops supplying power according to the second instruction; A third instruction is sent to the alarm device so that the alarm device turns on a preset alarm mode according to the third instruction.

6. The method according to claim 4, characterized in that After setting the detection scores corresponding to all electrical connection points except the dangerous electrical connection point to a second preset format, the method further includes: Extracting real-time data of the dangerous electrical connection point, wherein the real-time data includes temperature, current and resistance; Comparing the real-time data with normal data corresponding to the dangerous electrical connection point to obtain dangerous data; The danger data is added to the detection result and set to a third preset format.

7. A solar energy storage battery system electrical connection point fault time prediction system, characterized in that: include: A data acquisition module, used to acquire real-time data of all electrical connection points, and input all the real-time data into a data analysis function to obtain a number of detection points corresponding to all the real-time data, wherein the real-time data includes the temperature, current and resistance of the electrical connection points; A preprocessing module, configured to preprocess the real-time data to obtain preprocessed data when it is determined that there is no test score greater than a preset risk value among the test scores, wherein the preprocessed data is data after outliers are deleted from the real-time data; A smoothing module is used to process the pre-processed data through a moving average algorithm to obtain real real-time data, where the real real-time data is data obtained by taking the average value of a number of real-time data; A fitting module, used for fitting a state trend curve according to the real real-time data, wherein the state trend curve is used for representing the state trend of the electrical connection point; A functional formula determination module, configured to determine the functional formula of the state trend curve using a polynomial regression algorithm when it is determined that the slope of the state trend curve is less than a preset value; A prediction module, used to determine, according to the functional formula, a fault prediction time point corresponding to when the detection score corresponding to the electrical connection point is greater than the preset risk value; A data output module is used to send the fault prediction time point to the detection terminal.

8. A solar energy storage battery system electrical connection point fault time prediction system, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the system to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 6.

Images