Data analysis and processing method for energy storage energy-saving system

Abstract

The present application relates to a data analysis and processing method for an energy storage energy-saving system, which belongs to the field of energy storage technology. The method includes: obtaining relevant data information from a historical database; preprocessing the relevant data information to obtain first characteristic data; building a fault prediction model based on the first characteristic data; inputting the second characteristic data into the fault prediction model, and pushing a real-time warning of the fault risk according to the output result. The present application can reduce labor costs and improve the real-time and accuracy of data analysis through an intelligent data analysis method, so as to provide early warning prompts for energy storage power station failures, improve operation and maintenance efficiency, and thus ensure the safe and stable operation of the energy storage power station.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to a data analysis and processing method for an energy storage energy-saving system. Background Art

[0002] In recent years, with the development of information technology, the traditional energy industry has begun to transform with the characteristics of informatization and intelligence. In the field of energy storage, energy storage power stations include energy storage energy-saving systems, which include multiple energy storage units, each of which includes a large number of energy storage batteries. When the energy storage power station is running, the characteristics of the energy storage energy-saving system will change. With the improvement of time accuracy, the amount of data generated by the energy storage energy-saving system during operation will also increase. When faced with massive data, the requirements for real-time and accuracy of data analysis are also getting higher and higher. How to monitor the data characteristics of the energy storage energy-saving system and perform data analysis and processing to ensure the safe and stable operation of the energy storage power station is particularly important for stabilizing the power grid and preventing power outages.

[0003] At present, the current status and characteristics of the energy storage energy-saving system are mainly understood through the monitoring system of the energy storage power station, and then the fault is located and the operation and maintenance tasks are established relying on the experience of the operation and maintenance personnel. The steps are cumbersome and the operation and maintenance efficiency is low. Therefore, it is urgent to propose a data analysis and processing method for the energy storage energy-saving system that can improve the operation and maintenance efficiency. Summary of the invention

[0004] Based on this, it is necessary to provide a data analysis and processing method for an energy storage energy-saving system that can improve operation and maintenance efficiency in response to the above technical problems.

[0005] On the one hand, a data analysis and processing method for an energy storage type energy-saving system is provided, the method comprising:

[0006] Acquire relevant data information in a historical database, wherein the relevant data information at least includes energy storage type energy-saving system fault information and target device operation status information corresponding to the energy storage type energy-saving system fault information;

[0007] Preprocessing the relevant data information to obtain first characteristic data;

[0008] Based on the first characteristic data, construct a fault prediction model;

[0009] The second characteristic data is input into the fault prediction model, and a real-time warning is pushed for the fault risk according to the output result, wherein the second characteristic data at least includes the real-time operating status information of the target device in the energy storage energy-saving system.

[0010] Optionally, the preprocessing the relevant data information to obtain the first characteristic data includes:

[0011] Extracting first data from the energy storage energy-saving system fault information, wherein the first data includes at least one of the following: a fault time period, a fault type, a fault node location, a fault device and its number;

[0012] Based on the first data, using a first state function to calculate first state information of the energy storage energy-saving system fault information;

[0013] Extracting second data from the target device operation status information, wherein the second data includes at least one of the following: normal operation status, fault operation status, maintenance operation status, and shutdown status;

[0014] Based on the second data, calculating second state information corresponding to the target device operation state information using a second state function;

[0015] Determine, according to the first state information and the second state information, a state correlation degree between the energy storage energy-saving system fault information and its corresponding target device operating state information;

[0016] In response to detecting that the state association degree is greater than a first preset value, a plurality of association sets of the first state information and the second state information are extracted to generate first feature data.

[0017] Optionally, based on the first data, calculating the first state information of the energy storage energy-saving system fault information using a first state function includes:

[0018] Define the state set corresponding to the first data as , the probability that each state constitutes the failure information of the energy storage energy-saving system is ,and , define the first state function as:

[0019]

[0020] in, represents the output value of the first state function, Indicates the number of faulty devices. represents the first state function variable, Indicates the fault time period assignment, Indicates the fault type assignment, Indicates the fault node location assignment, Indicates the impact range of the fault node. If there is no impact on the surrounding area, the value is 1. Indicates the fault time period, Indicates the change in the equipment operating status within the fault time period;

[0021] Define the output value of the first state function The first state information.

[0022] Optionally, based on the second data, calculating the second state information corresponding to the target device operating state information by using the second state function includes:

[0023] Define the state set corresponding to the second data as , the probability that each state constitutes the target equipment operating status information corresponding to the energy storage energy-saving system fault information is ,and , define the second state function as:

[0024]

[0025] in, represents the output value of the second state function, Indicates the number of devices in the target operating state. represents the second state function variable, Indicates the number of devices in normal operation. Indicates the number of devices in abnormal operating status. Indicates the preset time length. Indicates fault operation status assignment;

[0026] Define the output value of the second state function The second state information.

[0027] Optionally, determining, according to the first state information and the second state information, a state correlation between the energy storage energy-saving system fault information and its corresponding target device operating state information includes:

[0028] Get the output value of the first state function and the second state function outputs the value , the first state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information is calculated using a state correlation calculation formula, the calculation formula being:

[0029]

[0030] in, represents the state coefficient, represents the iteration coefficient, , , are fitting coefficients, Indicates the first state association.

[0031] Optionally, the method further includes:

[0032] The first state correlation is corrected by using a correction function to obtain a second state correlation, wherein the correction function includes:

[0033] when When , the correction function includes:

[0034]

[0035] when When , the correction function includes:

[0036]

[0037] in, Indicates the preset threshold value, , Both represent the correction value, that is, the second state association degree, represents a constant, represents the correction factor, Indicates the value of the running state change;

[0038] The second state correlation is determined as the state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information.

[0039] Optionally, building a fault prediction model based on the first feature data includes:

[0040] In response to detecting that the state association degree is greater than a first preset value, extracting a plurality of association sets of the first state information and the second state information to generate first feature data;

[0041] Dividing the first feature data into a training set, a test set and a validation set according to a preset ratio;

[0042] Based on the training set, test set and validation set, respectively, the preset fault prediction model is trained, tested and validated;

[0043] When the accuracy of the preset fault prediction model is greater than the second preset value, the training is completed and a final fault prediction model is obtained, including:

[0044]

[0045] in, represents the predicted value, represents the adjustment function, Indicates the number of devices. represents the correction factor, is the coupling coefficient, represents the weighted sum of the real-time running status information changes, Indicates the change in real-time operating status information. Indicates the weighted sum of the real-time operating status information of the target device.

[0046] Optionally, the output result is a predicted value D, the second characteristic data is input into the fault prediction model, and real-time warning push of the fault risk is performed according to the output result, including:

[0047] In response to detecting that the predicted value D is greater than the third preset value, early warning information is pushed to the terminal for real-time early warning.

[0048] On the other hand, a data analysis and processing device for an energy storage type energy-saving system is provided, the device comprising:

[0049] An information acquisition module is used to acquire relevant data information in a historical database, wherein the relevant data information at least includes energy storage type energy-saving system fault information and target device operation status information corresponding to the energy storage type energy-saving system fault information;

[0050] A preprocessing module, used for preprocessing the relevant data information to obtain first characteristic data;

[0051] A model building module, used to build a fault prediction model based on the first feature data;

[0052] The early warning push module is used to input the second characteristic data into the fault prediction model and push a real-time early warning of the fault risk according to the output result. The second characteristic data at least includes the real-time operating status information of the target device in the energy storage energy-saving system.

[0053] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0054] Acquire relevant data information in a historical database, wherein the relevant data information at least includes energy storage type energy-saving system fault information and target device operation status information corresponding to the energy storage type energy-saving system fault information;

[0055] Preprocessing the relevant data information to obtain first characteristic data;

[0056] Based on the first characteristic data, construct a fault prediction model;

[0057] The second characteristic data is input into the fault prediction model, and a real-time warning is pushed for the fault risk according to the output result, wherein the second characteristic data at least includes the real-time operating status information of the target device in the energy storage energy-saving system.

[0058] In another aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0059] Acquire relevant data information in a historical database, wherein the relevant data information at least includes energy storage type energy-saving system fault information and target device operation status information corresponding to the energy storage type energy-saving system fault information;

[0060] Preprocessing the relevant data information to obtain first characteristic data;

[0061] Based on the first characteristic data, construct a fault prediction model;

[0062] The second characteristic data is input into the fault prediction model, and a real-time warning is pushed for the fault risk according to the output result, wherein the second characteristic data at least includes the real-time operating status information of the target device in the energy storage energy-saving system.

[0063] The data analysis and processing method of the above-mentioned energy storage energy-saving system comprises: obtaining relevant data information in a historical database, wherein the relevant data information at least comprises fault information of the energy storage energy-saving system and operating status information of a target device corresponding to the fault information of the energy storage energy-saving system; preprocessing the relevant data information to obtain first characteristic data; constructing a fault prediction model based on the first characteristic data; inputting the second characteristic data into the fault prediction model, and pushing a real-time early warning of the fault risk according to the output result, wherein the second characteristic data at least comprises the real-time operating status information of the target device in the energy storage energy-saving system. The present application can reduce labor costs and improve the real-time and accuracy of data analysis through an intelligent data analysis method, thereby being able to provide early warning prompts for energy storage power station faults, improve operation and maintenance efficiency, and thereby ensure the safe and stable operation of the energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 An application environment diagram of a data analysis and processing method for an energy storage type energy saving system in one embodiment;

[0065] Figure 2 A schematic flow chart of a data analysis and processing method for an energy storage type energy saving system in one embodiment;

[0066] Figure 3 A structural block diagram of a data analysis and processing device for an energy storage type energy saving system in one embodiment;

[0067] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0069] It should be understood that in the description of the present application, unless the context clearly requires otherwise, words such as "include", "comprises", and the like throughout the specification should be interpreted as including rather than being exclusive or exhaustive; that is, as including but not limited to.

[0070] It should also be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, "plurality" means two or more.

[0071] It should be noted that the terms "S1", "S2", etc. are only used for the purpose of describing the steps, and do not specifically refer to the order or sequence, nor are they used to limit the present application. They are only for the convenience of describing the method of the present application, and cannot be understood as indicating the order of the steps. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0072] The data analysis and processing method of the energy storage energy-saving system provided in this application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the data processing platform set on the server 104 through the network, wherein the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0073] In one embodiment, Figure 2 As shown, a data analysis and processing method for an energy storage energy-saving system is provided, and the method is applied to Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0074] S1: Acquire relevant data information in a historical database, wherein the relevant data information at least includes energy storage energy-saving system fault information and target device operation status information corresponding to the energy storage energy-saving system fault information.

[0075] It should be noted that the fault information of the energy storage energy-saving system may be the fault information corresponding to a certain time node, and the fault information may be the fault time period, fault type, fault node location, faulty equipment and its quantity, etc.; the target equipment operation status information corresponding to the fault information of the energy storage energy-saving system refers to the operation status information of the equipment in the energy storage energy-saving system when the energy storage energy-saving system is in a faulty state, which may include normal operation state, fault operation state, maintenance operation state and shutdown state. When the above information is used for subsequent data analysis and processing, the relevant data information is assigned accordingly through expert assignment, and the assignment is a scalar to ensure the accuracy of data analysis and processing.

[0076] S2: Preprocess the relevant data information to obtain first characteristic data.

[0077] It should be noted that this step specifically includes:

[0078] Extracting first data from the energy storage energy-saving system fault information, wherein the first data includes at least one of the following: a fault time period, a fault type, a fault node location, a fault device and its number;

[0079] Based on the first data, calculating the first state information of the energy storage energy-saving system fault information using a first state function includes:

[0080] Define the state set corresponding to the first data as , the probability that each state constitutes the failure information of the energy storage energy-saving system is ,and , define the first state function as:

[0081]

[0082] in, represents the output value of the first state function, Indicates the number of faulty devices. represents the first state function variable, Indicates the fault time period assignment, Indicates the fault type assignment, Indicates the fault node location assignment, Indicates the impact range of the fault node. If there is no impact on the surrounding area, the value is 1. Indicates the fault time period, Indicates the change in the equipment operating status within the fault time period;

[0083] Define the output value of the first state function is the first state information;

[0084] Extracting second data from the target device operation status information, wherein the second data includes at least one of the following: normal operation status, fault operation status, maintenance operation status, and shutdown status;

[0085] Calculating second state information corresponding to the target device operation state information using a second state function based on the second data includes:

[0086] Define the state set corresponding to the second data as , the probability that each state constitutes the target equipment operating status information corresponding to the energy storage energy-saving system fault information is ,and , define the second state function as:

[0087]

[0088] in, represents the output value of the second state function, Indicates the number of devices in the target operating state. represents the second state function variable, Indicates the number of devices in normal operation. Indicates the number of devices in abnormal operating status. Indicates the preset time length. Indicates fault operation status assignment;

[0089] Define the output value of the second state function is the second state information;

[0090] Determining the state correlation between the energy storage energy-saving system fault information and the corresponding target device operation state information according to the first state information and the second state information includes:

[0091] Get the output value of the first state function and the second state function outputs the value , the first state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information is calculated using a state correlation calculation formula, the calculation formula being:

[0092]

[0093] in, represents the state coefficient, represents the iteration coefficient, , , are fitting coefficients, represents the first state association degree;

[0094] Further, the first state correlation is corrected by using a correction function to obtain a second state correlation, and the correction function includes:

[0095] when When , the correction function includes:

[0096]

[0097] when When , the correction function includes:

[0098]

[0099] in, Indicates the preset threshold value, , Both represent the correction value, that is, the second state association degree, represents a constant, represents the correction factor, Indicates the value of the running state change;

[0100] Determine the second state correlation as the state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information;

[0101] In response to detecting that the state association degree is greater than a first preset value, a plurality of associated sets of the first state information and the second state information are extracted to generate first characteristic data, wherein the first preset value can be set according to actual needs.

[0102] S3: Building a fault prediction model based on the first feature data.

[0103] It should be noted that this step specifically includes:

[0104] In response to detecting that the state association degree is greater than a first preset value, extracting a plurality of association sets of the first state information and the second state information to generate first feature data;

[0105] Dividing the first feature data into a training set, a test set, and a validation set according to a preset ratio, wherein the preset ratio may be 7:2:1;

[0106] Based on the training set, test set and validation set, respectively, the preset fault prediction model is trained, tested and validated;

[0107] When the accuracy of the preset fault prediction model is greater than the second preset value, wherein the second preset value can be set according to actual needs, the training is completed to obtain the final fault prediction model, including:

[0108]

[0109] in, represents the predicted value, represents the adjustment function, Indicates the number of devices. represents the correction factor, is the coupling coefficient, represents the weighted sum of the real-time running status information changes, Indicates the change in real-time operating status information. Indicates the weighted sum of the real-time operating status information of the target device.

[0110] S4: Input the second characteristic data into the fault prediction model, and push a real-time warning of the fault risk according to the output result, wherein the second characteristic data at least includes the real-time operating status information of the target device in the energy storage energy-saving system.

[0111] It should be noted that the output result is the predicted value D. The real-time operation status information of the target device may include the number of devices in normal operation, the number of devices in faulty operation, the change in real-time operation status information (such as the number of devices that have changed from normal operation to faulty operation), etc. This step specifically includes:

[0112] Inputting the second characteristic data into the fault prediction model to obtain an output result and push a real-time warning of the fault risk includes:

[0113] In response to detecting that the predicted value D is greater than a third preset value, warning information is pushed to the terminal to perform a real-time warning, wherein the third preset value can be set according to actual needs.

[0114] In the data analysis and processing method of the above-mentioned energy storage energy-saving system, the method includes: obtaining relevant data information in a historical database, the relevant data information at least including energy storage energy-saving system fault information and target equipment operating status information corresponding to the energy storage energy-saving system fault information; preprocessing the relevant data information to obtain first feature data; constructing a fault prediction model based on the first feature data; inputting the second feature data into the fault prediction model, and pushing a real-time warning of the fault risk according to the output result, the second feature data at least including the real-time operating status information of the target equipment in the energy storage energy-saving system. The present application can reduce labor costs and improve the real-time and accuracy of data analysis through an intelligent data analysis method, so as to provide early warning prompts for energy storage power station faults, improve operation and maintenance efficiency, and thus ensure the safe and stable operation of the energy storage power station.

[0115] It should be understood that although Figure 2The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0116] In one embodiment, Figure 3 As shown, a data analysis and processing device for an energy storage energy-saving system is provided, including: an information acquisition module, a preprocessing module, a model building module and an early warning push module, wherein:

[0117] An information acquisition module is used to acquire relevant data information in a historical database, wherein the relevant data information at least includes energy storage type energy-saving system fault information and target device operation status information corresponding to the energy storage type energy-saving system fault information;

[0118] A preprocessing module, used for preprocessing the relevant data information to obtain first characteristic data;

[0119] A model building module, used to build a fault prediction model based on the first feature data;

[0120] The early warning push module is used to input the second characteristic data into the fault prediction model and push a real-time early warning of the fault risk according to the output result. The second characteristic data at least includes the real-time operating status information of the target device in the energy storage energy-saving system.

[0121] As a preferred implementation, in the embodiment of the present invention, the preprocessing module is specifically used for:

[0122] Extracting first data from the energy storage energy-saving system fault information, wherein the first data includes at least one of the following: a fault time period, a fault type, a fault node location, a fault device and its number;

[0123] Based on the first data, using a first state function to calculate first state information of the energy storage energy-saving system fault information;

[0124] Extracting second data from the target device operation status information, wherein the second data includes at least one of the following: normal operation status, fault operation status, maintenance operation status, and shutdown status;

[0125] Based on the second data, calculating second state information corresponding to the target device operation state information using a second state function;

[0126] Determine, according to the first state information and the second state information, a state correlation degree between the energy storage energy-saving system fault information and its corresponding target device operating state information;

[0127] In response to detecting that the state association degree is greater than a first preset value, a plurality of association sets of the first state information and the second state information are extracted to generate first feature data.

[0128] As a preferred implementation, in the embodiment of the present invention, the preprocessing module is further used for:

[0129] Define the state set corresponding to the first data as , the probability that each state constitutes the failure information of the energy storage energy-saving system is ,and , define the first state function as:

[0130]

[0131] in, represents the output value of the first state function, Indicates the number of faulty devices. represents the first state function variable, Indicates the fault time period assignment, Indicates the fault type assignment, Indicates the fault node location assignment, Indicates the impact range of the fault node. If there is no impact on the surrounding area, the value is 1. Indicates the fault time period, Indicates the change in the equipment operating status within the fault time period;

[0132] Define the output value of the first state function The first state information.

[0133] As a preferred implementation, in the embodiment of the present invention, the preprocessing module is further used for:

[0134] Define the state set corresponding to the second data as , the probability that each state constitutes the target equipment operating status information corresponding to the energy storage energy-saving system fault information is ,and , define the second state function as:

[0135]

[0136] in, represents the output value of the second state function, Indicates the number of devices in the target operating state. represents the second state function variable, Indicates the number of devices in normal operation. Indicates the number of devices in abnormal operating status. Indicates the preset time length. Indicates fault operation status assignment;

[0137] Define the output value of the second state function The second state information.

[0138] As a preferred implementation, in the embodiment of the present invention, the preprocessing module is further used for:

[0139] Get the output value of the first state function and the second state function outputs the value , the first state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information is calculated using a state correlation calculation formula, the calculation formula being:

[0140]

[0141] in, represents the state coefficient, represents the iteration coefficient, , , are fitting coefficients, Indicates the first state association.

[0142] As a preferred implementation, in the embodiment of the present invention, the preprocessing module is further used for:

[0143] The first state correlation is corrected by using a correction function to obtain a second state correlation, wherein the correction function includes:

[0144] when When , the correction function includes:

[0145]

[0146] when When , the correction function includes:

[0147]

[0148] in, Indicates the preset threshold value, , Both represent the correction value, that is, the second state association degree, represents a constant, represents the correction factor, Indicates the value of the running state change;

[0149] The second state correlation is determined as the state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information.

[0150] As a preferred implementation, in the embodiment of the present invention, the model building module is specifically used for:

[0151] In response to detecting that the state association degree is greater than a first preset value, extracting a plurality of association sets of the first state information and the second state information to generate first feature data;

[0152] Dividing the first feature data into a training set, a test set and a validation set according to a preset ratio;

[0153] Based on the training set, test set and validation set, respectively, the preset fault prediction model is trained, tested and validated;

[0154] When the accuracy of the preset fault prediction model is greater than the second preset value, the training is completed and a final fault prediction model is obtained, including:

[0155]

[0156] in, represents the predicted value, represents the adjustment function, Indicates the number of devices. represents the correction factor, is the coupling coefficient, represents the weighted sum of the real-time running status information changes, Indicates the change in real-time operating status information. Indicates the weighted sum of the real-time operating status information of the target device.

[0157] As a preferred implementation, in the embodiment of the present invention, the warning push module is specifically used for:

[0158] In response to detecting that the predicted value D is greater than the third preset value, early warning information is pushed to the terminal for real-time early warning.

[0159] For the specific definition of the data analysis and processing device of the energy storage energy-saving system, please refer to the definition of the data analysis and processing method of the energy storage energy-saving system mentioned above, which will not be repeated here. Each module in the data analysis and processing device of the above energy storage energy-saving system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0160] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a data analysis and processing method for an energy storage type energy-saving system is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0161] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0162] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0163] S1: Acquire relevant data information in a historical database, wherein the relevant data information at least includes energy storage energy-saving system fault information and target device operation status information corresponding to the energy storage energy-saving system fault information;

[0164] S2: preprocessing the relevant data information to obtain first characteristic data;

[0165] S3: constructing a fault prediction model based on the first characteristic data;

[0166] S4: Input the second characteristic data into the fault prediction model, and push a real-time warning of the fault risk according to the output result, wherein the second characteristic data at least includes the real-time operating status information of the target device in the energy storage energy-saving system.

[0167] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0168] Extracting first data from the energy storage energy-saving system fault information, wherein the first data includes at least one of the following: a fault time period, a fault type, a fault node location, a fault device and its number;

[0169] Based on the first data, using a first state function to calculate first state information of the energy storage energy-saving system fault information;

[0170] Extracting second data from the target device operation status information, wherein the second data includes at least one of the following: normal operation status, fault operation status, maintenance operation status, and shutdown status;

[0171] Based on the second data, calculating second state information corresponding to the target device operation state information using a second state function;

[0172] Determine, according to the first state information and the second state information, a state correlation degree between the energy storage energy-saving system fault information and its corresponding target device operating state information;

[0173] In response to detecting that the state association degree is greater than a first preset value, a plurality of association sets of the first state information and the second state information are extracted to generate first feature data.

[0174] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0175] Define the state set corresponding to the first data as , the probability that each state constitutes the failure information of the energy storage energy-saving system is ,and , define the first state function as:

[0176]

[0177] in, represents the output value of the first state function, Indicates the number of faulty devices. represents the first state function variable, Indicates the fault time period assignment, Indicates the fault type assignment, Indicates the fault node location assignment, Indicates the impact range of the fault node. If there is no impact on the surrounding area, the value is 1. Indicates the fault time period, Indicates the change in the equipment operating status within the fault time period;

[0178] Define the output value of the first state function The first state information.

[0179] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0180] Define the state set corresponding to the second data as , the probability that each state constitutes the target equipment operating status information corresponding to the energy storage energy-saving system fault information is ,and , define the second state function as:

[0181]

[0182] in, represents the output value of the second state function, Indicates the number of devices in the target operating state. represents the second state function variable, Indicates the number of devices in normal operation. Indicates the number of devices in abnormal operating status. Indicates the preset time length. Indicates fault operation status assignment;

[0183] Define the output value of the second state function The second state information.

[0184] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0185] Get the output value of the first state function and the second state function outputs the value , the first state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information is calculated using a state correlation calculation formula, the calculation formula being:

[0186]

[0187] in, represents the state coefficient, represents the iteration coefficient, , , are fitting coefficients, Indicates the first state association.

[0188] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0189] The first state correlation is corrected by using a correction function to obtain a second state correlation, wherein the correction function includes:

[0190] when When , the correction function includes:

[0191]

[0192] when When , the correction function includes:

[0193]

[0194] in, Indicates the preset threshold value, , Both represent the correction value, that is, the second state association degree, represents a constant, represents the correction factor, Indicates the value of the running state change;

[0195] The second state correlation is determined as the state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information.

[0196] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0197] In response to detecting that the state association degree is greater than a first preset value, extracting a plurality of association sets of the first state information and the second state information to generate first feature data;

[0198] Dividing the first feature data into a training set, a test set and a validation set according to a preset ratio;

[0199] Based on the training set, test set and validation set, respectively, the preset fault prediction model is trained, tested and validated;

[0200] When the accuracy of the preset fault prediction model is greater than the second preset value, the training is completed and a final fault prediction model is obtained, including:

[0201]

[0202] in, represents the predicted value, represents the adjustment function, Indicates the number of devices. represents the correction factor, is the coupling coefficient, represents the weighted sum of the real-time running status information changes, Indicates the change in real-time operating status information. Indicates the weighted sum of the real-time operating status information of the target device.

[0203] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0204] In response to detecting that the predicted value D is greater than the third preset value, early warning information is pushed to the terminal for real-time early warning.

[0205] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0206] S1: Acquire relevant data information in a historical database, wherein the relevant data information at least includes energy storage energy-saving system fault information and target device operation status information corresponding to the energy storage energy-saving system fault information;

[0207] S2: preprocessing the relevant data information to obtain first characteristic data;

[0208] S3: constructing a fault prediction model based on the first characteristic data;

[0209] S4: Input the second characteristic data into the fault prediction model, and push a real-time warning of the fault risk according to the output result, wherein the second characteristic data at least includes the real-time operating status information of the target device in the energy storage energy-saving system.

[0210] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0211] Extracting first data from the energy storage energy-saving system fault information, wherein the first data includes at least one of the following: a fault time period, a fault type, a fault node location, a fault device and its number;

[0212] Based on the first data, using a first state function to calculate first state information of the energy storage energy-saving system fault information;

[0213] Extracting second data from the target device operation status information, wherein the second data includes at least one of the following: normal operation status, fault operation status, maintenance operation status, and shutdown status;

[0214] Based on the second data, calculating second state information corresponding to the target device operation state information using a second state function;

[0215] Determine, according to the first state information and the second state information, a state correlation degree between the energy storage energy-saving system fault information and its corresponding target device operating state information;

[0216] In response to detecting that the state association degree is greater than a first preset value, a plurality of association sets of the first state information and the second state information are extracted to generate first feature data.

[0217] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0218] Define the state set corresponding to the first data as , the probability that each state constitutes the failure information of the energy storage energy-saving system is ,and , define the first state function as:

[0219]

[0220] in, represents the output value of the first state function, Indicates the number of faulty devices. represents the first state function variable, Indicates the fault time period assignment, Indicates the fault type assignment, Indicates the fault node location assignment, Indicates the impact range of the fault node. If there is no impact on the surrounding area, the value is 1. Indicates the fault time period, Indicates the change in the equipment operating status within the fault time period;

[0221] Define the output value of the first state function The first state information.

[0222] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0223] Define the state set corresponding to the second data as , the probability that each state constitutes the target equipment operating status information corresponding to the energy storage energy-saving system fault information is ,and , define the second state function as:

[0224]

[0225] in, represents the output value of the second state function, Indicates the number of devices in the target operating state. represents the second state function variable, Indicates the number of devices in normal operation. Indicates the number of devices in abnormal operating status. Indicates the preset time length. Indicates fault operation status assignment;

[0226] Define the output value of the second state function The second state information.

[0227] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0228] Get the output value of the first state function And the second state function outputs the value , the first state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information is calculated using a state correlation calculation formula, the calculation formula being:

[0229]

[0230] in, represents the state coefficient, represents the iteration coefficient, , , are fitting coefficients, Indicates the first state association.

[0231] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0232] The first state correlation is corrected by using a correction function to obtain a second state correlation, wherein the correction function includes:

[0233] when When , the correction function includes:

[0234]

[0235] when When , the correction function includes:

[0236]

[0237] in, Indicates the preset threshold value, , Both represent the correction value, that is, the second state association degree, represents a constant, represents the correction factor, Indicates the value of the running state change;

[0238] The second state correlation is determined as the state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information.

[0239] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0240] In response to detecting that the state association degree is greater than a first preset value, extracting a plurality of association sets of the first state information and the second state information to generate first feature data;

[0241] Dividing the first feature data into a training set, a test set and a validation set according to a preset ratio;

[0242] Based on the training set, test set and validation set, respectively, a preset fault prediction model is trained, tested and validated;

[0243] When the accuracy of the preset fault prediction model is greater than the second preset value, the training is completed and a final fault prediction model is obtained, including:

[0244]

[0245] in, represents the predicted value, represents the adjustment function, Indicates the number of devices. represents the correction factor, is the coupling coefficient, represents the weighted sum of the real-time running status information changes, Indicates the change in real-time operating status information. Represents the weighted sum of the real-time operating status information of the target device.

[0246] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0247] In response to detecting that the predicted value D is greater than the third preset value, warning information is pushed to the terminal to perform real-time warning.

[0248] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0249] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0250] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A data analysis and processing method for an energy storage energy-saving system, characterized in that: include: Acquire relevant data information in a historical database, wherein the relevant data information at least includes energy storage type energy-saving system fault information and target device operation status information corresponding to the energy storage type energy-saving system fault information; Preprocessing the relevant data information to obtain first characteristic data; Based on the first characteristic data, construct a fault prediction model; Inputting the second characteristic data into the fault prediction model, and pushing a real-time warning of the fault risk according to the output result, wherein the second characteristic data at least includes the real-time operating status information of the target device in the energy storage energy-saving system; The preprocessing of the relevant data information to obtain the first characteristic data includes: Extracting first data from the energy storage energy-saving system fault information, wherein the first data includes at least one of the following: a fault time period, a fault type, a fault node location, a fault device and its number; Based on the first data, using a first state function to calculate first state information of the energy storage energy-saving system fault information; Extracting second data from the target device operation status information, wherein the second data includes at least one of the following: normal operation status, fault operation status, maintenance operation status, and shutdown status; Based on the second data, calculating second state information corresponding to the target device operation state information using a second state function; Determine, according to the first state information and the second state information, a state correlation degree between the energy storage energy-saving system fault information and its corresponding target device operating state information; In response to detecting that the state association degree is greater than a first preset value, extracting a plurality of association sets of the first state information and the second state information to generate first feature data; Based on the first data, the first state information of the energy storage energy-saving system fault information is calculated using the first state function, including: Define the state set corresponding to the first data as , the probability that each state constitutes the failure information of the energy storage energy-saving system is ,and , define the first state function as: in, represents the output value of the first state function, Indicates the number of faulty devices. represents the first state function variable, Indicates the fault time period assignment, Indicates the fault type assignment, Indicates the fault node location assignment, Indicates the impact range of the fault node. If there is no impact on the surrounding area, the value is 1. Indicates the fault time period, Indicates the change in the equipment operating status within the fault time period; Define the output value of the first state function is the first state information; Calculating the second state information corresponding to the target device operation state information by using the second state function based on the second data includes: Define the state set corresponding to the second data as , the probability that each state constitutes the target equipment operating status information corresponding to the energy storage energy-saving system fault information is ,and , define the second state function as: in, represents the output value of the second state function, Indicates the number of devices in the target operating state. represents the second state function variable, Indicates the number of devices in normal operation. Indicates the number of devices in abnormal operating status. Indicates the preset time length. Indicates fault operation status assignment; Define the output value of the second state function is the second state information; Determining the state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information according to the first state information and the second state information includes: Get the output value of the first state function and the second state function outputs the value , the first state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information is calculated using a state correlation calculation formula, the calculation formula being: in, represents the state coefficient, represents the iteration coefficient, , , are fitting coefficients, Indicates the first state association.

2. The data analysis and processing method of the energy storage energy-saving system according to claim 1 is characterized in that: The method further comprises: The first state correlation is corrected by using a correction function to obtain a second state correlation, wherein the correction function includes: when When , the correction function includes: when When , the correction function includes: in, Indicates the preset threshold value, , Both represent the correction value, that is, the second state association degree, represents a constant, represents the correction factor, Indicates the value of the running state change; The second state correlation is determined as the state correlation between the energy storage energy-saving system fault information and the corresponding target device operating state information.

3. The data analysis and processing method of the energy storage energy-saving system according to claim 2 is characterized in that: Based on the first characteristic data, constructing a fault prediction model includes: In response to detecting that the state association degree is greater than a first preset value, extracting a plurality of association sets of the first state information and the second state information to generate first feature data; Dividing the first feature data into a training set, a test set and a validation set according to a preset ratio; Based on the training set, test set and validation set, respectively, a preset fault prediction model is trained, tested and validated; When the accuracy of the preset fault prediction model is greater than the second preset value, the training is completed and a final fault prediction model is obtained, including: in, represents the predicted value, represents the adjustment function, Indicates the number of devices. represents the correction factor, is the coupling coefficient, represents the weighted sum of the real-time running status information changes, Indicates the change in real-time operating status information. Represents the weighted sum of the real-time operating status information of the target device.

4. The data analysis and processing method of the energy storage energy-saving system according to claim 3 is characterized in that: The output result is a predicted value D, the second characteristic data is input into the fault prediction model, and real-time warning push of the fault risk is performed according to the output result, including: In response to detecting that the predicted value D is greater than the third preset value, early warning information is pushed to the terminal for real-time early warning.

Images