Pumped storage equipment data processing method and device, computer equipment, storage medium and computer program product

Abstract

The invention relates to a pumped storage equipment data processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring original data corresponding to sample pumped storage equipment; constructing a metadata model corresponding to each piece of original data; obtaining element data corresponding to each equipment element, and determining a metadata model corresponding to each equipment element from the metadata models; according to the element data corresponding to each equipment element and the metadata model, constructing an element data model corresponding to each equipment element; according to the element data model, constructing an equipment data model corresponding to the pumped storage equipment to be analyzed; configuring a data entry rule corresponding to the equipment data model; generating a target form corresponding to the equipment data model according to the data entry rule and to-be-entered data of the to-be-analyzed pumped storage equipment; and inputting the to-be-input data into the target form. By adopting the method, the data processing efficiency of the pumped storage equipment can be improved.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a method and device for processing data of a pumped storage device, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] In the power system, in order to ensure the safe and reliable operation of pumped storage equipment, it is crucial to quickly process the data of pumped storage equipment.

[0003] In traditional technology, manual processing is generally used in the process of processing pumped storage equipment data; however, this manual processing method is cumbersome and requires a lot of time and manpower, resulting in low processing efficiency of pumped storage equipment data. Summary of the invention

[0004] Based on this, it is necessary to provide a pumped-storage equipment data processing method, device, computer equipment, computer-readable storage medium and computer program product that can improve the processing efficiency of pumped-storage equipment data in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for processing data of a pumped storage device, comprising:

[0006] Acquire original data corresponding to the sample pumped-storage equipment; the original data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment;

[0007] Constructing a metadata model corresponding to each of the original data;

[0008] Acquire component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model;

[0009] Constructing a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component;

[0010] According to the component data model, construct an equipment data model corresponding to the pumped storage equipment to be analyzed;

[0011] Configuring data entry rules corresponding to the device data model;

[0012] Generate a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed;

[0013] Enter the data to be entered into the target form.

[0014] In one embodiment, constructing a metadata model corresponding to each of the original data includes:

[0015] Identify the target data type corresponding to each of the original data;

[0016] According to the target data type corresponding to each of the original data, query the corresponding relationship between the data type and the model architecture to obtain the target model architecture corresponding to each of the original data;

[0017] According to the target model architecture corresponding to each of the original data, a metadata model corresponding to each of the original data is constructed.

[0018] In one embodiment, constructing a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component includes:

[0019] Inputting the component data corresponding to each device component into the trained importance prediction model to obtain the predicted importance of the component data corresponding to each device component;

[0020] Filtering out the component data whose predicted importance is greater than the preset importance from the component data corresponding to each of the equipment components as the key component data corresponding to each of the equipment components;

[0021] A component data model corresponding to each device component is constructed based on the key component data and metadata model corresponding to each device component.

[0022] In one embodiment, constructing a device data model corresponding to the pumped-storage device to be analyzed based on the component data model includes:

[0023] receiving a model configuration instruction for the pumped storage equipment to be analyzed sent by a terminal;

[0024] Extracting model identification information from the model configuration instruction;

[0025] Filtering out a component data model corresponding to the model identification information from each of the component data models as a target component data model;

[0026] According to the target component data model, a device data model corresponding to the power device to be analyzed is constructed.

[0027] In one embodiment, configuring the data entry rules corresponding to the device data model includes:

[0028] Get multiple preset project information;

[0029] Determining the correlation between each preset item information and the device data model;

[0030] From each of the preset project information, select the preset project information whose correlation degree with the device data model is greater than a preset correlation degree as the correlation project information corresponding to the device data model;

[0031] Configuring a data entry format corresponding to the device data model;

[0032] The data entry format and the associated project information are both used as the data entry rules.

[0033] In one embodiment, generating a target form corresponding to the equipment data model according to the data entry rule and the data to be entered of the pumped-storage equipment to be analyzed includes:

[0034] Acquire current operating condition information corresponding to the data to be entered for the pumped-storage equipment to be analyzed;

[0035] Determining a target preprocessing method corresponding to the data to be input according to the current working condition information;

[0036] Preprocessing the data to be entered according to the target preprocessing method to obtain preprocessed data to be entered;

[0037] According to the data entry rules and the pre-processed data to be entered, a target form corresponding to the device data model is generated.

[0038] In a second aspect, the present application also provides a pumped storage equipment data processing device, comprising:

[0039] A data acquisition module, used to acquire raw data corresponding to the sample pumped-storage equipment; the raw data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment;

[0040] A first construction module is used to construct a metadata model corresponding to each of the original data;

[0041] A model determination module, used to obtain component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model;

[0042] A second construction module, configured to construct a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component;

[0043] A third construction module is used to construct a device data model corresponding to the pumped-storage device to be analyzed according to the component data model;

[0044] A rule configuration module, used to configure data entry rules corresponding to the device data model;

[0045] A form generation module, used for generating a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed;

[0046] The data entry module is used to enter the data to be entered into the target form.

[0047] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0048] Acquire original data corresponding to the sample pumped-storage equipment; the original data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment;

[0049] Constructing a metadata model corresponding to each of the original data;

[0050] Acquire component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model;

[0051] Constructing a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component;

[0052] According to the component data model, construct an equipment data model corresponding to the pumped storage equipment to be analyzed;

[0053] Configuring data entry rules corresponding to the device data model;

[0054] Generate a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed;

[0055] Enter the data to be entered into the target form.

[0056] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0057] Acquire original data corresponding to the sample pumped-storage equipment; the original data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment;

[0058] Constructing a metadata model corresponding to each of the original data;

[0059] Acquire component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model;

[0060] Constructing a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component;

[0061] According to the component data model, construct an equipment data model corresponding to the pumped storage equipment to be analyzed;

[0062] Configuring data entry rules corresponding to the device data model;

[0063] Generate a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed;

[0064] Enter the data to be entered into the target form.

[0065] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0066] Acquire original data corresponding to the sample pumped-storage equipment; the original data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment;

[0067] Constructing a metadata model corresponding to each of the original data;

[0068] Acquire component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model;

[0069] Constructing a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component;

[0070] According to the component data model, construct an equipment data model corresponding to the pumped storage equipment to be analyzed;

[0071] Configuring data entry rules corresponding to the device data model;

[0072] Generate a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed;

[0073] Enter the data to be entered into the target form.

[0074] The above-mentioned pumped-storage equipment data processing method, device, computer equipment, storage medium and computer program product first obtain the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment as raw data, and construct a metadata model corresponding to each raw data, then obtain the component data corresponding to each equipment component, and determine the metadata model corresponding to each equipment component from the metadata model, then, according to the component data and metadata model corresponding to each equipment component, construct a component data model corresponding to each equipment component, and according to the component data model, construct an equipment data model corresponding to the pumped-storage equipment to be analyzed, then, configure the data entry rules corresponding to the equipment data model, and generate the target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed, and finally enter the data to be entered into the target form. In this way, in the process of processing the pumped-storage equipment data, after obtaining the original data corresponding to the sample pumped-storage equipment, the metadata model corresponding to each original data can be quickly constructed, and through a series of data analysis, the equipment data model corresponding to the pumped-storage equipment to be analyzed can be quickly constructed, so that the data entry rules corresponding to the equipment data model can be configured more quickly, and then the target form corresponding to the equipment data model can be generated more quickly, which is conducive to improving the entry efficiency of the data to be entered for the pumped-storage equipment to be analyzed, thereby improving the processing efficiency of the pumped-storage equipment data; moreover, the entire process does not require manual intervention, avoiding the cumbersome manual processing method, which requires a lot of time and manpower, resulting in the defect of low processing efficiency of pumped-storage equipment data, and further improving the processing efficiency of pumped-storage equipment data. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0076] Figure 1 A schematic flow chart of a method for processing data of a pumped storage device in one embodiment;

[0077] Figure 2 A schematic flow chart of a method for processing data of a pumped storage device in another embodiment;

[0078] Figure 3 is a flow chart of a data processing method based on a device data model in one embodiment;

[0079] Figure 4is a schematic diagram of a component data model in one embodiment;

[0080] Figure 5 is a schematic diagram of a data model framework in one embodiment;

[0081] Figure 6 A schematic diagram of a component data model configuration interface in one embodiment;

[0082] Figure 7 A structural block diagram of a data processing device for pumped storage equipment in one embodiment;

[0083] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0085] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0086] In an exemplary embodiment, Figure 1 As shown, a method for processing data of a pumped storage device is provided. This embodiment uses the method applied to a server as an example for illustration; it can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptops, smart phones and tablet computers; the server can be implemented with an independent server or a server cluster composed of multiple servers. In this embodiment, the method includes the following steps:

[0087] Step S101, obtaining original data corresponding to the sample pumped-storage equipment; the original data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment.

[0088] The sample pumped-storage equipment refers to the pumped-storage equipment corresponding to the metadata model.

[0089] The original data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped storage equipment.

[0090] The measurement data is used to represent the data obtained by real-time measurement of the sample pumped storage equipment during operation, such as contact status, coil voltage, etc.

[0091] Among them, equipment technical parameters are used to indicate the technical indicators that reflect the basic performance and specifications of the equipment determined during the design and manufacturing process of the sample pumped-storage equipment, such as rated parameters, setting parameters, etc.

[0092] Among them, the maintenance test data is used to represent the data obtained by testing the sample pumped storage equipment, such as the operating voltage and the return voltage.

[0093] Exemplarily, the server obtains the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment from the database; then, the server combines the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment according to a preset combination method to obtain the original data corresponding to the sample pumped-storage equipment.

[0094] Step S102: construct a metadata model corresponding to each original data.

[0095] Among them, the metadata model is used to represent the abstract model that describes the key characteristics of the original data such as the structure and content, including the measurement data model, equipment technical parameter model and maintenance test data model.

[0096] Exemplarily, the server constructs a measurement data model corresponding to the measurement data, constructs an equipment technical parameter model corresponding to the equipment technical parameters, and constructs a maintenance test data model corresponding to the maintenance test data; then, the server uses the measurement data model, the equipment technical parameter model and the maintenance test data model as metadata models corresponding to each original data.

[0097] Step S103, acquiring component data corresponding to each device component, and determining the metadata model corresponding to each device component from the metadata model.

[0098] Among them, equipment components refer to the basic units that constitute the sample pumped-storage equipment, such as relays, valves, motors, switches, etc.

[0099] Among them, component data refers to various data related to device components.

[0100] Exemplarily, the server determines multiple equipment components corresponding to the sample pumped-storage equipment; then, the server obtains component data corresponding to each equipment component from the database; then, the server determines the component type of each equipment component (such as a mechanical component, a control component, etc.); then, the server determines the metadata model corresponding to the component type of each equipment element from the metadata model as the metadata model corresponding to each equipment element.

[0101] Step S104: construct a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component.

[0102] The component data model refers to an abstract model used to structure component data.

[0103] Exemplarily, the server constructs a component data model corresponding to the component data and metadata model of each device component according to the component data and metadata model corresponding to each device component as the component data model corresponding to each device component.

[0104] Step S105: constructing a device data model corresponding to the pumped-storage device to be analyzed according to the component data model.

[0105] The pumped-storage equipment to be analyzed refers to the pumped-storage equipment that needs to be analyzed.

[0106] The equipment data model refers to an abstract model used to structuredly represent the pumped storage equipment to be analyzed.

[0107] Exemplarily, the server receives a model configuration instruction for the pumped-storage equipment to be analyzed sent by the terminal; then, the server selects the component data model corresponding to the model configuration instruction from each component data model as the target component data model; then, the server constructs the equipment data model corresponding to the pumped-storage equipment to be analyzed based on the target component data model.

[0108] Step S106: configure data entry rules corresponding to the device data model.

[0109] The data entry rules refer to the data entry format and associated project information corresponding to the equipment data model.

[0110] Exemplarily, the server configures the data entry format and associated project information corresponding to the device data model as data entry rules corresponding to the device data model.

[0111] Step S107, generating a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed.

[0112] The target form refers to the data entry form corresponding to the device data model.

[0113] Exemplarily, the server generates a form corresponding to the data entry rules and the data to be entered for the pumped-storage equipment to be analyzed according to the data entry rules and the data to be entered as a target form corresponding to the equipment data model.

[0114] Step S108, entering the data to be entered into the target form.

[0115] Exemplarily, the server performs an integrity check on the target form and obtains an integrity check result corresponding to the target form; when the integrity check result indicates that the target form passes, the server determines a preset entry method corresponding to the data to be entered; then, the server enters the data to be entered into the target form according to the preset entry method.

[0116] In the above-mentioned pumped-storage equipment data processing method, the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment are first obtained as raw data, and a metadata model corresponding to each raw data is constructed, and then the component data corresponding to each equipment component is obtained, and the metadata model corresponding to each equipment component is determined from the metadata model. Then, according to the component data and metadata model corresponding to each equipment component, a component data model corresponding to each equipment component is constructed, and according to the component data model, an equipment data model corresponding to the pumped-storage equipment to be analyzed is constructed, and then, the data entry rules corresponding to the equipment data model are configured, and according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed, a target form corresponding to the equipment data model is generated, and finally the data to be entered is entered into the target form. In this way, in the process of processing the pumped-storage equipment data, after obtaining the original data corresponding to the sample pumped-storage equipment, the metadata model corresponding to each original data can be quickly constructed, and through a series of data analysis, the equipment data model corresponding to the pumped-storage equipment to be analyzed can be quickly constructed, so that the data entry rules corresponding to the equipment data model can be configured more quickly, and then the target form corresponding to the equipment data model can be generated more quickly, which is conducive to improving the entry efficiency of the data to be entered for the pumped-storage equipment to be analyzed, thereby improving the processing efficiency of the pumped-storage equipment data; moreover, the entire process does not require manual intervention, avoiding the cumbersome manual processing method, which requires a lot of time and manpower, resulting in the defect of low processing efficiency of pumped-storage equipment data, and further improving the processing efficiency of pumped-storage equipment data.

[0117] In an exemplary embodiment, the above step S102 constructs a metadata model corresponding to each raw data, which specifically includes the following contents: identifying the target data type corresponding to each raw data; according to the target data type corresponding to each raw data, querying the correspondence between the data type and the model architecture to obtain the target model architecture corresponding to each raw data; according to the target model architecture corresponding to each raw data, constructing the metadata model corresponding to each raw data.

[0118] The target data type refers to the data type corresponding to the original data.

[0119] The corresponding relationship between the data type and the model architecture is used to represent the association information between the data type and the model architecture. For example, structured data corresponds to the relational model architecture, and unstructured data corresponds to the object model architecture.

[0120] Among them, the target model architecture refers to the model architecture corresponding to the original data.

[0121] Exemplarily, the server inputs each raw data into a trained data type prediction model, and obtains the target data type corresponding to each raw data through the trained data type prediction model; then, the server queries the correspondence between the data type and the model architecture based on the target data type corresponding to each raw data, and obtains the model architecture corresponding to the target data type of each raw data as the target model architecture corresponding to each raw data; then, the server constructs a metadata model corresponding to each raw data according to the target model architecture corresponding to each raw data.

[0122] In this embodiment, by accurately identifying the target data type corresponding to each original data and based on the corresponding relationship, the target model architecture corresponding to each original data can be more accurately obtained, and then the metadata model corresponding to each original data can be more accurately constructed, providing a data foundation for subsequent processing.

[0123] In an exemplary embodiment, the above step S104 constructs a component data model corresponding to each device component based on the component data and metadata model corresponding to each device component, and specifically includes the following contents: inputting the component data corresponding to each device component into the trained importance prediction model to obtain the predicted importance of the component data corresponding to each device component; filtering out the component data with a predicted importance greater than a preset importance from the component data corresponding to each device component as the key component data corresponding to each device component; constructing the component data model corresponding to each device component based on the key component data and metadata model corresponding to each device component.

[0124] The importance prediction model refers to a network model that can use the component data corresponding to each device component to obtain the predicted importance of the component data corresponding to each device component, such as a random forest model.

[0125] Exemplarily, the server inputs the component data corresponding to each device component into the trained importance prediction model, and obtains the predicted importance of the component data corresponding to each device component through the trained importance prediction model; then, the server screens out the component data whose predicted importance is greater than the preset importance from the component data corresponding to each device component, and uses these component data as the key component data corresponding to each device component; then, the server constructs a component data model corresponding to the key component data and the metadata model corresponding to each device component according to the key component data and the metadata model corresponding to each device component, as the component data model corresponding to each device component.

[0126] In this embodiment, key component data is screened out through the importance prediction model, which can effectively reduce the amount of data corresponding to each device component, thereby making the object of data management more focused; moreover, a component data model is constructed based on the key component data, so that the model is more in line with the actual key characteristics and important operating laws of the component, which is conducive to improving the construction accuracy of the component data model.

[0127] In an exemplary embodiment, the above step S105, based on the component data model, constructs a device data model corresponding to the pumped-storage equipment to be analyzed, specifically including the following contents: receiving a model configuration instruction for the pumped-storage equipment to be analyzed sent by the terminal; extracting the model identification information in the model configuration instruction; screening out the component data model corresponding to the model identification information from each component data model as the target component data model; and constructing the device data model corresponding to the pumped-storage equipment to be analyzed based on the target component data model.

[0128] The model configuration instruction is used to represent the instruction information corresponding to the configuration of the equipment data model of the pumped-storage equipment to be analyzed.

[0129] The model identification information refers to identification information corresponding to the component data model, such as a name corresponding to the component data model.

[0130] The target component data model is used to represent the component data model corresponding to the model identification information.

[0131] Exemplarily, the server establishes a network path with the terminal; then, the server receives the model configuration instruction for the pumped-storage equipment to be analyzed sent by the terminal through the network path; then, the server parses the model configuration instruction to obtain the parsing information corresponding to the model configuration instruction; then, the server extracts the model identification information in the parsing information; then, the server selects the component data model corresponding to the model identification information from each component data model, and uses the component data model as the target component data model; then, the server constructs the equipment data model corresponding to the pumped-storage equipment to be analyzed based on the target component data model.

[0132] In this embodiment, by using model configuration instructions, the component data model that is compatible with it can be accurately screened out, and then the equipment data model that meets actual needs can be accurately constructed, which is beneficial to improving the construction accuracy of the equipment data model and avoiding blind model construction, which leads to the defect of low model construction accuracy.

[0133] In an exemplary embodiment, the above step S106 configures the data entry rules corresponding to the device data model, which specifically include the following contents: obtaining multiple preset project information; determining the correlation between each preset project information and the device data model; from each preset project information, screening out the preset project information whose correlation with the device data model is greater than the preset correlation, as the associated project information corresponding to the device data model; configuring the data entry format corresponding to the device data model; and using both the data entry format and the associated project information as data entry rules.

[0134] Among them, the preset project information refers to the current maintenance project information.

[0135] The correlation degree is used to indicate the correlation degree between each preset item information and the device data model.

[0136] The preset correlation degree refers to a preset correlation degree threshold.

[0137] The associated project information refers to preset project information whose association with the device data model is greater than a preset association.

[0138] Among them, the data entry format is used to indicate the rules and structure followed by data entry.

[0139] Exemplarily, the server obtains model information of the device data model; then, the server performs feature extraction processing on each preset project information and model information respectively, and obtains a first feature vector corresponding to each preset project information, and a second feature vector corresponding to the model information; then, the server inputs the first feature vector corresponding to each preset project information, and the second feature vector corresponding to the model information, into the trained correlation prediction model to obtain the correlation between each preset project information and the device data model; then, the server screens out the preset project information whose correlation with the device data model is greater than the preset correlation from each preset project information, and uses these preset project information as the associated project information corresponding to the device data model; then, the server configures the data entry format corresponding to the device data model; then, the server uses both the data entry format and the associated project information as data entry rules.

[0140] In this embodiment, information with a correlation degree greater than a preset correlation degree is screened out as related project information, so that key content closely related to the device data model can be accurately extracted, which is beneficial to improving the accuracy of determining the related project information, and further improving the accuracy of determining the data entry rules, providing a data basis for subsequent data entry.

[0141] In an exemplary embodiment, the above step S107 generates a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed, and specifically includes the following contents: obtaining the current operating condition information corresponding to the data to be entered of the pumped-storage equipment to be analyzed; determining the target preprocessing method corresponding to the data to be entered according to the current operating condition information; preprocessing the data to be entered according to the target preprocessing method to obtain the preprocessed data to be entered; generating the target form corresponding to the equipment data model according to the data entry rules and the preprocessed data to be entered.

[0142] The current operating condition information refers to the operating condition information of the data to be entered at the current time, such as power generation conditions, pumping conditions, etc.

[0143] The target preprocessing method refers to the preprocessing method corresponding to the data to be entered.

[0144] Among them, the preprocessed data to be entered is the preprocessed data to be entered.

[0145] Exemplarily, the server obtains the timestamp corresponding to the data to be entered of the pumped-storage equipment to be analyzed, and determines the current operating condition information corresponding to the data to be entered of the pumped-storage equipment to be analyzed based on the timestamp; then, the server queries the correspondence between the operating condition information and the preprocessing method based on the current operating condition information, and obtains the target preprocessing method corresponding to the data to be entered; for example, when the current operating condition information is the power generation condition, the target preprocessing method corresponding to the data to be entered is filtering processing; when the current operating condition information is the pumping condition, the target preprocessing method corresponding to the data to be entered is data smoothing processing; then, the server preprocesses the data to be entered according to the target preprocessing method to obtain the preprocessed data to be entered; then, the server generates a form corresponding to the data entry rules and the preprocessed data to be entered based on the data entry rules and the preprocessed data to be entered, as the target form corresponding to the equipment data model.

[0146] In this embodiment, by obtaining the current operating condition information and determining the target preprocessing method accordingly, it is possible to perform special processing on the characteristics of the data to be entered under different operating conditions of the pumped storage equipment, thereby greatly improving the accuracy and reliability of the data and providing a high-quality data foundation for subsequent data analysis.

[0147] In an exemplary embodiment, Figure 2 As shown, another method for processing data of pumped storage equipment is provided, which is described by taking the method applied to a server as an example, and includes the following steps:

[0148] Step S201, obtaining original data corresponding to the sample pumped-storage equipment; the original data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment.

[0149] Step S202, identifying the target data type corresponding to each raw data; according to the target data type corresponding to each raw data, querying the correspondence between the data type and the model architecture, and obtaining the target model architecture corresponding to each raw data; according to the target model architecture corresponding to each raw data, constructing the metadata model corresponding to each raw data.

[0150] Step S203, acquiring component data corresponding to each device component, and determining the metadata model corresponding to each device component from the metadata model.

[0151] Step S204 , inputting the component data corresponding to each device component into the trained importance prediction model to obtain the predicted importance of the component data corresponding to each device component.

[0152] Step S205 , selecting component data with a predicted importance greater than a preset importance from the component data corresponding to each device component as key component data corresponding to each device component.

[0153] Step S206: construct a component data model corresponding to each device component according to the key component data and metadata model corresponding to each device component.

[0154] Step S207, receiving a model configuration instruction for the pumped-storage equipment to be analyzed sent by the terminal; extracting model identification information in the model configuration instruction.

[0155] Step S208, selecting a component data model corresponding to the model identification information from each component data model as a target component data model; and constructing a device data model corresponding to the pumped-storage device to be analyzed according to the target component data model.

[0156] Step S209, obtain multiple preset project information; determine the correlation between each preset project information and the device data model; from each preset project information, filter out the preset project information whose correlation with the device data model is greater than the preset correlation as the associated project information corresponding to the device data model.

[0157] Step S210, configuring the data entry format corresponding to the device data model; using the data entry format and associated project information as data entry rules.

[0158] Step S211, obtaining current operating condition information corresponding to the data to be entered of the pumped-storage equipment to be analyzed; and determining a target preprocessing method corresponding to the data to be entered according to the current operating condition information.

[0159] Step S212, preprocessing the data to be entered according to the target preprocessing method to obtain the preprocessed data to be entered; generating a target form corresponding to the device data model according to the data entry rules and the preprocessed data to be entered.

[0160] Step S213, entering the data to be entered into the target form.

[0161] In the above-mentioned pumped-storage equipment data processing method, in the process of processing the pumped-storage equipment data, after obtaining the original data corresponding to the sample pumped-storage equipment, the metadata model corresponding to each original data can be quickly constructed, and through a series of data analysis, the equipment data model corresponding to the pumped-storage equipment to be analyzed can be quickly constructed, so that the data entry rules corresponding to the equipment data model can be configured more quickly, and then the target form corresponding to the equipment data model can be generated more quickly, which is conducive to improving the entry efficiency of the data to be entered for the pumped-storage equipment to be analyzed, thereby improving the processing efficiency of the pumped-storage equipment data; moreover, the whole process does not require manual intervention, avoiding the defect that the manual processing method is cumbersome, requires a lot of time and manpower, and leads to low processing efficiency of the pumped-storage equipment data, and further improves the processing efficiency of the pumped-storage equipment data.

[0162] In an exemplary embodiment, in order to more clearly illustrate the pumped storage equipment data processing method provided in the embodiment of the present application, the pumped storage equipment data processing method is specifically described below with a specific embodiment. In one embodiment, Figure 3 As shown, the present application also provides a data processing method based on the equipment data model. In the process of processing the pumped-storage equipment data, the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment are first obtained as the original data, and the metadata model corresponding to each original data is constructed. Then, the component data corresponding to each equipment component is obtained, and the metadata model corresponding to each equipment component is determined from the metadata model. Then, according to the component data and metadata model corresponding to each equipment component, the component data model corresponding to each equipment component is constructed, and according to the component data model, the equipment data model corresponding to the pumped-storage equipment to be analyzed is constructed. Then, the data entry rules corresponding to the equipment data model are configured, and according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed, the target form corresponding to the equipment data model is generated, and finally the data to be entered is entered into the target form. Specifically including the following contents:

[0163] 1. Obtain the original data of equipment (such as pumped storage equipment); the original data includes: measurement data, equipment technical parameters, and maintenance test data.

[0164] 2. Based on the original data, build the corresponding metadata model; the metadata model includes four types of models: measurement data, equipment technical parameters, maintenance test data, and algorithm indicator data.

[0165] 3. Obtain the data of equipment components, and use the model tool to generate corresponding component data models (such as relays, valves, motors, switches and other specific categories of components) based on the data of equipment components and metadata models. Figure 4For example, in the component data model, you can customize the directory tree and associate multiple metadata models.

[0166] Among them, the construction method of the component data model is as follows Figure 5 As shown, the specific steps include:

[0167] (1) Construct a metadata model. For example, construct a corresponding metadata model based on measurement data, equipment technical parameters, and maintenance test data.

[0168] (2) According to the metadata model, associate the corresponding data configuration.

[0169] (3) Generate the corresponding component data model (such as relays, valves, motors, switches and other specific categories of components) based on the associated data configuration. In the component data model, you can customize the directory tree and associate multiple metadata models.

[0170] (4) Users can also configure the component data model. The specific contents are as follows: Users can manage or create component data models in the component data model management interface; Users can configure the directory tree and generate a tree structure in the component data model configuration interface; Users can select any node in the directory tree to manage component data model information, such as Figure 6 shown.

[0171] It should be noted that there are other functions in the configuration interface: support for copying and deleting multiple configurations; to create a new directory tree, the corresponding node should be selected to operate, and support the creation of nodes at the same level and sub-nodes; support the export of excel tables in xlsx format, support the import of xls and xlsx formats, including the field information of the directory tree, and the export function includes all the information under the directory tree; the "!" mark should be added to the "name" field, and the name naming specification will be displayed when the mouse is hovered; after the user selects a single directory tree, the font of the corresponding node should be colored, and all configuration information of the node and its subordinate nodes will be displayed in the data configuration. Global information can be displayed by deselecting nodes (same as the directory tree design of the algorithm center); support the scaling and expansion of the directory tree; support the copying of the entire component data model.

[0172] 4. Obtain a standard device tree (measurement device tree) and build a specific device data model based on the standard device tree (measurement device tree); or, support the introduction of component data models, identify and configure differentiated content, and generate corresponding device data models through component data models. For example, the device data model can import multiple component data models and manually perform differentiated configuration.

[0173] Among them, the construction method of the device data model is as follows Figure 5 As shown, the specific steps include:

[0174] (1) Obtain the standard device tree (measurement device tree).

[0175] (2) Obtain component data model.

[0176] (3) Based on the standard device tree (measurement device tree) and component data model, a specific device data model is constructed. For example, it supports the introduction of component data models, identification and configuration of differentiated content, and the generation of corresponding device data models through component data models. For example, a device data model can import multiple component data models and manually perform differentiated configuration.

[0177] (4) Based on the equipment data model, create a new data entry configuration, formulate the data format and associated metadata model for offline data entry, and configure the maintenance project to which the form belongs; for example, users can manage and create new maintenance test data entry tasks in the maintenance test data entry management interface, and each task corresponds to a specific maintenance project; after the maintenance test data entry task is saved, the maintenance details and data tables associated with the maintenance project, as well as their associated equipment models, are automatically generated.

[0178] (5) Users can also perform relevant configurations on the device data model. The specific contents are as follows: Users can manage or create new device data models in the device data model management interface; Users can configure the directory tree and generate a tree structure in the device data model configuration interface to support the import of component data models; Users can configure data and associate data models based on the structure and data standards of the actual equipment.

[0179] It should be noted that there are other functions in the configuration interface: the basic function reuses the component data model configuration; the data model and the corresponding plant and unit instances can be automatically written into the metadata model through the "Synchronize Metadata Model" module, and only two types of equipment technical parameters and maintenance test data need to be supported; the "Maintenance Test Data Entry Configuration" button is used to switch to the maintenance test data entry configuration interface, and the equipment directory tree remains unchanged after switching; a single component data model can be associated and introduced at any node in the directory tree through the "Introduce Component Model" button, and the system automatically adds the directory tree structure of the component data model to the corresponding node, inheriting the original node structure and content.

[0180] (6) When configuring the device data model, if the user needs to manually configure all metadata models contained in the device, it will result in low work efficiency. It is necessary to use automated means to efficiently generate metadata models.

[0181] The user clicks "Synchronize Model" (Synchronize Metadata Model) in the data model configuration to trigger the synchronization algorithm. The main logic is as follows:

[0182] Determine the synchronization scope: Initialize the synchronization list, traverse all equipment data model configurations, if the "Type" is "Equipment Technical Parameters" or "Maintenance Test Data", and the "Meta Model Code" is empty, then record its model type, name, unit, and remarks information, and add it to the synchronization list.

[0183] Automatic configuration of metamodel data: Access different model service interfaces according to model types. Interface requirements: Input: List<code, name, unit, remarks>; Logic: Automatically create a corresponding data model in the database, and fill in the required fields according to the system time. No instance needs to be added. Return the model ID (identity) matching code.

[0184] Supplement related information: Supplement the "Metamodel Code" field based on the information returned by the model service.

[0185] 5. Formulate the format for maintenance test data entry based on the equipment data model, and associate it with maintenance projects to form data entry configuration rules. For example, adaptively generate entry forms based on data modeling results, and support multiple data entry and import methods. For example, based on the equipment data model, create a new data entry configuration, formulate the data format and associated metadata model for offline data entry, and configure the maintenance project to which the form belongs.

[0186] 6. When users need to enter data, the program can automatically generate relevant forms according to the data entry configuration rules and intelligently write data. Of course, users can also use the device data model service provided by the device data model to query historical data curves, charts, and algorithm configuration.

[0187] In the above-mentioned embodiment, in the process of processing the pumped-storage equipment data, after obtaining the original data corresponding to the sample pumped-storage equipment, the metadata model corresponding to each original data can be quickly constructed, and through a series of data analysis, the equipment data model corresponding to the pumped-storage equipment to be analyzed can be quickly constructed, so that the data entry rules corresponding to the equipment data model can be configured more quickly, and then the target form corresponding to the equipment data model can be generated more quickly, which is conducive to improving the entry efficiency of the data to be entered for the pumped-storage equipment to be analyzed, and then the processing efficiency of the pumped-storage equipment data is improved; moreover, the whole process does not require manual intervention, avoiding the defect that the manual processing method is cumbersome, requires a lot of time and manpower, and leads to low processing efficiency of the pumped-storage equipment data, and further improves the processing efficiency of the pumped-storage equipment data.

[0188] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0189] Based on the same inventive concept, the embodiment of the present application also provides a pumped-storage equipment data processing device for implementing the above-mentioned pumped-storage equipment data processing method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the pumped-storage equipment data processing device provided below can refer to the above-mentioned limitations on the pumped-storage equipment data processing method, and will not be repeated here.

[0190] In an exemplary embodiment, Figure 7 As shown, a pumped storage equipment data processing device is provided, comprising: a data acquisition module 701, a first construction module 702, a model determination module 703, a second construction module 704, a third construction module 705, a rule configuration module 706, a form generation module 707 and a data entry module 708, wherein:

[0191] The data acquisition module 701 is used to acquire the original data corresponding to the sample pumped-storage equipment; the original data at least includes the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment.

[0192] The first construction module 702 is used to construct a metadata model corresponding to each original data.

[0193] The model determination module 703 is used to obtain component data corresponding to each device component and determine the metadata model corresponding to each device component from the metadata model.

[0194] The second construction module 704 is used to construct a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component.

[0195] The third construction module 705 is used to construct a device data model corresponding to the pumped-storage device to be analyzed according to the component data model.

[0196] The rule configuration module 706 is used to configure the data entry rules corresponding to the device data model.

[0197] The form generation module 707 is used to generate a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped storage equipment to be analyzed.

[0198] The data entry module 708 is used to enter the data to be entered into the target form.

[0199] In an exemplary embodiment, the first construction module 702 is also used to identify the target data type corresponding to each raw data; according to the target data type corresponding to each raw data, query the correspondence between the data type and the model architecture to obtain the target model architecture corresponding to each raw data; and construct the metadata model corresponding to each raw data according to the target model architecture corresponding to each raw data.

[0200] In an exemplary embodiment, the second construction module 704 is also used to input the component data corresponding to each device component into the trained importance prediction model to obtain the predicted importance of the component data corresponding to each device component; filter out the component data with a predicted importance greater than a preset importance from the component data corresponding to each device component as the key component data corresponding to each device component; and construct a component data model corresponding to each device component based on the key component data and metadata model corresponding to each device component.

[0201] In an exemplary embodiment, the third construction module 705 is also used to receive a model configuration instruction for the pumped-storage equipment to be analyzed sent by the terminal; extract the model identification information in the model configuration instruction; filter out the component data model corresponding to the model identification information from each component data model as the target component data model; and construct the equipment data model corresponding to the pumped-storage equipment to be analyzed based on the target component data model.

[0202] In an exemplary embodiment, the rule configuration module 706 is also used to obtain multiple preset project information; determine the correlation between each preset project information and the device data model; filter out the preset project information whose correlation with the device data model is greater than the preset correlation from each preset project information, as the associated project information corresponding to the device data model; configure the data entry format corresponding to the device data model; and use the data entry format and the associated project information as data entry rules.

[0203] In an exemplary embodiment, the form generation module 707 is also used to obtain current operating condition information corresponding to the data to be entered of the pumped-storage equipment to be analyzed; determine the target preprocessing method corresponding to the data to be entered based on the current operating condition information; preprocess the data to be entered according to the target preprocessing method to obtain the preprocessed data to be entered; generate a target form corresponding to the equipment data model based on the data entry rules and the preprocessed data to be entered.

[0204] Each module in the above-mentioned pumped storage equipment data processing device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned 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 in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0205] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store measurement data, equipment technical parameters and maintenance test data, etc. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication 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 method for processing data of a pumped storage device is implemented.

[0206] Those skilled in the art will understand that Figure 8 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.

[0207] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0208] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0209] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0210] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0211] 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.

[0212] The above-described 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 present application. 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 present application shall be subject to the attached claims.

Claims

1. A method for processing data of a pumped storage device, characterized in that: The method comprises: Acquire original data corresponding to the sample pumped-storage equipment; the original data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment; Constructing a metadata model corresponding to each of the original data; Acquire component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model; Constructing a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component; According to the component data model, construct an equipment data model corresponding to the pumped storage equipment to be analyzed; Configuring data entry rules corresponding to the device data model; Generate a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed; Enter the data to be entered into the target form.

2. The method according to claim 1, characterized in that The step of constructing a metadata model corresponding to each of the original data includes: Identify the target data type corresponding to each of the original data; According to the target data type corresponding to each of the original data, query the corresponding relationship between the data type and the model architecture to obtain the target model architecture corresponding to each of the original data; According to the target model architecture corresponding to each of the original data, a metadata model corresponding to each of the original data is constructed.

3. The method according to claim 1, characterized in that The step of constructing a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component includes: Inputting the component data corresponding to each device component into the trained importance prediction model to obtain the predicted importance of the component data corresponding to each device component; Filtering out the component data whose predicted importance is greater than the preset importance from the component data corresponding to each of the equipment components as the key component data corresponding to each of the equipment components; A component data model corresponding to each device component is constructed based on the key component data and metadata model corresponding to each device component.

4. The method according to claim 1, characterized in that: The device data model corresponding to the pumped-storage device to be analyzed is constructed according to the component data model, including: receiving a model configuration instruction for the pumped storage equipment to be analyzed sent by a terminal; Extracting model identification information from the model configuration instruction; Filtering out a component data model corresponding to the model identification information from each of the component data models as a target component data model; According to the target component data model, an equipment data model corresponding to the pumped-storage equipment to be analyzed is constructed.

5. The method according to claim 1, characterized in that: The configuring of the data entry rules corresponding to the device data model includes: Get multiple preset project information; Determining the correlation between each preset item information and the device data model; From each of the preset project information, select the preset project information whose correlation degree with the device data model is greater than a preset correlation degree as the correlation project information corresponding to the device data model; Configuring a data entry format corresponding to the device data model; The data entry format and the associated project information are both used as the data entry rules.

6. The method according to any one of claims 1 to 5, characterized in that: The step of generating a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed comprises: Acquire current operating condition information corresponding to the data to be entered for the pumped-storage equipment to be analyzed; Determining a target preprocessing method corresponding to the data to be input according to the current working condition information; Preprocessing the data to be entered according to the target preprocessing method to obtain preprocessed data to be entered; According to the data entry rules and the pre-processed data to be entered, a target form corresponding to the device data model is generated.

7. A pumped storage equipment data processing device, characterized in that: The device comprises: A data acquisition module, used to acquire raw data corresponding to the sample pumped-storage equipment; the raw data at least includes measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped-storage equipment; A first construction module is used to construct a metadata model corresponding to each of the original data; A model determination module, used to obtain component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model; A second construction module is used to construct a component data model corresponding to each device component according to the component data and metadata model corresponding to each device component; A third construction module is used to construct a device data model corresponding to the pumped-storage device to be analyzed according to the component data model; A rule configuration module, used to configure data entry rules corresponding to the device data model; A form generation module, used for generating a target form corresponding to the equipment data model according to the data entry rules and the data to be entered of the pumped-storage equipment to be analyzed; The data entry module is used to enter the data to be entered into the target form.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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