Data processing method and apparatus for pumped storage device, and computer device, storage medium and computer program product

Abstract

The present application relates to a data processing method and apparatus for a pumped storage device, and a computer device, a storage medium and a computer program product. The method comprises: acquiring original data corresponding to a sample pumped storage device; constructing metadata models corresponding to the various pieces of original data; acquiring component data corresponding to each device component, and determining, from among the metadata models, a metadata model corresponding to each device component; on the basis of the component data and the metadata model corresponding to each device component, constructing a component data model corresponding to each device component; on the basis of the component data models, constructing a device data model corresponding to a pumped storage device to be analyzed; configuring data entry rules corresponding to the device data model; on the basis of the data entry rules and data to be entered of the pumped storage device to be analyzed, generating a target form corresponding to the device data model; and entering the data to be entered into the target form. By means of the method, the efficiency of processing data of a pumped storage device can be improved.

Description

Pumped storage power station data processing method and device, computer device, storage medium and computer program product TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid, in particular to a pumped storage power station data processing method and device, computer device, computer readable storage medium and computer program product. BACKGROUND

[0002] In the power system, in order to guarantee the safe and reliable operation of pumped storage power station, how to quickly process the data of pumped storage power station is very important.

[0003] In the prior art, in the process of processing the data of pumped storage power station, the artificial processing method is generally used. However, this artificial processing method is relatively cumbersome, and a large amount of time and manpower is needed, resulting in low processing efficiency of pumped storage power station data. SUMMARY

[0004] Therefore, it is necessary to provide a pumped storage power station data processing method and device, computer device, computer readable storage medium and computer program product capable of improving the processing efficiency of pumped storage power station data.

[0005] In a first aspect, the present application provides a pumped storage power station data processing method, comprising:

[0006] Obtaining the original data corresponding to the sample pumped storage power station; the original data at least includes the measurement data, the equipment technical parameter and the maintenance test data corresponding to the sample pumped storage power station;

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

[0008] Obtaining the element data corresponding to each equipment element, and determining the metadata model corresponding to each equipment element from the metadata model;

[0009] According to the element data and the metadata model corresponding to each equipment element, the element data model corresponding to each equipment element is constructed;

[0010] According to the element data model, the equipment data model corresponding to the pumped storage power station to be analyzed is constructed;

[0011] Configuring the data entry rule corresponding to the equipment data model;

[0012] According to the data entry rule and the to-be-entered data of the pumped storage power station to be analyzed, the target form corresponding to the equipment data model is generated;

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

[0014] In one of the embodiments, the constructing the metadata model corresponding to each of the raw data comprises:

[0015] Identifying a target data type corresponding to each of the raw data;

[0016] According to the target data type corresponding to each of the raw data, querying the correspondence between the data type and the model architecture to obtain the target model architecture corresponding to each of the raw data;

[0017] According to the target model architecture corresponding to each of the raw data, constructing the metadata model corresponding to each of the raw data.

[0018] In one of the embodiments, the constructing the element data model corresponding to each of the device elements according to the element data corresponding to each of the device elements and the metadata model comprises:

[0019] Respectively inputting the element data corresponding to each of the device elements into the trained importance prediction model to obtain the predicted importance of the element data corresponding to each of the device elements;

[0020] Respectively filtering the element data whose predicted importance is greater than a preset importance from the element data corresponding to each of the device elements as the key element data corresponding to each of the device elements;

[0021] According to the key element data corresponding to each of the device elements and the metadata model, constructing the element data model corresponding to each of the device elements.

[0022] In one of the embodiments, the constructing the device data model corresponding to the to-be-analyzed pumped storage power plant according to the element data model comprises:

[0023] Receiving a model configuration instruction for the to-be-analyzed pumped storage power plant sent by a terminal;

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

[0025] From each of the element data models, filtering an element data model corresponding to the model identification information as a target element data model;

[0026] According to the target element data model, constructing a device data model corresponding to the to-be-analyzed power plant.

[0027] In one of the embodiments, the configuring the data entry rule corresponding to the device data model comprises:

[0028] acquire a plurality of preset project information;

[0029] determine a correlation degree between each preset project information and the equipment data model;

[0030] from each preset project information, filter out the preset project information with a correlation degree greater than a preset correlation degree between the equipment data model, as the associated project information corresponding to the equipment data model;

[0031] configure the data entry format corresponding to the equipment data model;

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

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

[0034] acquire the current working condition information corresponding to the to-be-entered data of the to-be-analyzed pumped storage equipment;

[0035] determine the target preprocessing mode corresponding to the to-be-entered data according to the current working condition information;

[0036] According to the target preprocessing mode, the to-be-entered data is preprocessed to obtain preprocessed to-be-entered data;

[0037] generate the target form corresponding to the equipment data model according to the data entry rule and the preprocessed to-be-entered data.

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

[0039] a data acquisition module for acquiring original data corresponding to a 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;

[0040] a first construction module for constructing a metadata model corresponding to each original data;

[0041] a model determination module for acquiring element data corresponding to each equipment element, and determining a metadata model corresponding to each equipment element from the metadata model;

[0042] a second construction module for constructing an element data model corresponding to each equipment element according to the element data and the metadata model corresponding to each equipment element;

[0043] a third construction module, configured to construct, according to the element data model, a device data model corresponding to the pumped storage power station to be analyzed;

[0044] a rule configuration module, configured to configure a data entry rule corresponding to the device data model;

[0045] a form generation module, configured to generate a target form corresponding to the device data model according to the data entry rule and to-be-entered data of the pumped storage power station to be analyzed;

[0046] a data entry module, configured to enter the to-be-entered data into the target form.

[0047] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0048] obtaining original data corresponding to a sample pumped storage power station; the original data at least comprises measurement data, device technical parameters and maintenance test data corresponding to the sample pumped storage power station;

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

[0050] obtaining element data corresponding to each device element, and determining a metadata model corresponding to each device element from the metadata model;

[0051] constructing an element data model corresponding to each device element according to the element data and the metadata model corresponding to each device element;

[0052] constructing a device data model corresponding to a pumped storage power station to be analyzed according to the element data model;

[0053] configuring a data entry rule corresponding to the device data model;

[0054] generating a target form corresponding to the device data model according to the data entry rule and to-be-entered data of the pumped storage power station to be analyzed;

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

[0056] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0057] obtaining original data corresponding to a sample pumped storage power station; the original data at least comprises measurement data, device technical parameters and maintenance test data corresponding to the sample pumped storage power station;

[0058] constructing a metadata model corresponding to each of the raw data;

[0059] obtaining element data corresponding to each device element, and determining a metadata model corresponding to each device element from the metadata model;

[0060] constructing an element data model corresponding to each device element according to the element data corresponding to each device element and the metadata model;

[0061] constructing a device data model corresponding to the to-be-analyzed pumped storage power plant according to the element data model;

[0062] configuring a data entry rule corresponding to the device data model;

[0063] generating a target form corresponding to the device data model according to the data entry rule and to-be-entered data of the to-be-analyzed pumped storage power plant;

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

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

[0066] obtaining raw data corresponding to a sample pumped storage power plant; the raw data at least comprising measurement data, device technical parameters and maintenance test data corresponding to the sample pumped storage power plant;

[0067] constructing a metadata model corresponding to each of the raw data;

[0068] obtaining element data corresponding to each device element, and determining a metadata model corresponding to each device element from the metadata model;

[0069] constructing an element data model corresponding to each device element according to the element data corresponding to each device element and the metadata model;

[0070] constructing a device data model corresponding to the to-be-analyzed pumped storage power plant according to the element data model;

[0071] configuring a data entry rule corresponding to the device data model;

[0072] generating a target form corresponding to the device data model according to the data entry rule and to-be-entered data of the to-be-analyzed pumped storage power plant;

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

[0074] The aforementioned data processing method, apparatus, computer equipment, storage medium, and computer program products for pumped storage equipment first acquire 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, they acquire the component data corresponding to each equipment component, and determine the metadata model corresponding to each equipment component from the metadata model. Next, based on the component data and metadata model corresponding to each equipment component, they construct the component data model corresponding to each equipment component, and based on the component data model, they construct the equipment data model corresponding to the pumped storage equipment to be analyzed. Then, they configure the data entry rules corresponding to the equipment data model, and based on the data entry rules and the data to be entered from the pumped storage equipment to be analyzed, they generate the target form corresponding to the equipment data model. Finally, they enter the data to be entered into the target form. In this way, during the data processing of pumped storage equipment, after obtaining the raw data corresponding to the sample pumped storage equipment, a metadata model corresponding to each raw data can be quickly constructed. Through a series of data analyses, an equipment data model corresponding to the pumped storage equipment to be analyzed can be quickly constructed. This allows for faster configuration of data entry rules corresponding to the equipment data model, and further faster generation of the target form corresponding to the equipment data model. This improves the efficiency of data entry for the pumped storage equipment to be analyzed, thereby increasing the processing efficiency of pumped storage equipment data. Moreover, the entire process requires no manual intervention, avoiding the drawbacks of cumbersome manual processing methods that require a lot of time and manpower, resulting in low processing efficiency of pumped storage equipment data, thus further improving the processing efficiency of pumped storage equipment data. Attached Figure Description

[0075] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0076] Figure 1 is a flowchart illustrating a data processing method for a pumped storage device in one embodiment;

[0077] Figure 2 is a flowchart illustrating the data processing method for a pumped storage device in another embodiment;

[0078] Figure 3 is a flowchart illustrating a data processing method based on a device data model in one embodiment;

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

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

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

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

[0083] Figure 8 is an internal structure diagram of a computer device in one embodiment. Detailed Implementation

[0084] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this 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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0086] In an exemplary embodiment, as shown in Figure 1, a data processing method for pumped storage equipment is provided. This embodiment illustrates the application of this method to a server; it is understood that the method can also be applied to a terminal, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, and tablets; the server can be a standalone server or a server cluster composed of multiple servers. In this embodiment, the method includes the following steps:

[0087] Step S101: Obtain the raw data corresponding to the sample pumped storage equipment; the raw data shall include at least the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped storage equipment.

[0088] Among them, the sample pumped storage equipment refers to the pumped storage equipment used to construct the metadata model.

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

[0090] Among them, 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 and coil voltage.

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

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

[0093] For example, the server retrieves the measurement data, equipment technical parameters, and maintenance test data corresponding to the sample pumped storage equipment from the database; then, the server combines and processes 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 the metadata model corresponding to each original data.

[0095] The metadata model is used to represent an abstract model that describes the key characteristics of the original data, such as its structure and content. It includes measurement data models, equipment technical parameter models, and maintenance and test data models.

[0096] For example, the server constructs a measurement data model corresponding to the measurement data, an equipment technical parameter model corresponding to the equipment technical parameters, and 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: Obtain the component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model.

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

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

[0100] For example, the server identifies multiple equipment components corresponding to the sample pumped storage device; then, the server retrieves the component data corresponding to each equipment component from the database; then, the server determines the component type of each equipment component (such as mechanical components, control components, etc.); then, the server determines the metadata model corresponding to the component type of each equipment component from the metadata model, as the metadata model corresponding to each equipment component.

[0101] Step S104: Construct the component data model corresponding to each device component based on the component data and metadata model corresponding to each device component.

[0102] Among them, the component data model refers to the abstract model used to structurally represent component data.

[0103] For example, the server constructs a component data model corresponding to the component data and metadata model of each device component based on the component data and metadata model corresponding to each device component, and uses it as the component data model corresponding to each device component.

[0104] Step S105: Based on the component data model, construct the equipment data model corresponding to the pumped storage equipment to be analyzed.

[0105] Among them, the pumped storage equipment to be analyzed refers to the pumped storage equipment that needs to be analyzed.

[0106] Among them, the equipment data model refers to the abstract model used to structurally represent the pumped storage equipment to be analyzed.

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

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

[0109] Among them, data entry rules refer to the data entry format and associated project information corresponding to the device data model.

[0110] For example, the server configures the data entry format and associated project information corresponding to the device data model, which are both used as the data entry rules for the device data model.

[0111] Step S107: Based on the data entry rules and the data to be entered from the pumped storage equipment to be analyzed, generate the target form corresponding to the equipment data model.

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

[0113] For example, the server generates a form corresponding to the data entry rules and the data to be entered from the pumped storage equipment to be analyzed, based on the data entry rules and the data to be entered, and uses this form as the target form corresponding to the equipment data model.

[0114] Step S108: Enter the data to be entered into the target form.

[0115] For example, the server performs an integrity check on the target form and obtains the integrity check result corresponding to the target form; if the integrity check result indicates that the target form passes, the server determines the 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 data processing method for pumped storage equipment, 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. 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. Next, based on the component data and metadata model corresponding to each equipment component, a component data model corresponding to each equipment component is constructed. Based on the component data model, an 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 based on the data entry rules and the data to be entered from the pumped storage equipment to be analyzed, a target form corresponding to the equipment data model is generated. Finally, the data to be entered is entered into the target form. In this way, during the data processing of pumped storage equipment, after obtaining the raw data corresponding to the sample pumped storage equipment, a metadata model corresponding to each raw data can be quickly constructed. Through a series of data analyses, an equipment data model corresponding to the pumped storage equipment to be analyzed can be quickly constructed. This allows for faster configuration of data entry rules corresponding to the equipment data model, and further faster generation of the target form corresponding to the equipment data model. This improves the efficiency of data entry for the pumped storage equipment to be analyzed, thereby increasing the processing efficiency of pumped storage equipment data. Moreover, the entire process requires no manual intervention, avoiding the drawbacks of cumbersome manual processing methods that require a lot of time and manpower, resulting in low processing efficiency of pumped storage equipment data, thus further improving the processing efficiency of pumped storage equipment data.

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

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

[0119] The correspondence between data types and model architectures represents the relationship between them. For example, structured data corresponds to a relational model architecture, while unstructured data corresponds to an object model architecture.

[0120] The target model architecture refers to the model architecture corresponding to the original data.

[0121] For example, the server inputs each raw data into the 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 data type and model architecture according to 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 the 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 correspondence, the target model architecture corresponding to each original data can be obtained more accurately, and the metadata model corresponding to each original data can be constructed more accurately, providing a data foundation for subsequent processing.

[0123] In an exemplary embodiment, step S104 above, which constructs a component data model for each device component based on the component data and metadata model corresponding to each device component, specifically includes the following: 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; selecting 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 constructing a component data model for each device component based on the key component data and metadata model corresponding to each device component.

[0124] Among them, 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 the random forest model.

[0125] For example, 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 selects the component data with a predicted importance greater than a 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 metadata model corresponding to each device component based on the key component data and metadata model corresponding to each device component, and uses it as the component data model corresponding to each device component.

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

[0127] In an exemplary embodiment, step S105 above, which constructs a device data model corresponding to the pumped storage device to be analyzed based on the component data model, specifically includes the following: receiving a model configuration instruction sent by the terminal for the pumped storage device to be analyzed; extracting the model identification information from the model configuration instruction; selecting 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 device to be analyzed based on the target component data model.

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

[0129] Among them, model identification information refers to the identification information corresponding to the component data model, such as the name of 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] For example, the server establishes a network path with the terminal; then, the server receives a model configuration instruction for the pumped storage equipment to be analyzed from 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 from the parsing information; then, the server selects the component data model corresponding to the model identification information from the component data models and uses the component data model as the target component data model; finally, 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 selected, and then the equipment data model that meets the actual needs can be accurately constructed. This helps to improve the accuracy of the equipment data model construction and avoids the defect of blindly constructing the model, which leads to low accuracy.

[0133] In an exemplary embodiment, step S106 above, configuring the data entry rules corresponding to the device data model, specifically includes the following: obtaining multiple preset item information; determining the correlation between each preset item information and the device data model; selecting preset item information from each preset item information whose correlation with the device data model is greater than the preset correlation, and using it as the associated item 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 item information as data entry rules.

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

[0135] Among them, the correlation degree is used to represent the degree of correlation between each preset project information and the equipment data model.

[0136] Among them, the preset correlation degree refers to the correlation degree threshold set in advance.

[0137] Among them, associated project information refers to preset project information whose correlation with the equipment data model is greater than the preset correlation.

[0138] The data entry format is used to indicate the rules and structure followed in data entry.

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

[0140] In this embodiment, information with a correlation greater than a preset correlation is selected as related project information. This allows for the accurate extraction of key content closely related to the device data model, which helps improve the accuracy of determining related project information and, consequently, the accuracy of determining data entry rules, thus providing a data foundation for subsequent data entry.

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

[0142] Among them, 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 condition, water pumping condition, etc.

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

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

[0145] For example, the server obtains the timestamp corresponding to the data to be entered from the pumped storage equipment to be analyzed, and determines the current operating condition information corresponding to the data to be entered 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 to obtain the target preprocessing method corresponding to the data to be entered. For example, if the current operating condition is power generation, the target preprocessing method corresponding to the data to be entered is filtering; if the current operating condition is pumping, the target preprocessing method corresponding to the data to be entered is data smoothing. 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, which serves as the target form corresponding to the equipment data model.

[0146] In this embodiment, by acquiring current operating condition information and determining the target preprocessing method accordingly, it is possible to perform specialized 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, as shown in Figure 2, another data processing method for pumped storage equipment is provided. Taking the application of this method to a server as an example, it includes the following steps:

[0148] Step S201: Obtain the raw data corresponding to the sample pumped storage equipment; the raw data shall include at least the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped storage equipment.

[0149] Step S202: Identify the target data type corresponding to each original data; based on the target data type corresponding to each original data, query the correspondence between the data type and the model architecture to obtain the target model architecture corresponding to each original data; construct the metadata model corresponding to each original data according to the target model architecture corresponding to each original data.

[0150] Step S203: Obtain the component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model.

[0151] Step S204: 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.

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

[0153] Step S206: Construct a component data model for each device component based on the key component data and metadata model corresponding to each device component.

[0154] Step S207: Receive the model configuration instruction sent by the terminal for the pumped storage equipment to be analyzed; extract the model identification information from the model configuration instruction.

[0155] Step S208: Select the component data model corresponding to the model identification information from the component data models and use it as the target component data model; construct the equipment data model corresponding to the pumped storage equipment to be analyzed based on 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, and use it as the associated project information corresponding to the device data model.

[0157] Step S210: Configure the data entry format corresponding to the device data model; use the data entry format and associated project information as data entry rules.

[0158] Step S211: Obtain the current operating condition information corresponding to the data to be entered from 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.

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

[0160] Step S213: Enter the data to be entered into the target form.

[0161] In the aforementioned data processing method for pumped storage equipment, after acquiring the original data corresponding to the sample pumped storage equipment, a metadata model corresponding to each original data can be quickly constructed. Through a series of data analyses, a device data model corresponding to the pumped storage equipment to be analyzed can be quickly constructed. This allows for faster configuration of data entry rules corresponding to the device data model, and further faster generation of the target form corresponding to the device data model. This improves the data entry efficiency of the pumped storage equipment to be analyzed, thereby increasing the data processing efficiency. Moreover, the entire process requires no manual intervention, avoiding the drawbacks of cumbersome manual processing methods that require significant time and manpower, resulting in low data processing efficiency. This further improves the overall data processing efficiency of pumped storage equipment.

[0162] In an exemplary embodiment, to more clearly illustrate the data processing method for pumped storage equipment provided in this application, the following specific embodiment will be used to describe the data processing method for pumped storage equipment. In one embodiment, as shown in FIG3, this application also provides a data processing method based on an equipment data model. In the process of processing pumped storage equipment data, firstly, the measurement data, equipment technical parameters, and maintenance test data corresponding to the sample pumped storage equipment are obtained as raw data, and a metadata model corresponding to each raw 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. Next, based on the component data and metadata model corresponding to each equipment component, a component data model corresponding to each equipment component is constructed. Based on the component data model, an 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 based on the data entry rules and the data to be entered from the pumped storage equipment to be analyzed, a target form corresponding to the equipment data model is generated. Finally, the data to be entered is entered into the target form. Specifically, the following content is included:

[0163] 1. Obtain raw data from equipment (such as pumped storage equipment); raw data includes: measurement data, equipment technical parameters, and maintenance and testing data.

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

[0165] 3. Acquire the data of equipment components, and use modeling tools to generate corresponding component data models (such as relays, valves, motors, switches, and other specific categories of components) based on the equipment component data and metadata models, as shown in Figure 4. For example, the component data model can have a custom directory tree and be associated with multiple metadata models.

[0166] The method for constructing the component data model is shown in Figure 5, and specifically includes the following steps:

[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) Associate the corresponding data configuration based on the metadata model.

[0169] (3) Generate corresponding component data models (such as relays, valves, motors, switches, and other specific types of components) based on the associated data configuration. Among them, the component data model 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, as shown in Figure 6.

[0171] It should be noted that the configuration interface also includes other functions: support for copying and deleting multi-select configurations; creation of a new directory tree requires selecting the corresponding node, supporting creation of sibling nodes and child nodes; export to Excel (.xlsx) format and import to both xls and xlsx formats, including the directory tree's field information; the export function includes all information under the directory tree; an exclamation mark "!" should be added to the "Name" field, and the naming convention should be displayed when the mouse hovers over it; after a user selects a single node in the directory tree, the font of the corresponding node should change color, and the data configuration should display all configuration information of that node and its subordinate nodes. Global information can be displayed by inverting node selection (same as the algorithm platform's directory tree design); scaling and expanding of the directory tree are supported; copying the entire component data model is supported.

[0172] 4. Obtain a standard device tree (measurement device tree) and construct a specific device data model based on it; alternatively, support the import of component data models, identify and configure differentiated content, and generate corresponding device data models through the component data models. For example, multiple component data models can be imported into the device data model, and manual differentiation configuration can be performed.

[0173] The method for constructing the equipment data model is shown in Figure 5, and specifically includes the following steps:

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

[0175] (2) Obtain the component data model.

[0176] (3) Construct specific equipment data models based on standard equipment trees (measurement equipment trees) and component data models. For example, it supports the import of component data models, the identification and configuration of differentiated content, and the generation of corresponding equipment data models through component data models. For example, multiple component data models can be imported into the equipment data model, and manual differentiation configuration can be performed.

[0177] (4) Based on the equipment data model, create a new data entry configuration, define the data format and associated metadata model for offline data entry, and configure the maintenance project to which the form belongs; for example, in the maintenance test data entry management interface, users can manage and create maintenance test data entry tasks, 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 the equipment model associated with them, are automatically generated.

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

[0179] It should be noted that the configuration interface also includes other functions: basic functions reuse component data model configuration; through the "Synchronize Metadata Model" module, data models and corresponding plant and unit instances can be automatically written to the metadata model, supporting only two types: equipment technical parameters and maintenance test data; 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; at any node in the directory tree, a single component data model can be imported by using the "Import Component Model" button, and the system will automatically add 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 users have to manually configure all the metadata models contained in the device, it will result in low work efficiency. Automated methods should be used to generate metadata models efficiently.

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

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

[0183] Automated configuration of metamodel data: Access different model service interfaces based on model type. Interface requirements: Input: List<code, name, unit, remarks>; Logic: Automatically create the corresponding data model in the database, supplementing required fields according to the system time, without adding any instances. Return the model ID (identity, identity information) matched with the code.

[0184] Supplement related information: Based on the information returned by the model service, supplement the "metamodel encoding" field.

[0185] 5. Based on the equipment data model, define the format for entering maintenance and testing data, 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 instance, based on the equipment data model, create new data entry configurations, define 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 the data. Of course, users can also use the device data model service provided by the device data model to perform historical data curve queries, chart queries, algorithm configurations, etc.

[0187] In the above embodiments, during the processing of pumped storage equipment data, after obtaining the original data corresponding to the sample pumped storage equipment, a metadata model corresponding to each original data can be quickly constructed. Through a series of data analyses, an equipment data model corresponding to the pumped storage equipment to be analyzed can be quickly constructed. This allows for faster configuration of data entry rules corresponding to the equipment data model, and further faster generation of the target form corresponding to the equipment data model. This improves the efficiency of data entry for the pumped storage equipment to be analyzed, thereby increasing the processing efficiency of pumped storage equipment data. Moreover, the entire process requires no manual intervention, avoiding the drawbacks of cumbersome manual processing methods that require significant time and manpower, resulting in low processing efficiency of pumped storage equipment data. This further improves the processing efficiency of pumped storage equipment data.

[0188] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0189] Based on the same inventive concept, this application also provides a pumped storage equipment data processing apparatus for implementing the above-described pumped storage equipment data processing method. The solution provided by this apparatus is similar to the solution described in the above-described method. Therefore, the specific limitations in one or more embodiments of the pumped storage equipment data processing apparatus provided below can be found in the limitations of the pumped storage equipment data processing method described above, and will not be repeated here.

[0190] In an exemplary embodiment, as shown in FIG7, a data processing device for pumped storage equipment 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 raw data corresponding to the sample pumped storage equipment; the raw data includes at least the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped storage equipment.

[0192] The first building module 702 is used to build the metadata model corresponding to each original data.

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

[0195] The third construction module 705 is used to construct the equipment data model corresponding to the pumped storage equipment to be analyzed based on 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 the target form corresponding to the equipment data model based on the data entry rules and the data to be entered from 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 further configured to identify the target data type corresponding to each original data; query the correspondence between the data type and the model architecture according to the target data type corresponding to each original data to obtain the target model architecture corresponding to each original data; and construct the metadata model corresponding to each original data according to the target model architecture corresponding to each original data.

[0200] In an exemplary embodiment, the second construction module 704 is further configured 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; to filter out the component data corresponding to each device component whose predicted importance is greater than a preset importance, as the key component data corresponding to each device component; and to construct the 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 further configured to receive a model configuration instruction sent by the terminal for the pumped storage device to be analyzed; extract the model identification information from the model configuration instruction; select the component data model corresponding to the model identification information from each component data model as the target component data model; and construct the device data model corresponding to the pumped storage device to be analyzed based on the target component data model.

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

[0203] In an exemplary embodiment, the form generation module 707 is further configured to obtain the current operating condition information corresponding to the data to be entered from 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; and generate the 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 aforementioned pumped storage power equipment data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0205] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram is shown in Figure 8. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores measurement data, equipment technical parameters, and maintenance test data, etc. The I / O interfaces of the computer device are used for information exchange between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a data processing method for pumped storage equipment.

[0206] Those skilled in the art will understand that the structure shown in Figure 8 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0207] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0208] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above-described method embodiments.

[0209] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0210] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media 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), magnetic 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0211] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data processing method for pumped storage equipment, characterized in that, The method includes: Obtain the raw data corresponding to the sample pumped storage equipment; the raw data includes at least the measurement data, equipment technical parameters, and maintenance test data corresponding to the sample pumped storage equipment; Construct the metadata model corresponding to each of the original data; Obtain the component data corresponding to each device component, and determine the metadata model corresponding to each device component from the metadata model; Based on the component data and metadata model corresponding to each device component, a component data model corresponding to each device component is constructed; Based on the component data model, the equipment data model corresponding to the pumped storage equipment to be analyzed is constructed. Configure the data entry rules corresponding to the device data model; Based on the data entry rules and the data to be entered from the pumped storage equipment to be analyzed, a target form corresponding to the equipment data model is generated. Enter the data to be entered into the target form.

2. The method according to claim 1, characterized in that, The construction of the metadata model corresponding to each of the original data includes: Identify the target data type corresponding to each of the original data; Based on the target data type corresponding to each of the original data, query the correspondence between the data type and the model architecture to obtain the target model architecture corresponding to each of the original data; Based on the target model architecture corresponding to each of the original data, construct the metadata model corresponding to each of the original data.

3. The method according to claim 1, characterized in that, The step of constructing a component data model corresponding to each device component based on the component data and metadata model corresponding to each device component includes: The component data corresponding to each device component is input into the trained importance prediction model to obtain the predicted importance of the component data corresponding to each device component. From the component data corresponding to each device component, the component data with a predicted importance greater than a preset importance are selected as the key component data corresponding to each device component; Based on the key component data and metadata model corresponding to each device component, a component data model corresponding to each device component is constructed.

4. The method according to claim 1, characterized in that, The step of constructing a device data model corresponding to the pumped storage device to be analyzed based on the component data model includes: Receive the model configuration instructions sent by the terminal for the pumped storage equipment to be analyzed; Extract the model identification information from the model configuration instructions; From each of the component data models, select the component data model that corresponds to the model identification information and use it as the target component data model; Based on the target component data model, a device data model corresponding to the pumped storage device to be analyzed is constructed.

5. The method according to claim 1, characterized in that, The data entry rules corresponding to the device data model are configured as follows: Retrieve information from multiple preset items; Determine the correlation between each preset item information and the device data model; From each preset item information, preset item information with a correlation degree greater than a preset correlation degree with the device data model is selected and used as the associated item information corresponding to the device data model. Configure the 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 based on the data entry rules and the data to be entered from the pumped storage equipment to be analyzed includes: Obtain the current operating status information corresponding to the data to be entered from the pumped storage equipment to be analyzed; Based on the current working condition information, the target preprocessing method corresponding to the data to be entered is determined; According to the target preprocessing method, the data to be entered is preprocessed to obtain the preprocessed data to be entered. Based on the data entry rules and the preprocessed data to be entered, a target form corresponding to the device data model is generated.

7. A data processing device for pumped storage equipment, characterized in that, The device includes: The data acquisition module is used to acquire the raw data corresponding to the sample pumped storage equipment; the raw data includes at least the measurement data, equipment technical parameters and maintenance test data corresponding to the sample pumped storage equipment; The first construction module is used to construct the metadata model corresponding to each of the original data; The model determination module is used to obtain the component data corresponding to each device component and determine the metadata model corresponding to each device component from the metadata model. The second construction module is used to construct the component data model corresponding to each device component based on the component data and metadata model corresponding to each device component. The third construction module is used to construct the equipment data model corresponding to the pumped storage equipment to be analyzed based on the component data model. The rule configuration module is used to configure the data entry rules corresponding to the device data model; The form generation module is used to generate a target form corresponding to the equipment data model based on the data entry rules and the data to be entered for 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, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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

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

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