Data processing method and apparatus for equipment consumption prediction

Abstract

This application discloses a data processing method and apparatus for predicting equipment consumption. The method includes: acquiring data to be processed, which is relevant data representing a task to be processed; performing similarity matching processing on the data to be processed based on multi-dimensional scene features to obtain first equipment consumption feature data, which is equipment consumption feature data representing similar multi-dimensional scene features; performing prediction processing on the first equipment consumption feature data and the data to be processed based on an equipment consumption prediction model to obtain process equipment consumption data; and performing dynamic correction processing on the process equipment consumption data based on task execution features to obtain final equipment consumption data. By performing equipment consumption prediction processing on the task to be processed based on multi-dimensional scene feature matching, and performing dynamic correction processing on the obtained process prediction data based on task execution, dynamic prediction of task equipment consumption throughout the entire lifecycle is achieved, and the accuracy of equipment consumption prediction is improved.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to a data processing method and apparatus for predicting equipment consumption. Background Technology

[0002] Equipment consumption refers to the process by which equipment gradually decreases or becomes ineffective due to use, wear and tear, aging, and other reasons in various application scenarios. To ensure effective resource allocation and reduce waste, especially in fields such as military equipment, industrial production, and vehicle maintenance, predicting equipment consumption is a prerequisite and foundation for effective technical support.

[0003] Currently, with the increasing number of factors affecting equipment consumption and the growing amount of data, existing methods for predicting equipment consumption are no longer applicable, resulting in low prediction accuracy.

[0004] Therefore, the relevant technologies cannot improve the accuracy of equipment consumption prediction. Summary of the Invention

[0005] The main objective of this application is to provide a data processing method and apparatus for predicting equipment consumption, so as to solve the technical problem of the accuracy of equipment consumption prediction in the prior art, and to achieve the technical effect of improving the accuracy of equipment consumption prediction and the prediction of the entire equipment consumption cycle.

[0006] To achieve the above objectives, the first aspect of this application proposes a data processing method for predicting equipment consumption during a mission cycle, comprising: Acquire data to be processed, wherein the data to be processed is relevant data used to represent the task to be processed; The data to be processed is subjected to similarity matching processing based on multi-dimensional scene features to obtain first equipment consumption feature data, wherein the first equipment consumption feature data is used to represent equipment consumption feature data corresponding to similar multi-dimensional scene features. The first equipment consumption characteristic data and the data to be processed are subjected to prediction processing based on the equipment consumption prediction model to obtain process equipment consumption data. The process equipment consumption data is dynamically corrected based on task execution characteristics to obtain the final equipment consumption data.

[0007] Further, the first equipment consumption characteristic data and the data to be processed are subjected to prediction processing based on the equipment consumption prediction model to obtain process equipment consumption data, including: The data to be processed is subjected to extraction processing based on task time features to obtain first task feature data, wherein the first task feature data is feature data of the time series of the task to be processed. The first equipment consumption feature data is updated based on the first task feature to obtain the second equipment consumption feature data, wherein the second equipment consumption feature data is data used to represent the equipment consumption features of the task to be processed. The second equipment consumption characteristic data is processed by a prediction model based on equipment consumption to obtain process equipment consumption data.

[0008] Furthermore, the process equipment consumption data is dynamically corrected based on task execution characteristics to obtain the following resulting equipment consumption data: Acquire task execution data, wherein the task execution data is data used to represent a preset task execution stage; The task execution data and the process equipment consumption data are subjected to time-series-based consumption comparison processing to obtain equipment consumption deviation characteristic data; The task execution data and the data to be processed are compared based on scene features to obtain scene offset feature data; The equipment consumption deviation feature data and the scene offset feature data are subjected to feedback correction processing based on the equipment consumption prediction model to obtain the resulting equipment consumption data.

[0009] Furthermore, the equipment consumption deviation feature data and the scene offset feature data are subjected to feedback correction processing based on the equipment consumption prediction model to obtain the resulting equipment consumption data, which includes: If the equipment consumption deviation feature data is greater than the preset consumption deviation feature threshold, the execution equipment consumption data is obtained, and the execution equipment consumption data and the process equipment consumption data are spliced ​​based on the time series to obtain updated equipment consumption sequence data. The scene feature data in the task execution data is updated based on the scene offset feature data to obtain updated task scene feature data; The updated equipment consumption sequence data and the updated task scenario feature data are subjected to prediction processing based on the equipment consumption prediction model to obtain the resulting equipment consumption data.

[0010] Furthermore, the data to be processed is subjected to similarity matching processing based on multi-dimensional scene features to obtain the first equipment consumption feature data, including: The data to be processed is subjected to multi-dimensional scene feature extraction processing to obtain task multi-dimensional scene feature data, wherein the task multi-dimensional scene feature data includes task environment feature data, task scale feature data and task action feature data. Match historical task data that are similar to the multidimensional scene feature data of the task in the preset historical task database to obtain multiple similar historical task data; The data on the consumption characteristics of the first equipment are obtained by filtering the multiple similar historical task data based on time intervals.

[0011] Furthermore, the data to be processed is subjected to multi-dimensional scene feature extraction processing to obtain task multi-dimensional scene feature data, including: The data to be processed is subjected to extraction processing based on task environment features to obtain task environment feature data, which is feature data used to represent the environment of the task to be executed. The data to be processed is subjected to extraction processing based on task scale features to obtain task scale feature data, which is feature data used to represent the scale of personnel and equipment for the task to be performed. The data to be processed is subjected to task action feature extraction processing to obtain task action feature data, which is feature data suitable for representing actions in the task to be executed; The multidimensional scenario feature data of the task is obtained based on the task environment feature data, the task scale feature data, and the task action feature data.

[0012] Furthermore, after performing dynamic correction processing based on task execution characteristics on the process equipment consumption data to obtain the result equipment consumption data, the method further includes: Obtain full-cycle task execution data, wherein the full-cycle task execution data is data used to represent the entire cycle of executing the task to be processed; The full-cycle task execution data is subjected to incremental learning processing based on similar scene features to obtain updated similar scene feature data; The updated similar scene feature data is processed by model update based on the equipment consumption prediction model to obtain the updated equipment consumption prediction model.

[0013] According to a second aspect of this application, a data processing apparatus for predicting equipment consumption is proposed, applied to predicting equipment consumption during a task cycle, comprising: A data acquisition module is used to acquire data to be processed, wherein the data to be processed is relevant data used to represent the task to be processed; The feature matching module is used to perform similarity matching processing on the data to be processed based on multi-dimensional scene features to obtain first equipment consumption feature data, wherein the first equipment consumption feature data is used to represent equipment consumption feature data corresponding to similar multi-dimensional scene features. The process prediction module is used to perform prediction processing on the first equipment consumption feature data and the data to be processed based on the equipment consumption prediction model to obtain process equipment consumption data. The dynamic correction module is used to perform dynamic correction processing on the process equipment consumption data based on task execution characteristics to obtain the result equipment consumption data.

[0014] According to a third aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing the computer to perform the above-described data processing method for predicting equipment consumption.

[0015] According to a fourth aspect of this application, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to said at least one processor; wherein the memory stores a computer program executable by said at least one processor, said computer program being executed by said at least one processor to cause said at least one processor to perform the above-described data processing method for predicting equipment consumption.

[0016] The technical solutions provided by the embodiments of this application may include the following beneficial effects: In this application, data to be processed is obtained, wherein the data to be processed is relevant data representing the task to be processed; similarity matching processing based on multi-dimensional scene features is performed on the data to be processed to obtain first equipment consumption feature data, wherein the first equipment consumption feature data is equipment consumption feature data representing similar multi-dimensional scene features; prediction processing based on an equipment consumption prediction model is performed on the first equipment consumption feature data and the data to be processed to obtain process equipment consumption data; dynamic correction processing based on task execution features is performed on the process equipment consumption data to obtain result equipment consumption data. By performing equipment consumption prediction processing based on multi-dimensional scene feature matching on the task to be processed, and performing dynamic correction processing based on task execution on the obtained process prediction data, dynamic prediction of task equipment consumption throughout the entire cycle is realized, and the accuracy of equipment consumption prediction is improved through similarity matching of multi-dimensional scene features. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application. In the drawings: Figure 1 A flowchart of a data processing method for predicting equipment consumption is provided in this application; Figure 2A flowchart of a data processing method for predicting equipment consumption is provided in this application; Figure 3 A flowchart of a data processing method for predicting equipment consumption is provided in this application; Figure 4 This application presents a schematic diagram of a data processing device for predicting equipment consumption. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] In this application, the terms "upper," "lower," "left," "right," "front," "rear," "top," "bottom," "inner," "outer," "middle," "vertical," "horizontal," "lateral," and "longitudinal" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are primarily for the purpose of better describing this application and its embodiments, and are not intended to limit the indicated device, element, or component to having a specific orientation, or to be constructed and operated in a specific orientation.

[0021] Furthermore, in addition to indicating location or positional relationship, some of the aforementioned terms may also have other meanings. For example, the term "above" may also be used in some cases to indicate a certain dependency or connection relationship. Those skilled in the art can understand the specific meaning of these terms in this application based on the specific circumstances.

[0022] Furthermore, the terms "installation," "setup," "equipped with," "connection," "linked," and "socketing" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or an internal connection between two devices, components, or parts. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0023] Equipment consumption forecasting is the prerequisite and foundation for effective technical support. With technological advancements, the factors influencing equipment consumption during various special operations have changed, posing significant challenges to equipment support. Equipment demand forecasting is fundamental to equipment support, guiding both equipment procurement and replenishment. For example, in battlefield environments, the demand for various equipment changes continuously during mission execution. Timely forecasting of equipment consumption can quickly guide replenishment. Modern high-tech equipment often consists of highly efficient and precise instruments, making demand forecasting crucial. Equipment demand forecasting systems or programs are indispensable. Accurately forecasting equipment demand is key to precise equipment support and also a current challenge in equipment support research.

[0024] Currently, with the increasing number of factors influencing equipment consumption and the growing volume of data, existing methods for predicting equipment consumption are no longer applicable, resulting in low prediction accuracy. Therefore, these technologies cannot improve the accuracy of equipment consumption prediction.

[0025] In an optional embodiment of this application, a data processing method for predicting equipment consumption is proposed, which is applied to predicting equipment consumption during a task cycle. Figure 1 This application provides a flowchart of a data processing method for predicting equipment consumption, as shown below. Figure 1 As shown, the method includes the following steps: S101: Obtain the data to be processed; The data to be processed refers to the relevant data used to represent the tasks to be processed. The data to be processed includes multi-dimensional data of the tasks to be processed, such as the action data of the tasks to be processed, including the duration of the action, the task of the action, and the intensity of the action; the scale data of the tasks to be processed, including the number of personnel and the equipment index of the participants; and the environmental data of the tasks to be processed, including climate, terrain, wind, visibility and other data.

[0026] S102: Perform similarity matching processing based on multi-dimensional scene features on the data to be processed to obtain the first equipment consumption feature data; The first equipment consumption feature data is used to represent the equipment consumption feature data corresponding to similar multidimensional scene features.

[0027] In an optional embodiment of this application, by matching multi-dimensional scene features with historical tasks, the equipment consumption features corresponding to historical task data are obtained. Compared with the existing technical solution of directly predicting equipment consumption through models, this reduces the amount of data that needs to be processed for predicting equipment consumption under multiple equipment consumption influencing factors in multi-dimensional scenes, and improves the efficiency of equipment consumption prediction.

[0028] In an optional embodiment of this application, a data processing method for predicting equipment consumption is proposed. Figure 2 This application provides a flowchart of a data processing method for predicting equipment consumption, as shown below. Figure 2 As shown, the method includes the following steps: S201: Extract multi-dimensional scene features from the data to be processed to obtain multi-dimensional scene feature data for the task. Multidimensional scenario feature data for tasks includes task environment feature data, task scale feature data, and task action feature data; In an optional embodiment of this application, a data processing method for predicting equipment consumption is proposed, comprising: The data to be processed is extracted based on task environment features to obtain task environment feature data, which is used to represent the environment of the task to be executed; the data to be processed is extracted based on task scale features to obtain task scale feature data, which is used to represent the scale of personnel and equipment in the task to be executed; the data to be processed is extracted based on task action features to obtain task action feature data, which is suitable for representing actions in the task to be executed; and multi-dimensional scenario feature data of the task is obtained based on the task environment feature data, task scale feature data, and task action feature data.

[0029] S202: Match historical task data that are similar to the multidimensional scene feature data of the task in the preset historical task database to obtain multiple similar historical task data; S203: Perform time-based filtering on multiple similar historical task data to obtain the first equipment consumption characteristic data.

[0030] The time intervals of the data to be processed and the aforementioned similar historical task data are extracted to obtain multiple time interval feature data, wherein the time interval feature data is used to represent the time interval between the similar historical task and the task to be executed; the equipment consumption features of the aforementioned similar historical task data are extracted to obtain multiple similar equipment consumption feature data; the multiple similar equipment consumption feature data are fused and filtered based on the aforementioned multiple time interval features to obtain the first equipment consumption feature data. Further, the multiple similar equipment consumption feature data can be weighted and fused based on the aforementioned multiple time interval features to obtain the equipment consumption feature data corresponding to the similar multidimensional scene feature.

[0031] S103: Perform prediction processing based on the equipment consumption prediction model on the first equipment consumption characteristic data and the data to be processed to obtain process equipment consumption data; In an optional embodiment of this application, a data processing method for predicting equipment consumption is proposed, comprising: The data to be processed is subjected to task time feature extraction processing to obtain first task feature data, wherein the first task feature data is feature data of the time series of the task to be processed, and the time series is used to represent the time corresponding to each execution stage of the task to be executed and the duration of the task to be executed.

[0032] The first equipment consumption feature data is updated based on the first task feature to obtain the second equipment consumption feature data, wherein the second equipment consumption feature data is used to represent the equipment consumption features of the task to be processed; the time series corresponding to the first equipment consumption feature data is different from the time series of the task to be executed, and the equipment consumption features in the first equipment consumption feature data are updated according to the time series of the task to be executed and the time series in the first equipment consumption feature data to obtain equipment consumption feature data that matches the time series of the task to be executed.

[0033] The second set of equipment consumption characteristic data is processed by a prediction model based on equipment consumption to obtain process equipment consumption data.

[0034] In an optional embodiment of this application, the equipment consumption prediction model is a pre-trained model, which is obtained by: using a combination of LSTM and Attention, training the LSTM-Attention model based on a sample set, optimizing the LSTM-Attention parameters using the gradient optimization algorithm GBO to improve the accuracy of prediction, and using a reverse learning strategy to fuse GBO and optimize the LSTM-Attention parameters to obtain the equipment consumption prediction model.

[0035] S104: Perform dynamic correction processing on the process equipment consumption data based on task execution characteristics to obtain the result equipment consumption data.

[0036] In an optional embodiment of this application, a data processing method for predicting equipment consumption is proposed. Figure 3 This application provides a flowchart of a data processing method for predicting equipment consumption, as shown below. Figure 3 As shown, the method includes the following steps: S301: Obtain task execution data; Task execution data is used to represent the preset task execution stage. The preset task execution stage can be a preset consumption prediction and correction period or a preset node in the task time series. Relevant data in the preset task execution stage are collected, including task equipment consumption data, current task environment data, current task action data, etc.

[0037] S302: Perform time-series-based consumption comparison processing on task execution data and process equipment consumption data to obtain equipment consumption deviation characteristic data; The actual consumption data and predicted consumption data of various equipment in the task execution data are compared to obtain the consumption deviation of each equipment. Based on the time series of the task to be executed and the time series of the actual task, the consumption deviation characteristic data of the above equipment are obtained by comparing them in different time periods.

[0038] S303: Perform scene feature-based comparison processing on the task execution data and the data to be processed to obtain scene offset feature data; The multidimensional scene features in the task execution data and the data to be processed are compared. If the multidimensional scene feature parameters in the task execution data and the data to be processed are greater than the preset scene offset threshold, the scene is offset and the scene offset feature data is obtained.

[0039] S304: Perform feedback correction processing on the equipment consumption deviation characteristic data and scene offset characteristic data based on the equipment consumption prediction model to obtain the resulting equipment consumption data.

[0040] In an optional embodiment of this application, a data processing method for predicting equipment consumption is proposed, comprising: If the equipment consumption deviation characteristic data is greater than the preset consumption deviation characteristic threshold, the execution equipment consumption data is obtained, and the execution equipment consumption data and the process equipment consumption data are spliced ​​together based on time series to obtain the updated equipment consumption sequence data. The scene feature data in the task execution data is updated based on the scene offset feature data to obtain the updated task scene feature data. The updated equipment consumption sequence data and updated task scenario feature data are subjected to prediction processing based on the equipment consumption prediction model to obtain the resulting equipment consumption data.

[0041] In an optional embodiment of this application, at each correction node, where the correction node is a preset node in the aforementioned consumption prediction correction cycle and / or task time series, the actual consumption data of the executed stage is concatenated with the current prediction data of the remaining task stage to construct a task consumption sequence, thereby obtaining updated equipment consumption sequence data; the scene feature data in the aforementioned task execution data is subjected to weighted update processing based on scene offset feature data to obtain updated task scene feature data; the reconstructed task consumption sequence is predicted together with the current updated task scene feature data; the re-prediction result is used to cover the original prediction amount of the remaining task stage, thereby realizing dynamic updating of the prediction result.

[0042] In an optional embodiment of this application, a data processing method for predicting equipment consumption is proposed. After performing dynamic correction processing on the process equipment consumption data based on task execution characteristics to obtain the result equipment consumption data, the method further includes: Obtain full-cycle task execution data, where full-cycle task execution data refers to data used to represent the entire cycle of executing the task to be processed; Incremental learning based on similar scene features is performed on the full-cycle task execution data to obtain updated similar scene feature data; The updated similar scene feature data are processed by the equipment consumption prediction model to obtain the updated equipment consumption prediction model.

[0043] In an optional embodiment of this application, a data processing device for predicting equipment consumption is applied to predicting equipment consumption during a task cycle. Figure 4 This application provides a schematic diagram of a data processing device for predicting equipment consumption, as shown below. Figure 4 As shown, it includes: The data acquisition module 41 is used to acquire data to be processed, wherein the data to be processed is relevant data used to represent the task to be processed; The feature matching module 42 is used to perform similarity matching processing based on multi-dimensional scene features on the data to be processed to obtain first equipment consumption feature data, wherein the first equipment consumption feature data is used to represent equipment consumption feature data corresponding to similar multi-dimensional scene features. The process prediction module 43 is used to perform prediction processing on the first equipment consumption feature data and the data to be processed based on the equipment consumption prediction model to obtain process equipment consumption data. The dynamic correction module 44 is used to perform dynamic correction processing on the process equipment consumption data based on task execution characteristics to obtain the result equipment consumption data.

[0044] The specific methods of execution of each unit in the above embodiments have been described in detail in the embodiments of the method, and will not be elaborated here.

[0045] In this application, data to be processed is obtained, wherein the data to be processed is relevant data representing the task to be processed; similarity matching processing based on multi-dimensional scene features is performed on the data to be processed to obtain first equipment consumption feature data, wherein the first equipment consumption feature data is equipment consumption feature data representing similar multi-dimensional scene features; prediction processing based on an equipment consumption prediction model is performed on the first equipment consumption feature data and the data to be processed to obtain process equipment consumption data; dynamic correction processing based on task execution features is performed on the process equipment consumption data to obtain result equipment consumption data. By performing equipment consumption prediction processing based on multi-dimensional scene feature matching on the task to be processed, and performing dynamic correction processing based on task execution on the obtained process prediction data, dynamic prediction of task equipment consumption throughout the entire cycle is achieved, and the accuracy of equipment consumption prediction is improved through similarity matching of multi-dimensional scene features.

[0046] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0047] Obviously, those skilled in the art should understand that the various units or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps into a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0048] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A data processing method for predicting equipment consumption, characterized in that, Equipment consumption prediction applied to the mission cycle includes: Acquire data to be processed, wherein the data to be processed is relevant data used to represent the task to be processed; The data to be processed is subjected to similarity matching processing based on multi-dimensional scene features to obtain first equipment consumption feature data, wherein the first equipment consumption feature data is used to represent equipment consumption feature data corresponding to similar multi-dimensional scene features. The first equipment consumption characteristic data and the data to be processed are subjected to prediction processing based on the equipment consumption prediction model to obtain process equipment consumption data. The process equipment consumption data is dynamically corrected based on task execution characteristics to obtain the final equipment consumption data.

2. The data processing method according to claim 1, characterized in that, The first equipment consumption characteristic data and the data to be processed are subjected to prediction processing based on the equipment consumption prediction model to obtain process equipment consumption data, including: The data to be processed is subjected to extraction processing based on task time features to obtain first task feature data, wherein the first task feature data is feature data of the time series of the task to be processed. The first equipment consumption feature data is updated based on the first task feature to obtain the second equipment consumption feature data, wherein the second equipment consumption feature data is data used to represent the equipment consumption features of the task to be processed. The second equipment consumption characteristic data is processed by a prediction model based on equipment consumption to obtain process equipment consumption data.

3. The data processing method according to claim 1, characterized in that, The process material consumption data is dynamically corrected based on task execution characteristics to obtain the following resulting material consumption data: Acquire task execution data, wherein the task execution data is data used to represent a preset task execution stage; The task execution data and the process equipment consumption data are subjected to time-series-based consumption comparison processing to obtain equipment consumption deviation characteristic data; The task execution data and the data to be processed are compared based on scene features to obtain scene offset feature data; The equipment consumption deviation feature data and the scene offset feature data are subjected to feedback correction processing based on the equipment consumption prediction model to obtain the resulting equipment consumption data.

4. The data processing method according to claim 3, characterized in that, The equipment consumption deviation characteristic data and the scene offset characteristic data are subjected to feedback correction processing based on the equipment consumption prediction model to obtain the resulting equipment consumption data, which includes: If the equipment consumption deviation feature data is greater than the preset consumption deviation feature threshold, the execution equipment consumption data is obtained, and the execution equipment consumption data and the process equipment consumption data are spliced ​​based on the time series to obtain updated equipment consumption sequence data. The scene feature data in the task execution data is updated based on the scene offset feature data to obtain updated task scene feature data; The updated equipment consumption sequence data and the updated task scenario feature data are subjected to prediction processing based on the equipment consumption prediction model to obtain the resulting equipment consumption data.

5. The data processing method according to claim 1, characterized in that, The data to be processed is subjected to similarity matching based on multi-dimensional scene features to obtain the first equipment consumption feature data, including: The data to be processed is subjected to multi-dimensional scene feature extraction processing to obtain task multi-dimensional scene feature data, wherein the task multi-dimensional scene feature data includes task environment feature data, task scale feature data and task action feature data. Match historical task data that are similar to the multidimensional scene feature data of the task in the preset historical task database to obtain multiple similar historical task data; The data on the consumption characteristics of the first equipment are obtained by filtering the multiple similar historical task data based on time intervals.

6. The data processing method according to claim 5, characterized in that, The data to be processed is subjected to multi-dimensional scene feature extraction processing to obtain task multi-dimensional scene feature data, including: The data to be processed is subjected to extraction processing based on task environment features to obtain task environment feature data, which is feature data used to represent the environment of the task to be executed. The data to be processed is subjected to extraction processing based on task scale features to obtain task scale feature data, which is feature data used to represent the scale of personnel and equipment for the task to be performed. The data to be processed is subjected to task action feature extraction processing to obtain task action feature data, which is feature data suitable for representing actions in the task to be executed; The multidimensional scenario feature data of the task is obtained based on the task environment feature data, the task scale feature data, and the task action feature data.

7. The data processing method according to claim 1, characterized in that, After performing dynamic correction processing based on task execution characteristics on the process equipment consumption data to obtain the resulting equipment consumption data, the method further includes: Obtain full-cycle task execution data, wherein the full-cycle task execution data is data used to represent the entire cycle of executing the task to be processed; The full-cycle task execution data is subjected to incremental learning processing based on similar scene features to obtain updated similar scene feature data; The updated similar scene feature data is processed by model update based on the equipment consumption prediction model to obtain the updated equipment consumption prediction model.

8. A data processing device for predicting equipment consumption, characterized in that, Equipment consumption prediction applied to the mission cycle includes: A data acquisition module is used to acquire data to be processed, wherein the data to be processed is relevant data used to represent the task to be processed; The feature matching module is used to perform similarity matching processing on the data to be processed based on multi-dimensional scene features to obtain first equipment consumption feature data, wherein the first equipment consumption feature data is used to represent equipment consumption feature data corresponding to similar multi-dimensional scene features. The process prediction module is used to perform prediction processing on the first equipment consumption feature data and the data to be processed based on the equipment consumption prediction model to obtain process equipment consumption data. The dynamic correction module is used to perform dynamic correction processing on the process equipment consumption data based on task execution characteristics to obtain the result equipment consumption data.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the data processing method for predicting equipment consumption as described in any one of claims 1-7.

10. An electronic device, characterized in that, include: At least one processor; The at least one processor is also connected in communication with a memory, wherein the memory stores a computer program that can be executed by the at least one processor to cause the at least one processor to perform the data processing method for predicting equipment consumption as described in any one of claims 1-7.

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