Data processing method and device, equipment, medium and program product

Abstract

The invention relates to a data processing method and device, equipment, a medium and a program product. The data processing method comprises the steps that at least two types of to-be-processed data are acquired, and the at least two types of to-be-processed data are acquired based on different sensors; performing normalization processing on the to-be-processed data to obtain first data; obtaining a preset resolution, and performing matching processing on the resolution of the first data based on the preset resolution to obtain second data; and carrying out statistical analysis on the second data to obtain a statistical analysis result corresponding to the to-be-processed data, thereby obtaining data with the same scale and resolution by carrying out normalization processing and resolution matching processing on the to-be-processed data, laying a foundation for statistical analysis of the to-be-processed data, reducing the complexity of data statistical analysis, and improving the accuracy of statistical analysis of the to-be-processed data. And meanwhile, the accuracy of data statistical analysis is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a data processing method, device, equipment, medium and program product. Background Art

[0002] Water conservancy and hydropower is a key infrastructure construction, involving the development and utilization of water resources and the production of electricity. It provides water and electricity for agriculture, industry and life through the construction of reservoirs, hydropower stations, irrigation systems, etc. Water conservancy and hydropower projects not only promote local economic development, but also help flood prevention and disaster reduction and environmental protection. In water conservancy and hydropower projects, data collection and processing is the core of project decision-making and management. It plays a vital role in all stages of project planning, design, construction, operation and maintenance.

[0003] However, water conservancy and hydropower projects often involve a variety of different data sources, which differ in temporal and spatial distribution, accuracy and coverage, posing challenges to subsequent data statistical analysis. Summary of the invention

[0004] In order to solve the above technical problems, the present disclosure provides a data processing method, device, equipment, medium and program product.

[0005] A first aspect of an embodiment of the present disclosure provides a data processing method, including:

[0006] Acquire at least two types of data to be processed, where the at least two types of data to be processed are collected based on different sensors;

[0007] Performing normalization processing on the data to be processed to obtain first data;

[0008] Acquire a preset resolution, and perform matching processing on the resolution of the first data based on the preset resolution to obtain second data;

[0009] Perform statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed.

[0010] A second aspect of an embodiment of the present disclosure provides a data processing device, including:

[0011] A data acquisition module, used to acquire at least two types of data to be processed, where the at least two types of data to be processed are acquired based on different sensors;

[0012] A normalization processing module, used for performing normalization processing on the data to be processed to obtain first data;

[0013] A resolution processing module, used to obtain a preset resolution, and perform matching processing on the resolution of the first data based on the preset resolution to obtain second data;

[0014] The statistical analysis module is used to perform statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed.

[0015] A third aspect of the embodiments of the present disclosure provides an electronic device, including:

[0016] processor;

[0017] A memory for storing executable instructions;

[0018] The processor is used to read executable instructions from the memory and execute the executable instructions to implement the data processing method provided in the first aspect above.

[0019] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the data processing method provided by the first aspect.

[0020] A fifth aspect of an embodiment of the present disclosure provides a computer program product, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the data processing method of the first aspect is implemented.

[0021] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0022] The data processing method, apparatus, device, medium and program product provided by the embodiments of the present disclosure can obtain at least two types of data to be processed, wherein at least two types of data to be processed are obtained based on different sensors, the data to be processed are normalized to obtain first data, after obtaining the first data, a preset resolution is obtained, and then, the resolution of the first data is matched based on the preset resolution to obtain second data, and then the second data is statistically analyzed to obtain statistical analysis results corresponding to the data to be processed. Therefore, by normalizing the data to be processed and matching the resolution, data with the same scale and resolution can be obtained, which solves the problem that data collected by different sensors have different scales and resolutions, lays the foundation for the statistical analysis of the data to be processed, and thus reduces the complexity of the data statistical analysis, while improving the accuracy of the data statistical analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 is a flow chart of a data processing method provided by an embodiment of the present disclosure;

[0026] Figure 2 is a flowchart of a resolution processing method provided by an embodiment of the present disclosure;

[0027] Figure 3 is a flow chart of another data processing method provided by an embodiment of the present disclosure;

[0028] Figure 4 is a structural schematic diagram of a data processing device provided by an embodiment of the present disclosure;

[0029] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0032] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0033] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0034] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0035] Generally speaking, in water conservancy and hydropower projects, data collection and processing is the core of project decision-making and management, and it plays a vital role in all stages of project planning, design, construction, operation and maintenance. However, water conservancy and hydropower projects often involve a variety of different data sources, and these data have differences in temporal and spatial distribution, accuracy and coverage, which brings challenges to subsequent data statistical analysis. To address this problem, the present disclosure provides a data processing method, which is introduced in conjunction with specific embodiments below.

[0036] Figure 1 It is a flowchart of a data processing method provided by an embodiment of the present disclosure. The method can be executed by a data processing device. The data processing device can be implemented in software and / or hardware. The data processing device can be configured in an electronic device, such as a server or a terminal, wherein the terminal specifically includes a mobile phone, a computer or a tablet computer, etc.

[0037] like Figure 1 As shown, the data processing method provided in this embodiment includes the following steps.

[0038] S110 , obtaining at least two types of data to be processed, where the at least two types of data to be processed are collected based on different sensors.

[0039] In the embodiments of the present disclosure, the data to be processed can be understood as data that requires subsequent statistical analysis of the model or other processing.

[0040] In different application fields, at least two types of data to be processed are different.

[0041] For example, in the field of water conservancy and hydropower engineering, at least two types of data to be processed may include topographic data collected by satellite sensors, precipitation data collected by drone remote sensing, and environmental data such as temperature, humidity, and air pressure collected by sensors at ground meteorological observation stations, etc., without limitation here.

[0042] The at least two types of data to be processed may be data collected by different sensors in the same time period and the same area.

[0043] In some embodiments of the present disclosure, an electronic device may receive a data processing request, parse the data processing request, obtain identification information of the data to be processed corresponding to the data processing request, and obtain the data to be processed from a preset database based on the identification information of the data to be processed.

[0044] In some other embodiments of the present disclosure, the electronic device may receive a data processing request, and after receiving the data processing request, control different sensors to perform data acquisition operations, and receive different types of data returned by different sensors in real time, and determine them as data to be processed.

[0045] S120 , normalize the data to be processed to obtain first data.

[0046] In the embodiments of the present disclosure, normalization processing may be understood as an operation of converting data of different scales and numerical ranges to a uniform scale.

[0047] Specifically, after obtaining at least two types of data to be processed, the electronic device obtains a preset scale range; for each type of data to be processed, based on the preset scale range and the preset normalization processing method, the data to be processed is normalized to within the preset scale range to obtain first data, thereby being able to obtain data with a uniform scale by normalizing the data to be processed.

[0048] Exemplarily, taking the preset scale range of [0,1] and the data to be processed as the temperature data collected by the ground meteorological observation station sensor as an example, the temperature data collected by the ground meteorological observation station sensor is the temperature value from time 0 to time 24 in area A. The highest temperature of the temperature data from time 0 to time 24 in area A is 20 degrees, and the lowest temperature is minus 10 degrees. At this time, the temperature data is normalized by subtracting the minimum temperature, i.e., minus 10 degrees, from the temperature value at time B and dividing the difference by the difference between the highest temperature and the lowest temperature to obtain the data within the preset scale range after normalization at time B. By analogy, the first data corresponding to the normalized temperature data collected by the ground meteorological observation station sensor is obtained.

[0049] S130: Obtain a preset resolution, and perform matching processing on the resolution of the first data based on the preset resolution to obtain second data.

[0050] In the embodiments of the present disclosure, the data to be processed may include spatial data and time series data. Spatial data can be understood as data of spatial dimension, i.e., data used to describe the location, shape, size and distribution characteristics of spatial entities, such as topographic data; time series data can be understood as data that changes over time, such as precipitation, temperature, humidity and other data.

[0051] For spatial data, the preset resolution is spatial resolution; for time series data, the preset resolution is temporal resolution.

[0052] In the embodiment of the present disclosure, the preset resolutions for the same type of data are the same.

[0053] Specifically, after acquiring the first data, the electronic device obtains a preset resolution corresponding to each type of first data for different types of first data, and then determines a method for performing resolution matching processing based on the preset resolution and the resolution corresponding to each type of first data, and then matches the resolutions of the different types of first data to obtain second data corresponding to each type of first data.

[0054] S140: Perform statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed.

[0055] In the embodiment of the present disclosure, statistical analysis can be understood as performing statistics and analysis on the change trend of data in the second data.

[0056] The statistical analysis results include statistical analysis results corresponding to each type of data to be processed. For example, when the data to be processed include topographic data collected by satellite sensors, precipitation data collected by drone remote sensing, and environmental data such as temperature, humidity, and air pressure collected by ground meteorological observation station sensors, the statistical analysis results include the change trend of the elevation value of a certain area in the topographic data after scale unification and resolution matching, the change trend of precipitation in a certain area within a preset time period and the abnormality of precipitation, and the change trend of temperature, humidity, air pressure, etc. in a certain area within a preset time period and its abnormality.

[0057] Specifically, after acquiring the second data, the electronic device forms a curve graph corresponding to the second data, and based on a preset curvature threshold and / or a preset change threshold, performs statistical analysis on the change trend and abnormal conditions of the second data, such as the change trend and abnormal conditions of the topography and / or environment corresponding to the second data, to obtain the statistical analysis results corresponding to the data to be processed. Thus, the statistical analysis results can be applied to aspects such as project planning, design, monitoring and evaluation, and the data to be processed can also be displayed based on the statistical analysis results, so that users can view the data to be processed.

[0058] In the embodiment of the present disclosure, at least two types of data to be processed can be obtained, wherein at least two types of data to be processed are obtained based on different sensors. The data to be processed are normalized to obtain first data. After obtaining the first data, a preset resolution is obtained. Then, the resolution of the first data is matched based on the preset resolution to obtain second data. Then, the second data is statistically analyzed to obtain statistical analysis results corresponding to the data to be processed. Thus, data with the same scale and resolution can be obtained by normalizing the data to be processed and matching the resolution, which solves the problem that data collected by different sensors have different scales and resolutions, lays the foundation for the statistical analysis of the data to be processed, and thus reduces the complexity of the data statistical analysis while improving the accuracy of the data statistical analysis.

[0059] Based on the above embodiments of the present disclosure, Figure 2 A flowchart of a resolution processing method is provided, specifically, as follows: Figure 2 As shown, matching processing is performed on the resolution of the first data based on a preset resolution to obtain the second data, which specifically includes the following steps:

[0060] S210: Obtain a resolution of the first data, and compare the resolution of the first data with a preset resolution to obtain a comparison result.

[0061] Specifically, after acquiring the first data, the electronic device can acquire the resolution of the first data, and acquire a preset resolution corresponding to the first data from a preset database based on the type of the first data, and compare the resolution of the first data with the preset resolution to obtain a comparison result.

[0062] S220: When the comparison result shows that the resolution of the first data is smaller than the preset resolution, matching processing is performed on the resolution of the first data based on the preset resolution and a preset interpolation algorithm to obtain second data.

[0063] Specifically, when the electronic device determines that the comparison result is that the resolution of the first data is less than the preset resolution, it determines that the resolution of the first data needs to be upsampled, and then matches the resolution of the first data based on the preset resolution and the preset interpolation algorithm to obtain the second data.

[0064] The preset interpolation algorithm can be a linear interpolation algorithm. Specifically, a linear function can be constructed based on any two data points in the first data, and the first data can be interpolated based on the linear function and multiple data points in the first data. During the interpolation process, if the data change of the first data is relatively slow, interpolation is performed based on data point pairs within a preset range of distance from the interpolation point. At the same time, the interpolation result can also be adjusted based on the difference between the interpolation result and the actual value to optimize the interpolation effect, thereby achieving resolution matching of the first data.

[0065] S230: When the comparison result shows that the resolution of the first data is greater than the preset resolution, matching processing is performed on the resolution of the first data based on the preset resolution and a downsampling method to obtain second data.

[0066] Specifically, when the electronic device determines that the comparison result is that the resolution of the first data is greater than the preset resolution, it determines that the resolution of the first data needs to be downsampled, and then matches the resolution of the first data based on the preset resolution and the downsampling method to obtain the second data.

[0067] In some embodiments of the present disclosure, when the data to be processed is spatial data, that is, when the first data is spatial data, the resolution of the first data is matched based on a preset resolution and a downsampling method to obtain second data, which may specifically include: dividing the area corresponding to the first data into at least two area grids based on a preset resolution, calculating the average value of the first data corresponding to each area grid, and determining the second data based on the average value of the first data.

[0068] Specifically, when the electronic device determines that the first data is spatial data, it divides the area corresponding to the first data into at least two regional grids based on a preset resolution, performs an average calculation on the data in each regional grid to obtain the average value of the first data corresponding to each regional grid, and then, based on the order between the regional grids, determines the data set composed of the average values ​​of the first data corresponding to each regional grid as the second data.

[0069] In other embodiments of the present disclosure, when the data to be processed is time series data, that is, when the first data is time series data, the resolution of the first data is matched based on a preset resolution and a downsampling method to obtain second data, which may specifically include: dividing the time corresponding to the first data into at least two time periods based on a preset resolution, calculating the average value of the second data corresponding to each time period, and determining the second data based on the average value of the second data.

[0070] Specifically, when the electronic device determines that the first data is time series data, it divides the time corresponding to the first data into at least two time periods based on a preset resolution, performs an average calculation on the data in each time period to obtain an average value of the second data corresponding to each time period, and then, based on the sequence between the time periods, determines the data set consisting of the average values ​​of the second data corresponding to each time period as the second data.

[0071] In the embodiment of the present disclosure, resolution matching processing can be performed on the first data based on a preset resolution to obtain second data after resolution matching, which lays a foundation for subsequent statistical analysis of the data and the use of the data and facilitates the viewing and use of the data.

[0072] Furthermore, after the second data is obtained, the second data corresponding to each different type of data to be processed are merged into the same folder to facilitate users to view and use the data.

[0073] In an embodiment of the present disclosure, after performing statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed, the data processing method may further include: inputting the second data and the statistical analysis results as training data into a preset machine learning model, training the preset machine learning model, and obtaining a trained machine learning model.

[0074] Among them, the preset machine learning model can be a deep learning model such as a convolutional neural network, a recurrent neural network, etc., which is not limited here.

[0075] In the embodiments of the present disclosure, the specific implementation method of training the preset machine learning model is similar to the existing method of training convolutional neural network models, etc., and will not be elaborated here.

[0076] The trained machine learning model has the ability to predict the data change trend in the subsequent preset time period based on the data in the current time period.

[0077] Furthermore, after obtaining the trained machine learning model, the data processing method may also include: for time series data, inputting the third data corresponding to the current time period into the trained machine learning model, and using the trained machine learning model to predict the change trend of the third data within a preset time period after the current time period, to obtain the prediction result corresponding to the third data.

[0078] The third data may be understood as data that has been normalized and resolution matched and needs to be predicted for its changing trend.

[0079] In the embodiments of the present disclosure, the changing trends of time series data can be effectively analyzed and predicted through the trained machine learning model, further improving the use value of the data.

[0080] In an embodiment of the present disclosure, after the third data corresponding to the current time period is input into a trained machine learning model, the trained machine learning model predicts the change trend of the third data within a preset time period after the current time period, and obtains the prediction result corresponding to the third data, the data processing method may further include: obtaining the actual change trend of the third data within a preset time period after the current time period; and optimizing the trained machine learning model based on the actual change trend and the prediction result.

[0081] Specifically, the electronic device can periodically obtain the actual change trend of the third data within a preset period after the current period from the preset database, compare the actual change trend with the prediction result, and then optimize the trained machine learning model based on the comparison result.

[0082] Exemplarily, the third data is the temperature data of area A. The electronic device obtains the temperature data of area A at 0-8 seconds, and then inputs it into the trained machine learning model. The trained machine learning model predicts the change trend of the temperature data of area A at 9-12 seconds to obtain the prediction result. After obtaining the prediction result, the actual change trend of the temperature data of area A at 9-12 seconds is obtained within a certain period of time, and the actual change trend is compared with the prediction result. In this way, the parameters of the trained machine learning model are adjusted based on the comparison result to optimize the trained machine learning model.

[0083] In the embodiments of the present disclosure, the performance of the trained machine learning model can be optimized and the prediction accuracy of the trained machine learning model can be improved by optimizing the trained machine learning model.

[0084] In the embodiment of the present disclosure, at least two types of data to be processed may include topographic data and / or environmental data. Correspondingly, the statistical analysis results include the changing trends of the topography and / or environment corresponding to the at least two types of data to be processed.

[0085] After performing statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed, the data processing method may further include: visually displaying the data to be processed based on the statistical analysis results.

[0086] In some embodiments of the present disclosure, after obtaining the statistical analysis results, the electronic device performs three-dimensional modeling on the statistical analysis results, and then displays the data to be processed and the change trend of the data to be processed in the form of three-dimensional space, thereby improving the visibility and intuitiveness of the data. For example, the topographic data is presented in the form of a three-dimensional model, which can intuitively display the landform feature information and change trend information of mountains, rivers, lakes, etc.

[0087] In some other embodiments of the present disclosure, after obtaining the statistical analysis results and the prediction results, the electronic device performs three-dimensional modeling on the statistical analysis results and the prediction results corresponding to the data to be processed, and then displays the data to be processed, the historical change trends of the data to be processed, and the future predicted change trends of the data to be processed in the form of three-dimensional space. Thereby, while improving the visibility and intuitiveness of the data, users can understand the future change trends of the data, thereby improving the user experience.

[0088] In the disclosed embodiments, the data processing method can be applied to an information system to process and analyze data in the information system in real time, wherein the information system supports data access and operation on multiple platforms and multiple terminals, thereby improving the efficiency of project management and providing scientific and comprehensive decision-making support for project planning, construction and operation.

[0089] Figure 3 is a flow chart of another data processing method provided by an embodiment of the present disclosure. Figure 3 As shown, the data processing method may include the following steps:

[0090] S310: Acquire at least two types of data to be processed, where the at least two types of data to be processed are collected based on different sensors.

[0091] S320 , normalize the data to be processed to obtain first data.

[0092] S330: Obtain a preset resolution, and perform matching processing on the resolution of the first data based on the preset resolution to obtain second data.

[0093] S340: Perform statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed.

[0094] S350. Input the second data and the statistical analysis results as training data into a preset machine learning model, train the preset machine learning model, and obtain a trained machine learning model.

[0095] S360. For time series data, the third data corresponding to the current time period is input into the trained machine learning model. The trained machine learning model predicts the change trend of the third data within a preset time period after the current time period to obtain the prediction result corresponding to the third data.

[0096] S370. Obtain an actual change trend of the third data within a preset time period after the current time period; and optimize the trained machine learning model based on the actual change trend and the prediction result.

[0097] It should be noted that the specific implementation of steps S310-S370 is similar to the implementation of the relevant steps in the above-mentioned embodiment of the present disclosure, and will not be repeated here.

[0098] In the disclosed embodiments, by normalizing and resolution matching different types of data to be processed, data with the same scale and resolution can be obtained, thereby improving the accuracy of statistical analysis of the data. At the same time, the preset machine learning model is trained by data with the same scale and resolution and their corresponding statistical analysis results to obtain a trained machine learning model. The trained machine learning model is then used to predict the changing trend of time series data to obtain prediction results, thereby facilitating users to make full use of the data, providing strong support for engineering decision-making, etc., improving user experience, and optimizing the trained machine learning model through actual data changing trends and prediction results to improve the accuracy of prediction results.

[0099] Figure 4 It is a structural schematic diagram of a data processing device provided in an embodiment of the present disclosure.

[0100] In the embodiments of the present disclosure, the data processing device may be arranged in an electronic device, and is understood as a part of the functional modules in the above electronic device. Specifically, the electronic device may be a server or a terminal, wherein the terminal specifically includes a mobile phone, a computer or a tablet computer, etc., or may be any device capable of executing the data processing method in the above embodiments of the present disclosure, and is not limited here.

[0101] like Figure 4As shown, the data processing device 400 may include a data acquisition module 410 , a normalization processing module 420 , a resolution processing module 430 and a statistical analysis module 440 .

[0102] The data acquisition module 410 can be used to acquire at least two types of data to be processed, where the at least two types of data to be processed are acquired based on different sensors.

[0103] The normalization processing module 420 can be used to perform normalization processing on the data to be processed to obtain first data.

[0104] The resolution processing module 430 may be used to obtain a preset resolution, and perform matching processing on the resolution of the first data based on the preset resolution to obtain the second data.

[0105] The statistical analysis module 440 may be used to perform statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed.

[0106] In the embodiment of the present disclosure, at least two types of data to be processed can be obtained, wherein at least two types of data to be processed are obtained based on different sensors. The data to be processed are normalized to obtain first data. After obtaining the first data, a preset resolution is obtained. Then, the resolution of the first data is matched based on the preset resolution to obtain second data. Then, the second data is statistically analyzed to obtain statistical analysis results corresponding to the data to be processed. Thus, data with the same scale and resolution can be obtained by normalizing the data to be processed and matching the resolution, which solves the problem that data collected by different sensors have different scales and resolutions, lays the foundation for the statistical analysis of the data to be processed, and thus reduces the complexity of the data statistical analysis while improving the accuracy of the data statistical analysis.

[0107] In some embodiments of the present disclosure, the resolution processing module 430 may be specifically used to obtain the resolution of the first data, compare the resolution of the first data with a preset resolution, and obtain a comparison result;

[0108] When the comparison result shows that the resolution of the first data is greater than the preset resolution, matching processing is performed on the resolution of the first data based on the preset resolution and the downsampling method to obtain the second data;

[0109] When the comparison result is that the resolution of the first data is smaller than the preset resolution, the resolution of the first data is matched based on the preset resolution and the preset interpolation algorithm to obtain the second data.

[0110] In some embodiments of the present disclosure, at least two types of data to be processed include spatial data and / or time series data.

[0111] The resolution processing module 430 may also be specifically configured to, when the first data is spatial data, divide the region corresponding to the first data into at least two regional grids based on a preset resolution, calculate an average value of the first data corresponding to each regional grid, and determine the second data based on the average value of the first data;

[0112] When the first data is time series data, the time corresponding to the first data is divided into at least two time periods based on a preset resolution, the average value of the second data corresponding to each time period is calculated, and the second data is determined based on the average value of the second data.

[0113] In some embodiments of the present disclosure, the data processing device 400 may further include a model training module and a data prediction module.

[0114] The model training module can be used to perform statistical analysis on the second data and obtain statistical analysis results corresponding to the data to be processed, and then input the second data and the statistical analysis results as training data into a preset machine learning model to train the preset machine learning model to obtain a trained machine learning model.

[0115] The data prediction module can be used to input the third data corresponding to the current time period into the trained machine learning model for time series data after obtaining the trained machine learning model. The trained machine learning model will predict the changing trend of the third data within a preset time period after the current time period to obtain the prediction result corresponding to the third data.

[0116] In some embodiments of the present disclosure, the data processing device 400 may further include a model optimization module.

[0117] The model optimization module may be used to input the third data corresponding to the current period into the trained machine learning model, and the trained machine learning model predicts the change trend of the third data within a preset period after the current period, and after obtaining the prediction result corresponding to the third data, obtain the actual change trend of the third data within the preset period after the current period;

[0118] The trained machine learning model is optimized based on the actual change trends and prediction results.

[0119] In some embodiments of the present disclosure, at least two types of data to be processed include topographic data and / or environmental data, and the statistical analysis results include changing trends of topography and / or environment corresponding to the at least two types of data to be processed.

[0120] The data processing device 400 may further include an information display module.

[0121] The information display module can be used to perform a statistical analysis on the second data to obtain a statistical analysis result corresponding to the data to be processed, and then perform a visual display of the data to be processed based on the statistical analysis result.

[0122] It should be noted that Figure 4 The data processing device 400 shown can execute each step in the above method embodiment and realize each process and effect in the above method embodiment, which will not be described in detail here.

[0123] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure.

[0124] In the disclosed embodiment, Figure 5 The electronic device shown may be a server or a terminal, wherein the terminal specifically includes a mobile phone, a computer or a tablet computer, etc., and may also be any device capable of executing the data processing method in the above-mentioned embodiment of the present disclosure, and is not limited here.

[0125] like Figure 5 As shown, the electronic device may include a processor 510 and a memory 520 storing computer program instructions.

[0126] Specifically, the processor 510 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present disclosure.

[0127] The memory 520 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 520 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 520 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 520 may be inside or outside the integrated gateway device. In a particular embodiment, the memory 520 is a non-volatile solid-state memory. In a particular embodiment, the memory 520 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0128] The processor 510 reads and executes the computer program instructions stored in the memory 520 to perform the steps of the data processing method provided in the embodiment of the present disclosure.

[0129] In one example, the electronic device may further include a transceiver 530 and a bus 540. Figure 5 As shown, the processor 510, the memory 520 and the transceiver 530 are connected via a bus 540 and communicate with each other.

[0130] The bus 540 includes hardware, software, or both. For example, but not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a Memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 540 may include one or more buses.

[0131] The embodiments of the present disclosure further provide a computer-readable storage medium, which may store a computer program. When the computer program is executed by a processor, the processor implements the data processing method provided by the embodiments of the present disclosure.

[0132] The above-mentioned storage medium may, for example, include a memory 520 of computer program instructions, and the above-mentioned instructions may be executed by the processor 510 of the electronic device to complete the data processing method provided by the embodiment of the present disclosure. Optionally, the storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (Random Access Memory, RAM), a compact disc read-only memory (Compact Disc ROM, CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0133] The embodiments of the present disclosure also provide a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, the data processing method provided by the embodiments of the present disclosure is implemented, and the various processes and effects in the above-mentioned embodiments of the present disclosure can be implemented, which will not be elaborated here.

[0134] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data processing method, characterized in that: include: Acquire at least two types of data to be processed, where the at least two types of data to be processed are collected based on different sensors; Normalizing the data to be processed to obtain first data; Acquire a preset resolution, and perform matching processing on the resolution of the first data based on the preset resolution to obtain second data; Perform statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed.

2. The method according to claim 1, characterized in that The matching process is performed on the resolution of the first data based on the preset resolution to obtain the second data, including: Acquire a resolution of the first data, and compare the resolution of the first data with the preset resolution to obtain a comparison result; When the comparison result is that the resolution of the first data is greater than the preset resolution, matching processing is performed on the resolution of the first data based on the preset resolution and a downsampling method to obtain the second data; When the comparison result is that the resolution of the first data is smaller than the preset resolution, the resolution of the first data is matched based on the preset resolution and a preset interpolation algorithm to obtain the second data.

3. The method according to claim 2, characterized in that The at least two types of data to be processed include spatial data and / or time series data; When the first data is spatial data, performing matching processing on the resolution of the first data based on the preset resolution and downsampling method to obtain the second data includes: Dividing the area corresponding to the first data into at least two area grids based on the preset resolution, calculating the average value of the first data corresponding to each area grid, and determining the second data based on the average value of the first data; When the first data is time series data, matching the resolution of the first data based on the preset resolution and downsampling method to obtain the second data includes: The time corresponding to the first data is divided into at least two time periods based on a preset resolution, an average value of the second data corresponding to each time period is calculated, and the second data is determined based on the average value of the second data.

4. The method according to claim 1, characterized in that: After performing statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed, the method further includes: Inputting the second data and the statistical analysis result as training data into a preset machine learning model, training the preset machine learning model, and obtaining a trained machine learning model; After obtaining the trained machine learning model, the method further includes: For time series data, the third data corresponding to the current time period is input into the trained machine learning model, and the trained machine learning model predicts the change trend of the third data within a preset time period after the current time period to obtain the prediction result corresponding to the third data.

5. The method according to claim 4, characterized in that After inputting the third data corresponding to the current period into the trained machine learning model, and predicting the change trend of the third data within a preset period after the current period by the trained machine learning model, and obtaining the prediction result corresponding to the third data, the method further includes: Acquire an actual change trend of the third data within a preset time period after the current time period; The trained machine learning model is optimized based on the actual change trend and the prediction result.

6. The method according to claim 1, characterized in that The at least two types of data to be processed include topographic data and / or environmental data, and the statistical analysis results include the changing trends of the topographic data and / or the environment corresponding to the at least two types of data to be processed respectively; After performing statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed, the method further includes: The data to be processed is visualized based on the statistical analysis results.

7. A data processing device, characterized in that: include: A data acquisition module, used to acquire at least two types of data to be processed, wherein the at least two types of data to be processed are acquired based on different sensors; A normalization processing module, used for performing normalization processing on the data to be processed to obtain first data; A resolution processing module, used to obtain a preset resolution, and perform matching processing on the resolution of the first data based on the preset resolution to obtain second data; The statistical analysis module is used to perform statistical analysis on the second data to obtain statistical analysis results corresponding to the data to be processed.

8. An electronic device, characterized in that: include: processor; A memory for storing executable instructions; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the data processing method described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the data processing method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program or instructions, characterized in that: When the computer program or instruction is executed by a processor, the data processing method according to any one of claims 1 to 6 is implemented.

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