Intelligent geographic surveying and mapping data processing and analyzing method and system

Abstract

The invention relates to an intelligent geographic surveying and mapping data processing analysis method and system, and relates to the field of data processing, and the method comprises the steps: sequentially carrying out the preprocessing, data registration and data fusion of multi-source geographic surveying and mapping data, and generating a regional standard surveying and mapping image; performing salient feature extraction on the regional standard surveying and mapping image to obtain a local salient surveying and mapping image set; based on a convolutional neural network, performing deviation traversal comparison on the local significant surveying and mapping image set and a historical local significant surveying and mapping image set, and outputting a surveying and mapping deviation data set; and inputting the surveying and mapping deviation data set into a flood spreading prediction plug-in, and outputting a flood spreading prediction result. According to the method, the technical problems that the accuracy and efficiency of data processing are insufficient and the accuracy of flood spreading prediction is poor due to the fact that data fusion and feature extraction cannot be effectively carried out in an existing method can be solved, efficient data fusion and registration can be realized, and the flood spreading prediction accuracy is improved. And the accuracy and reliability of flood spreading prediction are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an intelligent geographic surveying and mapping data processing and analysis method and system. Background Art

[0002] With the continuous advancement of geographic information technology and surveying and mapping equipment, modern geographic surveying and mapping work plays an important role in disaster monitoring, environmental assessment, resource management and other fields. Especially in flood monitoring and prediction, geographic surveying and mapping data, as basic information, plays a vital role.

[0003] However, existing geographic surveying and mapping data come from a wide range of sources, including satellite remote sensing images, lidar data, meteorological data, ground measurement data, etc. Each data type has its own unique characteristics and format, and there are errors and inconsistencies between the data. Therefore, when processing and analyzing these multi-source data, how to effectively fuse the data and eliminate errors and contradictions between various types of data is still a difficulty.

[0004] At present, many traditional surveying and mapping data fusion methods rely on manual correction and simple linear fusion technology, which results in the lack of high precision in the results of data fusion and the inability to provide consistent and reliable geographic information layers, affecting subsequent disaster prediction and decision support. Summary of the invention

[0005] The present invention aims to solve the technical problem that the existing geographic surveying and mapping data processing methods cannot effectively perform data fusion and feature extraction, resulting in insufficient accuracy and efficiency of data processing, resulting in poor accuracy of flood spread prediction, and provide an intelligent geographic surveying and mapping data processing and analysis method and system to solve the problem.

[0006] The technical solution of the present invention to solve the above technical problems is as follows: In the first aspect, the present invention provides an intelligent geographic surveying and mapping data processing and analysis method, comprising: acquiring multi-source geographic surveying and mapping data of the disaster-stricken area through fixed-point collection through a variety of surveying and mapping equipment; preprocessing, data registration and data fusion of the multi-source geographic surveying and mapping data in sequence to generate a regional standard surveying and mapping image; extracting significant features of the regional standard surveying and mapping image to obtain a local significant surveying and mapping image set; based on a convolutional neural network, performing deviation traversal comparison on the local significant surveying and mapping image set and the historical local significant surveying and mapping image set of the previous adjacent collection node, and outputting a surveying and mapping deviation data set; inputting the surveying and mapping deviation data set into a flood spread prediction plug-in to output a flood spread prediction result.

[0007] In the second aspect, the present invention provides an intelligent geographic surveying and mapping data processing and analysis system, including: a multi-source geographic surveying and mapping data acquisition module, which is used to obtain multi-source geographic surveying and mapping data of the disaster-stricken area through fixed-point acquisition through a variety of surveying and mapping equipment; a regional standard surveying and mapping image generation module, which is used to preprocess, align and fuse the multi-source geographic surveying and mapping data in turn to generate a regional standard surveying and mapping image; a significant feature extraction module, which is used to extract significant features of the regional standard surveying and mapping image to obtain a local significant surveying and mapping image set; a deviation traversal comparison module, which is used to perform deviation traversal comparison on the local significant mapping image set with the historical local significant mapping image set of the previous adjacent acquisition node based on a convolutional neural network, and output a surveying and mapping deviation data set; a flood spread prediction module, which is used to input the surveying and mapping deviation data set into a flood spread prediction plug-in, and output a flood spread prediction result.

[0008] In a third aspect, the present invention further provides an electronic device, comprising: At least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can perform the steps of any one of the methods described in the first aspect above.

[0009] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed, the steps of the method described in any one of the first aspects are implemented.

[0010] The beneficial effects of the present invention are as follows: multi-source geographic mapping data of the disaster-stricken area are acquired through fixed-point acquisition through a variety of mapping equipment; pre-processing, data registration and data fusion are performed on the multi-source geographic mapping data in turn to generate a regional standard mapping image; significant features are extracted from the regional standard mapping image to obtain a local significant mapping image set; based on a convolutional neural network, deviation traversal and comparison are performed on the local significant mapping image set and the historical local significant mapping image set of the previous adjacent acquisition node to output a mapping deviation data set; the mapping deviation data set is input into a flood spread prediction plug-in to output a flood spread prediction result; efficient data fusion and registration can be achieved, and the accuracy and efficiency of data processing can be improved, thereby achieving the technical effect of improving the accuracy and reliability of flood spread prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a flow chart of an intelligent geographic surveying and mapping data processing and analysis method provided by the present invention; Figure 2 A schematic diagram of the structure of an intelligent geographic surveying and mapping data processing and analysis system provided by the present invention; Figure 3 A schematic diagram of the structure of an electronic device provided by the present invention; Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0012] In the accompanying drawings, the components represented by the reference numerals are described as follows: Multi-source geographic surveying and mapping data acquisition module 01, regional standard surveying and mapping image generation module 02, significant feature extraction module 03, deviation traversal comparison module 04, flood spread prediction module 05, electronic device 500, memory 510, processor 520, first computer program 511, computer readable storage medium 600, second computer program 611. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0014] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0015] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0016] Embodiment 1, as Figure 1 As shown, the embodiment of the present invention provides an intelligent geographic surveying and mapping data processing and analysis method, which specifically includes the following steps: S100: Use a variety of surveying and mapping equipment to collect multi-source geographic surveying and mapping data of the disaster-stricken area at fixed points.

[0017] In one embodiment, step S100 of the present application further includes: S110: The multi-source geographic surveying and mapping data at least includes satellite remote sensing images, ground measurement data, meteorological data and hydrological data.

[0018] Specifically, a variety of surveying and mapping equipment is used to collect data at fixed points in the disaster-stricken area to obtain multi-source geographic surveying and mapping data of the disaster-stricken area, wherein the multi-source geographic surveying and mapping data at least includes satellite remote sensing images, ground measurement data, meteorological data and hydrological data. Satellite remote sensing images can provide large-scale, high-resolution surface information, which is particularly suitable for rapid assessment of disaster-stricken areas. Remote sensing images can help identify changes in water areas, ground coverage (such as farmland, cities, forests, etc.) and disaster traces (such as flood-soaked areas, landslides, etc.); ground measurement data mainly include on-site measurement data, such as water level, precipitation, soil moisture, flow rate, etc. Compared with remote sensing data, ground measurement data has higher accuracy and can provide more detailed and local geographic information; meteorological data includes rainfall, temperature, humidity, wind speed and other data; hydrological data includes precipitation, surface runoff, groundwater level, water evaporation and other information in the basin. By collecting multi-source geographic surveying and mapping data in the disaster-stricken area at fixed points and effectively integrating and processing these data, more accurate disaster monitoring and prediction can be achieved.

[0019] S200: Preprocessing, data registration and data fusion are performed on the multi-source geographic surveying and mapping data in sequence to generate a regional standard surveying and mapping image.

[0020] In one embodiment, step S200 of the present application further includes: S210: According to the data preprocessing scheme, the multi-source geographic surveying and mapping data are subjected to noise removal, geometric correction and data format conversion to obtain multi-source processed surveying and mapping data; S220: The multi-source processed surveying and mapping data are subjected to spatial alignment and error correction to obtain multi-source standard surveying and mapping data; S230: In combination with spatial analysis technology, the multi-source standard surveying and mapping data are subjected to multi-dimensional data fusion to generate the regional standard surveying and mapping image.

[0021] Specifically, according to the data preprocessing scheme, the multi-source geographic surveying and mapping data are subjected to noise removal, geometric correction and data format conversion. Various types of surveying and mapping equipment will be affected by environmental factors, equipment accuracy, etc. when collecting data, resulting in noise in the data. Noise removal is to remove these unreal outliers through filtering, smoothing and other technologies, thereby improving data quality. Since different surveying and mapping equipment have different data collection angles and coordinate systems, geometric correction is required to ensure the spatial alignment of various types of data, which usually includes geometric transformation of the image, such as rotation, scaling, affine transformation, etc., to ensure consistency of data coordinates. Different surveying and mapping equipment may use different file formats and data structures, and format conversion is required to convert them into a unified format for subsequent analysis and fusion. Multi-source processed surveying and mapping data is obtained.

[0022] Next, registration technology is used to spatially match data from different sources, and by setting a common reference point or using a geometric correction algorithm, all data are ensured to be represented in the same coordinate system; further error correction is performed on the data, including atmospheric error, terrain error, etc., which can be adjusted through existing error models or adaptive algorithms to improve the accuracy of the data and obtain multi-source standard mapping data. Then, spatial analysis algorithms (such as buffer analysis, neighborhood analysis, hot spot analysis, etc.) are used to conduct in-depth analysis of multi-source data to extract useful spatial information (such as changing trends and specific features within the measurement area); information from different data sources (such as remote sensing images, meteorological data, ground measurement data, etc.) is combined, and data fusion algorithms (such as weighted average method, principal component analysis, Kalman filtering, etc.) are used to integrate different dimensional information of the data to generate a comprehensive regional standard mapping image.

[0023] Through noise removal, geometric correction and error correction, we ensure accurate alignment of data from each source, eliminate errors, and improve data accuracy and consistency. Through data fusion, data from multiple sources can complement each other, providing more comprehensive and detailed regional mapping information, greatly improving the value of data utilization. S300: extracting significant features from the regional standard mapping image to obtain a local significant mapping image set.

[0024] In one embodiment, step S300 of the present application further includes: S310: Input the regional standard mapping image into a Gaussian pyramid for multi-scale Gaussian downsampling to obtain a multi-scale image set, wherein the Gaussian pyramid includes a brightness extraction unit, a color extraction unit and a direction extraction unit; S320: Perform center-surrounding difference calculation on the multi-scale image set to generate a brightness feature map, a color feature map and a direction feature map; S330: Overlay the brightness feature map, the color feature map and the direction feature map to generate an initial salient image; S340: Extract the initial salient image according to an initial salient feature threshold, set the salient feature area that meets the initial salient feature threshold as a local salient mapping image, and obtain the local salient mapping image set.

[0025] Specifically, the Gaussian pyramid is a structure used for multi-scale image analysis, which usually generates image levels of different resolutions by Gaussian blurring and gradually downsampling the image. Through multi-level image processing, the feature information of the image at different scales can be captured; wherein, the Gaussian pyramid includes a brightness extraction unit, a color extraction unit and a direction extraction unit, the brightness extraction unit is used to extract brightness information from the image, and the brightness reflects the light and dark changes of the image; the color extraction unit is used to extract color information from the image, and the color change reflects the characteristics of different regions in the image; the direction extraction unit is used to extract edge information from the image, and the edge and texture features in the image are captured through the direction change. Then, the regional standard mapping image is input into the Gaussian pyramid for multi-scale Gaussian downsampling. Through Gaussian downsampling, image levels of different resolutions are obtained, and the details of the image can be gradually analyzed from coarse to fine, and the feature information of different scales can be captured to obtain a multi-scale image set.

[0026] Then, for the image at each scale, the difference between the central area and the surrounding area of ​​the image is calculated. The significant area usually has a large difference in brightness, color or direction. The difference between the center and the surrounding reflects the interesting or important parts of the image, and generates a brightness feature map, a color feature map and a direction feature map. Among them, the brightness feature map highlights the areas with obvious changes in light and dark in the image by calculating the difference in brightness changes in the image; the color feature map highlights the areas with significant color changes by calculating the difference in color changes in the image; the direction feature map highlights the areas with obvious direction changes by calculating the difference in edge and texture changes in the image; through this difference calculation, feature maps of brightness, color and direction are generated, which respectively reflect the significant changes in brightness, color and direction of the image.

[0027] Furthermore, the brightness, color and direction feature maps calculated above are merged to integrate various types of feature information. This superposition can integrate information of different feature dimensions, thereby generating a more comprehensive significant image, identifying all significant areas in the image, and obtaining an initial significant image. Then, an initial significant feature threshold is set, and the initial significant feature threshold is used to filter out the most significant part of the initial significant image. The threshold is usually determined based on the grayscale value or feature intensity value of the image to ensure that only areas of great significance are extracted; the initial significant image is further extracted according to the initial significant feature threshold, and the significant feature area that meets the initial significant feature threshold is set as a local significant mapping image, that is, through threshold processing, the significant feature area that meets the conditions is identified and marked as a local significant mapping image. Such an area is usually the most prominent and most variable part of the image, representing the most critical geographic information feature in the image, and a local significant mapping image set is obtained.

[0028] S400: Based on a convolutional neural network, the local significant mapping image set is subjected to deviation traversal comparison with a historical local significant mapping image set of a previous adjacent acquisition node, and a mapping deviation data set is output.

[0029] In one embodiment, step S400 of the present application further includes: S410: Constructing a deviation comparison model based on a convolutional neural network, wherein the deviation comparison model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer; S420: Acquiring a sample binary significant mapping image set and a sample mapping deviation data set, and using the sample binary significant mapping image as input and the sample mapping deviation data as output, performing supervised training on the deviation comparison model to obtain a deviation comparison model that meets a predetermined convergence condition; S430: After mapping and combining the local significant mapping image set with the historical local significant mapping image set, inputting the input into the deviation comparison model for deviation traversal comparison, and outputting the mapping deviation data set.

[0030] Specifically, a deviation comparison model is constructed based on a convolutional neural network (CNN) to compare the deviation between a local significant mapping image set and a historical local significant mapping image set, wherein the deviation comparison model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer, wherein the input layer is used to receive input data, i.e., a local significant mapping image set; the convolution layer extracts local features in the input image through a convolution operation, and the convolution layer can capture information such as edges, textures, and shapes in the image; the pooling layer is used to perform dimensionality reduction processing on the convolution result, reduce the amount of calculation, and retain important features; the fully connected layer is used to flatten and connect the pooled feature data, and learn the relationship between the input data through weight transfer between multiple neurons; the output layer is used to output the mapping deviation data set.

[0031] Next, a sample binary salient mapping image set and a sample mapping deviation data set are acquired, wherein the sample binary salient mapping image is an image combination including two salient images; then, the sample binary salient mapping image is used as input, and the sample mapping deviation data is used as output, and the deviation comparison model is supervisedly trained using the sample binary salient mapping image set and the sample mapping deviation data set, that is, the sample binary salient mapping image set is used as input, and the sample mapping deviation data set is used as output, and the deviation comparison model is trained, and the training process adjusts the model parameters (such as convolution kernel, weight, etc.) so that the model can accurately predict the deviation between the input image and the historical image; during the training process, the loss function of the model will be continuously optimized until the predetermined convergence conditions are met, usually including minimizing the error or reaching a certain number of training rounds; and a deviation comparison model that meets the predetermined convergence conditions is obtained.

[0032] Finally, the local significant mapping image set is mapped and combined with the historical local significant mapping image set, and the combination result is input into the deviation comparison model as input data for deviation traversal comparison, and the mapping deviation data set is output. Through these technical steps, the accuracy and efficiency of geographic mapping data processing can be significantly improved, providing strong technical support for disaster management and environmental monitoring.

[0033] S500: Inputting the surveying and mapping deviation data set into a flood spread prediction plug-in, and outputting a flood spread prediction result.

[0034] In one embodiment, step S500 of the present application further includes: S510: Query historical flood records in the disaster-stricken area, obtain a sample flood spread data set and multiple sample mapping deviation data sets, and collect actual flood spread results of different sample flood spread data and different sample mapping deviation data sets after a predetermined time period to obtain a sample flood spread result set, wherein the flood spread results include the flood expansion speed and the flood expansion direction.

[0035] Specifically, the historical flood records of the disaster-stricken areas are queried, representative flood events are screened out from the historical flood records, and a sample flood spread data set and multiple sample mapping deviation data sets are collected; then the actual flood spread results of different sample flood spread data and different sample mapping deviation data sets after a predetermined time period are collected, where the flood spread results include the flood expansion speed and the flood expansion direction, to obtain a sample flood spread result set.

[0036] S520: Using the sample flood spread data set, multiple sample mapping deviation data sets and sample flood spread result set as training data sets, supervise the BP neural network until convergence, and obtain a flood spread prediction plug-in.

[0037] In one embodiment, step S520 of the present application further includes: S521: Divide the training data set into Q equal parts, and select Q parts with replacement to construct a first training set, iterate and select Q times to obtain Q training sets, where Q is an integer greater than 5; S522: Use the Q training sets to perform supervised training on the BP neural network respectively until convergence to obtain Q flood spread prediction models, and integrate and construct the flood spread prediction plug-in based on the Q flood spread prediction models, where the output of the flood spread prediction plug-in is the mode of the outputs of the Q flood spread prediction models.

[0038] Specifically, the training data set is divided into Q equal parts, where Q is an integer greater than 5, and Q parts of the data set are selected with replacement Q times to construct a first training set, and the same method is used to iteratively select Q times to obtain Q training sets.

[0039] The Q training sets are further utilized, with sample flood spread data and sample mapping deviation data sets as input and sample flood spread results as output, to perform supervised training on the BP neural network. The BP neural network is a common feedforward neural network, which is trained based on the back propagation algorithm and adjusts the weights in the network by minimizing the prediction error. In the present application, the BP neural network is used for flood spread prediction modeling, and its goal is to predict the output flood spread results (such as expansion speed and direction) based on the input training data (sample flood spread data and mapping deviation data sets), wherein the training process is to input sample data and obtain the predicted value through calculations at each layer of the network. Forward propagation is to generate the network output after the input signal is processed by weights, activation functions, etc.; the error is calculated by comparing the output predicted by the network with the actual flood spread result, and the error is usually expressed as mean square error; then the back propagation algorithm is used to update the weights and biases in the network according to the output error. This process calculates the gradient of each weight by the chain rule, adjusts the weights by the gradient descent method, and repeatedly trains with a known sample data set until the network error reaches the predetermined convergence condition, which usually means that the error is small enough or the number of training times reaches the maximum limit, and Q flood spread prediction models are obtained. Then, the flood spread prediction plug-in is integrated and constructed based on the Q flood spread prediction models, wherein the output of the flood spread prediction plug-in is the mode of the outputs of the Q flood spread prediction models.

[0040] S530: Input the surveying and mapping deviation data set into the flood spread prediction plug-in, and output the flood spread prediction result.

[0041] Specifically, the surveying and mapping deviation data set is finally input into the flood spread prediction plug-in, and the flood spread prediction result is output.

[0042] The intelligent geographic surveying and mapping data processing and analysis method provided by the embodiment of the present invention has at least the following technical effects: Through a variety of surveying and mapping equipment, multi-source geographic surveying and mapping data of the disaster-stricken area are collected at fixed points; the multi-source geographic surveying and mapping data are preprocessed, data aligned and data fused in turn to generate a regional standard surveying and mapping image; significant features are extracted from the regional standard surveying and mapping image to obtain a local significant surveying and mapping image set; based on a convolutional neural network, the local significant surveying and mapping image set is compared with the historical local significant surveying and mapping image set of the previous adjacent collection node through deviation traversal, and a surveying and mapping deviation data set is output; the surveying and mapping deviation data set is input into a flood spread prediction plug-in to output a flood spread prediction result; efficient data fusion and alignment can be achieved, and the accuracy and efficiency of data processing can be improved, thereby achieving the technical effect of improving the accuracy and reliability of flood spread prediction.

[0043] Embodiment 2, as Figure 2 As shown, based on the same inventive concept as the intelligent geographic surveying and mapping data processing and analysis method provided in the first embodiment, the embodiment of the present invention further provides an intelligent geographic surveying and mapping data processing and analysis system, including: The multi-source geographic surveying and mapping data acquisition module 01 is used to acquire multi-source geographic surveying and mapping data of the disaster-stricken area through fixed-point acquisition using a variety of surveying and mapping equipment; the regional standard surveying and mapping image generation module 02 is used to perform preprocessing, data registration and data fusion on the multi-source geographic surveying and mapping data in sequence to generate a regional standard surveying and mapping image; the significant feature extraction module 03 is used to extract significant features from the regional standard surveying and mapping image to obtain a local significant surveying and mapping image set; the deviation traversal comparison module 04 is used to perform deviation traversal comparison on the local significant surveying and mapping image set with the historical local significant surveying and mapping image set of the previous adjacent acquisition node based on a convolutional neural network, and output a surveying and mapping deviation data set; the flood spread prediction module 05 is used to input the surveying and mapping deviation data set into a flood spread prediction plug-in to output a flood spread prediction result.

[0044] In one embodiment, the intelligent geographic surveying and mapping data processing and analysis system further includes: the multi-source geographic surveying and mapping data at least includes satellite remote sensing images, ground measurement data, meteorological data and hydrological data.

[0045] In one embodiment, the intelligent geographic surveying and mapping data processing and analysis system further includes: according to a data preprocessing scheme, performing noise removal, geometric correction and data format conversion on the multi-source geographic surveying and mapping data to obtain multi-source processed surveying and mapping data; performing spatial alignment and error correction on the multi-source processed surveying and mapping data to obtain multi-source standard surveying and mapping data; and combining spatial analysis technology to perform multi-dimensional data fusion on the multi-source standard surveying and mapping data to generate the regional standard surveying and mapping image.

[0046] In one embodiment, the intelligent geographic surveying and mapping data processing and analysis system also includes: inputting the regional standard surveying and mapping image into a Gaussian pyramid for multi-scale Gaussian downsampling to obtain a multi-scale image set, wherein the Gaussian pyramid includes a brightness extraction unit, a color extraction unit and a direction extraction unit; performing center-surrounding difference calculations on the multi-scale image set to generate a brightness feature map, a color feature map and a direction feature map; superimposing the brightness feature map, the color feature map and the direction feature map to generate an initial significant image; extracting the initial significant image according to an initial significant feature threshold, setting the significant feature area that meets the initial significant feature threshold as a local significant surveying and mapping image, and obtaining the local significant surveying and mapping image set.

[0047] In one embodiment, the intelligent geographic surveying and mapping data processing and analysis system also includes: constructing a deviation comparison model based on a convolutional neural network, wherein the deviation comparison model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer; collecting and acquiring a sample binary significant mapping image set and a sample mapping deviation data set, and using the sample binary significant mapping image as input and the sample mapping deviation data as output, the deviation comparison model is supervised and trained to obtain a deviation comparison model that meets predetermined convergence conditions; after mapping and combining the local significant mapping image set with the historical local significant mapping image set, the deviation comparison model is input for deviation traversal comparison, and the mapping deviation data set is output.

[0048] In one embodiment, the intelligent geographic mapping data processing and analysis system also includes: querying historical flood records in the disaster-stricken area, obtaining a sample flood spread data set and multiple sample mapping deviation data sets, and collecting actual flood spread results of different sample flood spread data and different sample mapping deviation data sets after a predetermined time period to obtain a sample flood spread result set, wherein the flood spread result includes the flood expansion speed and the flood expansion direction; using the sample flood spread data set, multiple sample mapping deviation data sets and sample flood spread result set as training data sets, supervising the BP neural network until convergence, and obtaining a flood spread prediction plug-in; inputting the mapping deviation data set into the flood spread prediction plug-in, and outputting the flood spread prediction result.

[0049] In one embodiment, the intelligent geographic surveying and mapping data processing and analysis system also includes: dividing the training data set into Q equal parts, selecting Q times with replacement to construct a first training set, iteratively selecting Q times to obtain Q training sets, wherein Q is an integer greater than 5; using the Q training sets, respectively performing supervised training on the BP neural network until convergence to obtain Q flood spread prediction models, and integrating and constructing the flood spread prediction plug-in based on the Q flood spread prediction models, wherein the output of the flood spread prediction plug-in is the mode of the outputs of the Q flood spread prediction models.

[0050] For example 3, please refer to Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: using a variety of surveying and mapping equipment, fixed-point acquisition of multi-source geographic surveying and mapping data of the disaster-stricken area; sequentially preprocessing, data registration, and data fusion of the multi-source geographic surveying and mapping data to generate a regional standard surveying and mapping image; extracting significant features from the regional standard surveying and mapping image to obtain a local significant surveying and mapping image set; based on a convolutional neural network, performing deviation traversal comparison on the local significant surveying and mapping image set and the historical local significant surveying and mapping image set of the previous adjacent acquisition node, and outputting a surveying and mapping deviation data set; inputting the surveying and mapping deviation data set into a flood spread prediction plug-in to output a flood spread prediction result.

[0051] Example 4, please refer to Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by the processor, the following steps are implemented: through a variety of surveying and mapping equipment, multi-source geographic surveying and mapping data of the disaster-stricken area are acquired by fixed-point collection; the multi-source geographic surveying and mapping data are preprocessed, data registered and data fused in sequence to generate a regional standard surveying and mapping image; significant features are extracted from the regional standard surveying and mapping image to obtain a local significant surveying and mapping image set; based on a convolutional neural network, the local significant surveying and mapping image set is compared with the historical local significant surveying and mapping image set of the previous adjacent acquisition node through deviation traversal, and a surveying and mapping deviation data set is output; the surveying and mapping deviation data set is input into a flood spread prediction plug-in to output a flood spread prediction result.

[0052] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0053] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0055] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0056] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0057] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0058] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. An intelligent geographic surveying and mapping data processing and analysis method, characterized in that: include: Through a variety of surveying and mapping equipment, multi-source geographic surveying and mapping data of the disaster-stricken areas are collected at fixed points; Preprocessing, data registration and data fusion are performed on the multi-source geographic surveying and mapping data in sequence to generate a regional standard surveying and mapping image; Extracting significant features from the standard mapping image of the area to obtain a set of local significant mapping images; Based on a convolutional neural network, the local significant mapping image set is compared with the historical local significant mapping image set of the previous adjacent acquisition node through deviation traversal, and a mapping deviation data set is output; The surveying and mapping deviation data set is input into the flood spread prediction plug-in, and the flood spread prediction result is output.

2. The intelligent geographic surveying and mapping data processing and analysis method according to claim 1 is characterized in that: The multi-source geographic surveying and mapping data at least include satellite remote sensing images, ground measurement data, meteorological data and hydrological data.

3. The intelligent geographic surveying and mapping data processing and analysis method according to claim 2 is characterized in that: Preprocessing, data registration and data fusion are performed on the multi-source geographic surveying and mapping data in sequence to generate a regional standard surveying and mapping image, including: According to the data preprocessing scheme, the multi-source geographic surveying and mapping data are subjected to noise removal, geometric correction and data format conversion to obtain multi-source processed surveying and mapping data; Performing spatial alignment and error correction on the multi-source processed surveying and mapping data to obtain multi-source standard surveying and mapping data; Combined with spatial analysis technology, multi-dimensional data fusion is performed on the multi-source standard surveying and mapping data to generate the regional standard surveying and mapping image.

4. The intelligent geographic surveying and mapping data processing and analysis method according to claim 1 is characterized in that: Extracting significant features from the regional standard mapping image to obtain a local significant mapping image set includes: Inputting the regional standard surveying and mapping image into a Gaussian pyramid for multi-scale Gaussian downsampling to obtain a multi-scale image set, wherein the Gaussian pyramid includes a brightness extraction unit, a color extraction unit and a direction extraction unit; Performing center-surround difference calculations on the multi-scale image set to generate a brightness feature map, a color feature map, and a direction feature map; Performing superposition processing on the brightness feature map, the color feature map and the direction feature map to generate an initial salient image; The initial salient image is extracted according to an initial salient feature threshold, and a salient feature region satisfying the initial salient feature threshold is set as a local salient mapping image, thereby obtaining a set of local salient mapping images.

5. The intelligent geographic surveying and mapping data processing and analysis method according to claim 1 is characterized in that: Based on a convolutional neural network, the local significant mapping image set is compared with the historical local significant mapping image set of the previous adjacent acquisition node through deviation traversal, and a mapping deviation data set is output, including: Constructing a deviation comparison model based on a convolutional neural network, wherein the deviation comparison model includes an input layer, a convolutional layer, a pooling layer, a fully connected layer and an output layer; Acquire a sample binary significant mapping image set and a sample mapping deviation data set, and use the sample binary significant mapping image as input and the sample mapping deviation data as output to perform supervised training on the deviation comparison model, so as to obtain a deviation comparison model that meets a predetermined convergence condition; After mapping and combining the local significant mapping image set and the historical local significant mapping image set, the deviation comparison model is input to perform deviation traversal comparison, and the mapping deviation data set is output.

6. The intelligent geographic surveying and mapping data processing and analysis method according to claim 1 is characterized in that: The mapping deviation dataset is input into the flood spread prediction plug-in, and the flood spread prediction result is output, including: Query the historical flood records of the disaster-stricken area, obtain a sample flood spread data set and multiple sample mapping deviation data sets, and collect the actual flood spread results of different sample flood spread data and different sample mapping deviation data sets after a predetermined time period to obtain a sample flood spread result set, wherein the flood spread results include the flood expansion speed and the flood expansion direction; The sample flood spread data set, multiple sample mapping deviation data sets and sample flood spread result set are used as training data sets to supervise the BP neural network until convergence, thereby obtaining a flood spread prediction plug-in; The mapping deviation data set is input into the flood spread prediction plug-in, and the flood spread prediction result is output.

7. The intelligent geographic surveying and mapping data processing and analysis method according to claim 6 is characterized in that: Get the Flood Prediction plugin, including: Divide the training data set into Q equal parts, select Q parts with replacement, construct a first training set, iterate and select Q parts Q times, and obtain Q training sets, where Q is an integer greater than 5; Using the Q training sets, supervised training is performed on the BP neural network until convergence, to obtain Q flood spread prediction models, and the flood spread prediction plug-in is constructed based on the integration of the Q flood spread prediction models, wherein the output of the flood spread prediction plug-in is the mode of the outputs of the Q flood spread prediction models.

8. An intelligent geographic surveying and mapping data processing and analysis system, characterized in that: The steps for implementing the intelligent geographic surveying and mapping data processing and analysis method according to any one of claims 1 to 7 include: The multi-source geographic surveying and mapping data acquisition module is used to obtain multi-source geographic surveying and mapping data of the disaster-stricken area through a variety of surveying and mapping equipment; A regional standard surveying and mapping image generation module is used to perform preprocessing, data registration and data fusion on the multi-source geographic surveying and mapping data in sequence to generate a regional standard surveying and mapping image; A significant feature extraction module is used to extract significant features from the regional standard mapping image to obtain a local significant mapping image set; A deviation traversal comparison module is used to perform deviation traversal comparison on the local significant mapping image set and the historical local significant mapping image set of the previous adjacent acquisition node based on a convolutional neural network, and output a mapping deviation data set; The flood spread prediction module is used to input the surveying and mapping deviation data set into the flood spread prediction plug-in and output the flood spread prediction result.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the steps of an intelligent geographic surveying and mapping data processing and analysis method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, which, when executed by a processor, implements the steps of an intelligent geographic surveying and mapping data processing and analysis method as described in any one of claims 1 to 7.

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