Data management method, device and equipment based on Halatile technology and medium

Abstract

The invention relates to a data management method, device and equipment based on a Hailatile technology and a medium, and the method comprises the steps: determining business demand information based on the business process data of a Hailatile system, and the business demand information comprises a business link corresponding to Hailatile annotation data and a use scene corresponding to the Hailatile annotation data; and based on the business demand information and the system performance parameters, obtaining a classification management rule corresponding to the Halatile annotation data. According to the method and the device, the standardized classification management rule is formulated, so that the consistency and the accuracy of the processing process of the Hailatile annotation data can be ensured, the processing efficiency is improved, and the repetitive work is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of data management, and in particular to a data management method, device, equipment and medium based on Helava technology. Background Art

[0002] Three-dimensional visualization technology uses massive high-precision DEM data, high-resolution image data, sensitive data within the channel, and three-dimensional power equipment models to realistically reproduce the surrounding environment of the entire transmission line corridor through a three-dimensional visualization platform.

[0003] 3D visualization technology is widely used in power grid achievement display, auxiliary planning and design, auxiliary construction management and auxiliary safety operation and maintenance. In the transmission line, a complete transmission line stretches hundreds of kilometers or even thousands of kilometers. Therefore, in the 3D visualization system, whether it is vector data, raster data, or digital model, the amount of data is very huge, which makes the construction of 3D scene quite complicated.

[0004] With the continuous accumulation of power grid engineering resources at various voltage levels, the problem of digital models in three-dimensional visualization scenes has been basically solved. In the early stage of the project, the three-dimensional scene can be quickly built through the schematic tower model of the same voltage level, and in the later stage of the project, the three-dimensional scene can be quickly built through the three-dimensional designed model. However, sensitive data such as high-resolution aerial images, high-resolution terrain, and important crossings in the channel of each project cannot be reused and need to be processed through multiple processes to meet the needs. There is no shortage of manual and repetitive work in the process, which reduces work efficiency. Summary of the invention

[0005] In order to reduce repetitive work and improve work efficiency, the present application provides a data management method, device, equipment and medium based on Helava technology.

[0006] In a first aspect, the present application provides a data management method based on Helava technology, comprising:

[0007] Based on the business process data of the Helawa system, business demand information is determined, wherein the business demand information includes the business links corresponding to the Helawa mapping data and the usage scenarios corresponding to the Helawa mapping data;

[0008] Based on the business demand information and system performance parameters, classification management rules corresponding to the Heila Wa mapping data are obtained.

[0009] The beneficial effects of the present application are as follows: by formulating standardized classification management rules, the consistency and accuracy of the processing process of Helawa mapping data can be ensured, the complexity and uncertainty of Helawa mapping data processing can be reduced, and the processing efficiency can be improved. At the same time, it is convenient to track the processing progress and status of Helawa mapping data through classification management rules, thereby reducing repetitive work.

[0010] Further, the obtaining of classification management rules corresponding to the Helawa mapping data based on the business demand information and system performance parameters includes:

[0011] Based on the three-dimensional digitization target, the Helawa survey data is divided into first data and second data, the first data being key data for display on the three-dimensional platform;

[0012] Based on the business demand information and the system performance parameter, respectively obtain data classification granularity corresponding to the first data and the second data, wherein the data classification granularity is a parameter representing the degree of detail of the data;

[0013] Based on each of the data classification granularities, respectively obtain a data classification framework corresponding to the first data and the second data;

[0014] Based on the first data and the second data, classification management rules of the data classification framework corresponding to each are obtained, and the classification management rules include a classification basis and a classification method.

[0015] The beneficial effect of adopting the above further solution is: by dividing the Helava mapping data into first data and second data, and determining the respective data classification granularity based on business demand information and system performance parameters, the data storage and processing methods can be optimized, which helps to improve the overall performance of the Helava system while ensuring data quality.

[0016] Further, after obtaining the classification management rules of the data classification framework corresponding to each of the first data and the second data, the method further includes:

[0017] Determine whether the real-time business process data of the Helava system meets the granularity adjustment trigger condition;

[0018] If so, based on the historical business process data, predict the change trend of the Helava system business process data in the future, and adjust the data classification granularity according to the change trend.

[0019] The beneficial effects of adopting the above further scheme are:

[0020] Further, the change trend includes fluctuation of the data volume, and the fluctuation includes the rate of change of the data volume. Adjusting the data classification granularity according to the change trend includes:

[0021] If the change rate of the future data volume is a positive value and the change rate exceeds a preset rate range, a refinement rule corresponding to the data classification granularity is obtained based on the business demand information and the system performance parameter, and the data classification granularity is refined based on the refinement rule;

[0022] If the change rate does not exceed the preset rate range, or the change rate of the future data volume is negative and the change rate exceeds the rate range, then based on the business demand information and the system performance parameters, the coarsening rules corresponding to the data classification granularity are obtained, and the data classification granularity is coarsened based on the coarsening rules.

[0023] The beneficial effect of adopting the above further scheme is: dynamically adjusting the data classification granularity according to the change of data volume can ensure that the system always maintains the best performance state, reducing the possibility of system performance degradation or resource waste due to excessive or small data volume.

[0024] Furthermore, the method further comprises:

[0025] Based on the constructed image automation processing model, the cropped output image corresponding to the orthophoto generated by Helawa is obtained;

[0026] Calculating a plurality of evaluation index information corresponding to the output image, wherein the plurality of evaluation index information includes a signal-to-noise ratio, a peak signal-to-noise ratio, and a structural similarity index of the output image;

[0027] Based on the usage scenario and the plurality of evaluation index information, obtaining quality assessment information corresponding to the output image;

[0028] Based on the quality assessment information, determine whether the output image needs to be adjusted; if not, store the output image.

[0029] The beneficial effect of adopting the above further scheme is: by constructing a good image automation processing model, the orthophotos generated by Helawa can be automatically cropped and processed, reducing the steps and time of manual operations and improving processing efficiency. By calculating multiple evaluation index information and combining the use scenario for quality assessment, it can be ensured that the quality of the output image meets the requirements of the use scenario, and the availability and accuracy of the output image are improved. Through automated quality assessment and judgment, unnecessary adjustment operations can be reduced, and the adjustment cost and time cost are reduced. Storing the output images that meet the requirements can facilitate subsequent queries and use, and also help optimize storage management and improve the utilization of storage resources.

[0030] Furthermore, the image automation processing model includes geographic transformation tools, reclassification tools, raster to polygon tools, fusion tools and clipping tools;

[0031] The method of obtaining the cropped output image corresponding to the orthophoto generated by Helawa based on the constructed image automatic processing model includes:

[0032] Inputting the orthophoto generated by Helawa and the target seven parameters into the geographic transformation tool, and based on the geographic transformation tool and the target seven parameters, performing geographic correction processing on the orthophoto so as to align the orthophoto with the specified coordinate system, and obtaining and outputting the corrected image;

[0033] Inputting the corrected image into the reclassification tool, reclassifying the corrected image based on the reclassification tool, replacing the target pixel values ​​in the corrected image, obtaining and outputting a reclassified image, wherein the target pixel values ​​include noise and outliers;

[0034] Input the reclassified image into the raster-to-polygon tool, convert the raster image corresponding to the reclassified image into a vector surface element based on the raster-to-polygon tool, and obtain and output a vector layer containing a plurality of the vector surface elements;

[0035] Inputting the vector layer into the fusion tool, eliminating the target surface component in the vector layer based on the fusion tool, and obtaining and outputting a cleaned vector layer;

[0036] Inputting the cleaned vector layer into the fusion tool, merging adjacent surface elements with similar attributes corresponding to the cleaned vector layer into a single surface element based on the fusion tool, and obtaining and outputting a fused vector layer;

[0037] Based on the fused vector layer, a buffer vector layer including a buffer is obtained, wherein the buffer is used to ensure that the cropped image includes complete edge information;

[0038] The planar elements of the buffer vector layer are used as a clipping template, and the corrected image is clipped based on a clipping tool and the clipping template to obtain the clipped output image.

[0039] The beneficial effect of adopting the above further solution is: realizing the automation of the entire cutting process, greatly improving the processing efficiency and accuracy, and reducing the need for manual intervention.

[0040] Furthermore, before the orthophoto image and the target seven parameters generated by Helawa are input into the geographic transformation tool, the method further includes:

[0041] Obtaining suitability evaluation results corresponding to the current seven parameters, wherein the seven parameters include seven alignment correction parameters, and the suitability evaluation results include errors between corrected image data obtained by applying the current seven parameters and actual measurement data;

[0042] If the error is greater than a preset error threshold, the current seven parameters are corrected based on supplementary data to obtain corrected seven parameters, wherein the supplementary data is public control point data or measurement data covering different geographical areas and different application scenarios;

[0043] The modified seven parameters are verified, and if the verification passes, the modified seven parameters are used as the target seven parameters.

[0044] The beneficial effect of adopting the above further scheme is that by obtaining the suitability assessment results and correcting the current seven parameters when necessary, the accuracy of geographic transformation can be significantly improved to ensure the alignment accuracy of the orthophoto with the specified coordinate system.

[0045] In a second aspect, the present application provides a data management device based on Helava technology, comprising:

[0046] A determination module, used to determine business demand information based on the business process data of the Helawa system, wherein the business demand information includes the business links corresponding to the Helawa mapping data and the usage scenarios corresponding to the Helawa mapping data;

[0047] The first acquisition module is used to acquire the classification management rules corresponding to the Helawa mapping data based on the business demand information and system performance parameters.

[0048] In a third aspect, the present application provides an electronic device, including a processor and a memory, wherein the processor is coupled to the memory;

[0049] The processor is used to execute the computer program stored in the memory so that the electronic device performs the method as described in any one of the first aspects.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium, comprising a computer program or instructions, which, when executed on a computer, enables the computer to execute the method as described in any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a data management method based on Helava technology according to an embodiment of the present application;

[0052] Figure 2 This is a structural block diagram of a data management device based on Helava technology in an embodiment of the present application;

[0053] Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] The present application is further described in detail below in conjunction with the accompanying drawings.

[0055] The embodiment of the present application provides a data management method based on Helava technology, which can be executed by a device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, etc., but is not limited thereto.

[0056] like Figure 1 As shown, a data management method based on Helava technology is performed by an electronic device, and the main process of the method is described as follows (steps S101-S102):

[0057] Step S101: Based on the business process data of the Helawa system, business demand information is determined, where the business demand information includes the business links corresponding to the Helawa mapping data and the usage scenarios corresponding to the Helawa mapping data.

[0058] The Helava system refers to the Helava fully digital photography system, which is an advanced geographic surveying technology. The Helava system can generate orthophotos, digital ground models and three-dimensional landscape maps with stereoscopic effects from various image data through high-tech means such as satellites, aircraft, GPS (Global Positioning System), high-precision scanners and computer information processing systems, and output images and digital information in standard formats.

[0059] Through the Helawa system, you can get the Helawa survey and mapping data. The Helawa survey and mapping data are various types of geographic spatial data collected, processed and analyzed according to specific business needs and usage scenarios in the process of applying Helawa technology for geographic surveying and data processing.

[0060] The business process data of the Helava system refers to the data generated in the overall operation process corresponding to the Helava system, which may include original image data, intermediate processing data, and output result data, etc., reflecting the operation process and data processing status of the Helava system.

[0061] Business demand information refers to data requirements and usage requirements determined according to specific application scenarios and business needs when using the Helawa system for geographic surveying and data processing. In this embodiment, the business links supported by the Helawa survey data may include power grid engineering design, urban planning and management, and environmental monitoring and protection, and the usage scenarios of the Helawa survey data may include visualization and auxiliary design and construction.

[0062] By analyzing the collected business process data of the Helawa system, we can get the source, processing flow and usage scenarios of the data. At the same time, we can also obtain the data requirements and usage requirements of each business link in the business process. Based on the in-depth analysis of the business process, combined with specific application scenarios and business needs, we can determine the business links and usage scenarios that need to be supported by Helawa mapping data, thereby determining the business demand information.

[0063] Step S102: Based on the business demand information and system performance parameters, the classification management rules corresponding to the Helawa mapping data are obtained.

[0064] In this embodiment, the system performance parameters are a series of quantitative indicators used to evaluate the system performance, which characterize the capabilities and performance of the Helava system in different aspects. The system performance parameters may specifically include multiple dimensions such as system response time, throughput, number of concurrent users, and computing resource utilization.

[0065] By formulating standardized classification management rules, the consistency and accuracy of the processing of Helawa mapping data can be ensured, the complexity and uncertainty of Helawa mapping data processing can be reduced, and the processing efficiency can be improved. At the same time, it is convenient to track the processing progress and status of Helawa mapping data through classification management rules, reducing repetitive work.

[0066] By formulating the classification management rules for Helava mapping data, we can clarify the data classification principles, formulate detailed classification rules, optimize the data processing process, strengthen data reusability, and implement effect evaluation and continuous improvement. This can effectively solve the problems of low reusability of sensitive data, cumbersome processing procedures, and low work efficiency in the construction of three-dimensional visualization scenes during the accumulation of power grid engineering resources.

[0067] In this embodiment, step S102 specifically includes the following processing:

[0068] Based on the three-dimensional digitization target, the Helawa survey data is divided into first data and second data, the first data being key data for display on the three-dimensional platform;

[0069] Based on the business demand information and the system performance parameter, respectively obtain data classification granularity corresponding to the first data and the second data, wherein the data classification granularity is a parameter representing the degree of detail of the data;

[0070] Based on each of the data classification granularities, respectively obtain a data classification framework corresponding to the first data and the second data;

[0071] Based on the first data and the second data, classification management rules of the data classification framework corresponding to each are obtained, and the classification management rules include a classification basis and a classification method.

[0072] In this embodiment, the three-dimensional digitalization goal refers to converting the three-dimensional spatial information in the real world into a computer-processable data form through digital means to achieve data visualization, analysis, processing and management. In power grid projects, the three-dimensional digitalization goals can include improving the visualization effect of three-dimensional spatial information such as topography, buildings, and vegetation, optimizing the data processing process during power grid design, construction, and operation and maintenance, and enhancing the reusability and sharing of data.

[0073] According to the three-dimensional digitization goal, the Helawa survey data can be divided into the first data and the second data. The first data is the key data displayed on the three-dimensional platform, such as overhead lines, communication lines, underground lines, pipelines, river waters, traffic roads, other linear features, public facilities, power towers and communication towers, etc., which have three-dimensional spatial characteristics; the second data can be auxiliary data, such as attribute information, metadata, etc.

[0074] The electronic device stores a correspondence between the first data, the second data and the data classification granularity. The correspondence is combined with the business demand information and the system performance parameters to determine the data classification granularity corresponding to the current first data and the current second data respectively.

[0075] Exemplarily, for the first data, the corresponding data classification granularity can be a finer data classification granularity to ensure the accuracy and completeness of the data; for the second data, considering the limitations of system performance and the non-criticality of the data, a coarser data classification granularity can be selected to reduce the amount of data and improve processing efficiency.

[0076] In power grid projects, the data classification framework can include categories such as topographic data, building data, vegetation data, power grid equipment data, attribute information data, and hierarchical structures such as high resolution, low resolution, attribute level, and object level. According to the data classification granularity, the structure of the data classification hierarchy of different data can be obtained. The data classification hierarchy can include data categories, subcategories, attributes, etc. Ensure that the data classification hierarchy is clear and logically rigorous to facilitate data organization and management. And obtain the standards and rules corresponding to each data classification hierarchy. The standards and rules corresponding to the data classification hierarchy can guide the classification, storage, retrieval and use of data. Based on the data classification hierarchy and data classification standards, a data classification framework is constructed. The data classification framework can clearly reflect the classification and hierarchy of data, which is convenient for data understanding and use.

[0077] Based on the first data and the second data, classification management rules of the corresponding data classification framework are obtained. Classification management rules include classification basis and classification method. Classification basis is the basis for formulating classification management rules. For example, classification basis may include data attributes (such as topography, building type, vegetation type, etc.), characteristics (such as spatial location, shape, size, etc.), uses (such as power grid design, construction, operation and maintenance, etc.); classification method is a specific means to achieve data classification, such as line classification method, surface classification method, mixed classification method, etc., and appropriate classification method can be selected according to data characteristics and business needs.

[0078] By dividing the Helawa survey data into first data and second data, and determining the respective data classification granularity based on business demand information and system performance parameters, the data storage and processing methods can be optimized, which helps to improve the overall performance of the Helawa system while ensuring data quality. The construction of the data classification framework provides a clear hierarchy and classification standards for the organization and management of data, which helps to achieve rapid retrieval, efficient storage and convenient use of data, and optimizes the data management process in power grid projects. Through clear data classification hierarchies and classification management rules, data reuse and sharing can be achieved more easily. Different departments or projects can exchange and share data based on common data classification standards, which improves the reusability and sharing of data.

[0079] In this embodiment, after obtaining the classification management rules of the data classification framework corresponding to each other based on the first data and the second data, the following processing is also included: judging whether the real-time business process data of the Helava system meets the granularity adjustment trigger condition; if so, predicting the change trend of the business process data of the Helava system in the future based on the historical business process data, and adjusting the data classification granularity according to the change trend.

[0080] The granularity adjustment trigger condition may be preset data, and the granularity adjustment trigger condition may include one or more of the following: data access volume reaching a threshold, system performance degradation, and a significant change in business requirements.

[0081] Through the monitoring module or log system of the Helava system, relevant indicators of the Helava system business process data can be collected and analyzed in real time. By comparing the real-time monitored data with the set granularity adjustment trigger conditions, it can be determined whether the granularity adjustment trigger conditions are met.

[0082] The electronic device extracts historical business process data from the database of the Helava system, cleans and organizes it to ensure the accuracy and consistency of the data. Based on machine learning algorithms (such as support vector machines, neural networks, etc.), historical business process data can be modeled and analyzed, and the changing trend of Helava system business process data in the future can be predicted.

[0083] By real-time monitoring and predicting the change trend of the business process data of the Helava system, and adjusting the data classification granularity according to the predicted change trend, the Helava system can be more flexible to adapt to future data changes and business needs. By dynamically adjusting the data classification granularity, the data processing performance of the Helava system can be optimized to ensure that the Helava system can maintain stable operation under high load conditions.

[0084] In this embodiment, the change trend includes the fluctuation of the data volume, and the fluctuation includes the rate of change of the data volume. Adjusting the data classification granularity according to the change trend specifically includes the following processing:

[0085] If the change rate of the future data volume is a positive value and the change rate exceeds a preset rate range, a refinement rule corresponding to the data classification granularity is obtained based on the business demand information and the system performance parameter, and the data classification granularity is refined based on the refinement rule;

[0086] If the change rate does not exceed the preset rate range, or the change rate of the future data volume is negative and the change rate exceeds the rate range, then based on the business demand information and the system performance parameters, the coarsening rules corresponding to the data classification granularity are obtained, and the data classification granularity is coarsened based on the coarsening rules.

[0087] In this embodiment, a positive change rate indicates that the amount of data is constantly increasing. According to the business demand information and system performance parameters, the corresponding data classification granularity refinement rules are retrieved from the electronic device. By applying the data classification granularity refinement rules, the data classification granularity can be refined, for example, the data classification level or subclassification is increased to improve the level of detail of the data.

[0088] A negative rate of change means that the amount of data is decreasing. According to business demand information and system performance parameters, the corresponding data classification granularity coarsening rules are retrieved from the electronic device. By applying these coarsening rules, the data classification granularity can be coarsened, such as reducing the levels or subcategories of data classification to reduce the level of detail of the data.

[0089] When the amount of data increases, by refining the data classification granularity, data can be managed and processed more accurately, reducing the possibility of data redundancy and confusion, thereby improving data processing efficiency. When the amount of data decreases, by coarsening the data classification granularity, the complexity of data management can be reduced and the speed of data retrieval and processing can be improved. Dynamically adjusting the data classification granularity according to the change in data volume can ensure that the system always maintains the best performance state, reducing the possibility of system performance degradation or resource waste due to excessive or too small data volume.

[0090] In this embodiment, the method also includes the following processing: based on the constructed image automation processing model, obtaining the cropped output image corresponding to the orthophoto generated by Helava; calculating multiple evaluation index information corresponding to the output image, the multiple evaluation index information including the signal-to-noise ratio, peak signal-to-noise ratio and structural similarity index of the output image; based on the usage scenario and the multiple evaluation index information, obtaining quality assessment information corresponding to the output image; based on the quality assessment information, determining whether the output image needs to be adjusted; if not, storing the output image.

[0091] The electronic device includes a pre-built and trained image automatic processing model, which can receive the orthophoto generated by the Helawa system as input, and automatically perform processing such as cropping to generate an output image. By selecting the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as evaluation index information, the quality of the output image can be fully reflected. It is easy to understand that the signal-to-noise ratio (SNR) is the ratio of the signal to noise in the calculated image, which is used to reflect the clarity of the image; the peak signal-to-noise ratio (PSNR) is the ratio of the image quality to the noise level calculated based on the maximum possible pixel value of the output image, which is used to measure the quality of the image after compression; the structural similarity index (SSIM) is a measure of the similarity between two images, which can include three aspects: brightness, contrast and structure.

[0092] In this embodiment, when the electronic device stores specific requirements corresponding to multiple evaluation index information for different usage scenarios. By combining the usage scenario and the evaluation index information, the specific requirements for image quality can be obtained, thereby completing the quality assessment of the output image. For example, for map production that requires high-precision details, more attention can be paid to the peak signal-to-noise ratio and the structural similarity index value; while for rapid monitoring applications, the signal-to-noise ratio is more important. Based on the evaluation results, the quality assessment information of the output image is generated, and the quality assessment information may include information such as whether the usage scenario requirements are met and what quality problems exist.

[0093] According to the quality assessment information, determine whether the output image meets the requirements of the use scenario. If the quality assessment information of the output image shows that it meets the requirements of the use scenario, no adjustment is required; if not, the image automation processing model needs to be further adjusted to improve the quality of the output image. If the output image does not need to be adjusted, prepare to store it in the specified storage medium or database, perform the storage operation, and save the output image and its related information (such as file name, storage path, quality assessment information, etc.) to the specified location.

[0094] By building a good image automation processing model, the orthophotos generated by Helawa can be automatically cropped and processed, reducing the steps and time of manual operations and improving processing efficiency. By calculating multiple evaluation index information and combining the use scenario for quality assessment, it can ensure that the quality of the output image meets the requirements of the use scenario, and improve the availability and accuracy of the output image. Through automated quality assessment and judgment, unnecessary adjustment operations can be reduced, reducing adjustment costs and time costs. Storing the output images that meet the requirements can facilitate subsequent queries and use, and also help optimize storage management and improve the utilization of storage resources.

[0095] In this embodiment, the image automatic processing model includes a geographic transformation tool (ArcGIS), a reclassification tool (Reclassify), a raster to polygon tool (Raster to Polygon), a fusion tool (Dissolve) and a clipping tool (Clip).

[0096] The method of obtaining the cropped output image corresponding to the orthophoto generated by Helawa based on the constructed image automatic processing model specifically includes the following processing:

[0097] Inputting the orthophoto generated by Helawa and the target seven parameters into the geographic transformation tool, and based on the geographic transformation tool and the target seven parameters, performing geographic correction processing on the orthophoto so as to align the orthophoto with the specified coordinate system, and obtaining and outputting the corrected image;

[0098] Inputting the corrected image into the reclassification tool, reclassifying the corrected image based on the reclassification tool, replacing the target pixel values ​​in the corrected image, obtaining and outputting a reclassified image, wherein the target pixel values ​​include noise and outliers;

[0099] Input the reclassified image into the raster-to-polygon tool, convert the raster image corresponding to the reclassified image into a vector surface element based on the raster-to-polygon tool, and obtain and output a vector layer containing a plurality of the vector surface elements;

[0100] Inputting the vector layer into the fusion tool, eliminating the target surface component in the vector layer based on the fusion tool, and obtaining and outputting a cleaned vector layer;

[0101] Inputting the cleaned vector layer into the fusion tool, merging adjacent surface elements with similar attributes corresponding to the cleaned vector layer into a single surface element based on the fusion tool, and obtaining and outputting a fused vector layer;

[0102] Based on the fused vector layer, a buffer vector layer including a buffer is obtained, wherein the buffer is used to ensure that the cropped image includes complete edge information;

[0103] The planar elements of the buffer vector layer are used as a clipping template, and the corrected image is clipped based on a clipping tool and the clipping template to obtain the clipped output image.

[0104] It is easy to understand that the seven parameters include seven alignment correction parameters, which are three translation parameters, three rotation parameters and one scaling parameter. The seven parameters define the spatial transformation rules required when the orthophoto is transformed from one coordinate system to a specified coordinate system, among which the three translation parameters define the translation of the orthophoto in the X, Y, and Z directions, which ensure that the orthophoto can be correctly positioned in the transformed coordinate system (i.e., the specified coordinate system mentioned above); the three rotation parameters define the rotation angles of the orthophoto around the X, Y, and Z axes, which ensure that the orthophoto can maintain the correct direction in the transformed coordinate system; the scaling parameters are used to adjust the overall size of the orthophoto during the transformation process to ensure that the orthophoto has the correct scale in the transformed coordinate system.

[0105] Georectification improves the geographic accuracy of images by ensuring that they are aligned with a specified coordinate system.

[0106] By using the Geographic Transformation tool, the orthoimage is spatially transformed according to the seven parameters of the input target to align it with the specified coordinate system. The rectified image is the image after correction, and its geographic coordinates are consistent with the specified coordinate system.

[0107] By using the Reclassify tool, the pixel values ​​in the rectified image are replaced according to preset rules (such as threshold, range, etc.). The reclassified image is the reclassified image in which the target pixel values ​​have been replaced.

[0108] By using the Fusion tool, target surface components (such as small noise areas) in the vector layer are eliminated according to preset rules (such as area threshold, attribute similarity, etc.). The cleaned vector layer is the cleaned vector layer in which unnecessary surface components have been removed.

[0109] By using the Dissolve tool again to merge adjacent and similar face features into a single face feature, it helps to simplify the vector layer and improve the efficiency of subsequent processing. The fused vector layer is a fused vector layer in which adjacent and similar face features have been merged. Based on the fused vector layer, a buffered vector layer containing a buffer is generated. The size of the buffer can be adjusted as needed to ensure that the cropped image contains complete edge information.

[0110] The corrected image is cropped using the cropping tool according to the cropping template. The extent of the cropped output image is consistent with the surface features of the buffered vector layer.

[0111] Reclassification helps remove noise and outliers from images and improve image quality. Raster-to-polygon conversion and fusion simplify the representation of image data and reduce the complexity of subsequent processing. By generating a buffered vector layer containing a buffer, it ensures that the cropped image contains complete edge information and avoids the loss of important information. The entire processing process is automated, greatly improving processing efficiency and accuracy and reducing the need for manual intervention.

[0112] In this embodiment, before the orthophoto and target seven parameters generated by Helawa are input into the geographic transformation tool, the following processing is also included: obtaining the suitability evaluation result corresponding to the current seven parameters, the seven parameters including seven alignment correction parameters, the suitability evaluation result including the error between the corrected image data obtained by applying the current seven parameters and the actual measurement data; if the error is greater than a preset error threshold, the current seven parameters are corrected based on supplementary data to obtain corrected seven parameters, the supplementary data being public control point data or measurement data covering different geographical areas and different application scenarios; the corrected seven parameters are verified, and if the verification passes, the corrected seven parameters are used as the target seven parameters.

[0113] The parameter values ​​of the seven parameters can be preset in the initial stage. Due to the complexity of the earth's surface, measurement errors, time changes (such as crustal movement) and the differences in requirements of different application scenarios, the preset seven parameters cannot always meet the needs of high-precision conversion. Therefore, the seven parameters can be dynamically corrected in real time.

[0114] Real-time monitoring of the business process data of the Helawa system, including the generation, processing and subsequent application of orthophotos, can evaluate the conversion accuracy and applicability of the current seven parameters. By comparing the actual measurement data with the corrected images obtained by applying the current seven parameters, calculating the errors and deviations, the applicability evaluation results corresponding to the current seven parameters can be obtained. According to the applicability evaluation results, it is determined whether the current seven parameters need to be adjusted.

[0115] Public control point data can be obtained from official agencies such as the National Administration of Surveying, Mapping and Geoinformation, local surveying and mapping departments, etc. Public control point data has high accuracy and reliability and is suitable for the estimation and correction of the seven parameters. Relevant public control point data can also be obtained from academic research institutions, universities, etc. Measurement data can be obtained with high accuracy using satellite navigation systems such as GPS and Beidou. Measurement data can be collected through professional measurement equipment or related applications on smart devices such as mobile phones. Using supplementary data covering different geographical areas and application scenarios for correction can enhance the adaptability and generalization of the seven parameters, enabling them to be used under a wider range of conditions.

[0116] In the geographical area to be covered, select representative locations for field measurement to obtain coordinate data of control points or measurement points. The collected supplementary data are applied to the estimation and correction of the current seven parameters. Appropriate mathematical methods (such as least squares method, iterative optimization, etc.) can be used to estimate and correct the current seven parameters to improve the accuracy and adaptability of the seven parameters.

[0117] Electronic devices can be verified using an independent validation data set to compare the differences and accuracy improvements of the seven parameters before and after correction. By verifying the corrected seven parameters, the accuracy and reliability of the correction results can be ensured. By obtaining the suitability assessment results and correcting the current seven parameters when necessary, the accuracy of the geographic transformation can be significantly improved to ensure the alignment accuracy of the orthophoto with the specified coordinate system.

[0118] Based on the same technical concept, the present application also provides a data management device based on Helava technology, such as Figure 2 As shown, the data management device 200 based on Helava technology mainly includes:

[0119] The determination module 201 is used to determine the business demand information based on the business process data of the Helawa system, wherein the business demand information includes the business links corresponding to the Helawa mapping data and the usage scenarios corresponding to the Helawa mapping data;

[0120] The first acquisition module 202 is used to acquire the classification management rules corresponding to the Helawa mapping data based on the business demand information and system performance parameters.

[0121] Optionally, the first acquisition module 202 includes:

[0122] A division submodule, for dividing the Helawa mapping data into first data and second data based on a three-dimensional digital target, wherein the first data is key data for display on a three-dimensional platform;

[0123] A first acquisition submodule is used to acquire data classification granularities corresponding to the first data and the second data respectively based on the business demand information and the system performance parameter, where the data classification granularity is a parameter representing the detail level of the data;

[0124] A second acquisition submodule, used to acquire data classification frameworks corresponding to the first data and the second data respectively based on the data classification granularities;

[0125] A rule obtaining submodule is used to obtain classification management rules of the data classification framework corresponding to each of the first data and the second data, wherein the classification management rules include a classification basis and a classification method.

[0126] Optionally, after obtaining the rule submodule, it also includes:

[0127] The first judgment submodule is used to judge whether the real-time Helava system business process data meets the granularity adjustment trigger condition; if so, based on the historical business process data, predict the change trend of the Helava system business process data in the future, and adjust the data classification granularity according to the change trend.

[0128] Optionally, the change trend includes fluctuations in the amount of data, and the fluctuations include a rate of change in the amount of data. The first judgment submodule includes:

[0129] A third acquisition submodule is used to acquire, when the change rate of the future data volume is a positive value and exceeds a preset rate range, a refinement rule corresponding to the data classification granularity based on the business demand information and the system performance parameter, and refine the data classification granularity based on the refinement rule;

[0130] The fourth acquisition submodule is used to obtain the coarsening rules corresponding to the data classification granularity based on the business demand information and the system performance parameters when the change rate does not exceed the preset rate range, or the change rate of the future data volume is a negative value and the change rate exceeds the rate range, and coarsen the data classification granularity based on the coarsening rules.

[0131] Optionally, the device further comprises:

[0132] The second acquisition module is used to obtain the cropped output image corresponding to the orthophoto generated by Helawa based on the constructed image automation processing model;

[0133] A calculation index module, used for calculating a plurality of evaluation index information corresponding to the output image, wherein the plurality of evaluation index information includes a signal-to-noise ratio, a peak signal-to-noise ratio and a structural similarity index of the output image;

[0134] A third acquisition module, used for acquiring quality assessment information corresponding to the output image based on the usage scenario and the plurality of evaluation index information;

[0135] The judgment and adjustment module is used to judge whether the output image needs to be adjusted based on the quality assessment information; if not, store the output image.

[0136] Optionally, the image automatic processing model includes geographic transformation tools, reclassification tools, raster to polygon tools, fusion tools and clipping tools. The second acquisition module includes:

[0137] A correction submodule, for inputting the orthophoto generated by Helawa and the target seven parameters into the geographic transformation tool, and based on the geographic transformation tool and the target seven parameters, performing geographic correction processing on the orthophoto so as to align the orthophoto with the specified coordinate system, and obtaining and outputting a corrected image;

[0138] A reclassification submodule, for inputting the corrected image into the reclassification tool, reclassifying the corrected image based on the reclassification tool, replacing the target pixel values ​​in the corrected image, obtaining and outputting a reclassified image, wherein the target pixel values ​​include noise and outliers;

[0139] A conversion submodule, used for inputting the reclassified image into the raster-to-polygon tool, converting the raster image corresponding to the reclassified image into a vector surface element based on the raster-to-polygon tool, and obtaining and outputting a vector layer containing a plurality of the vector surface elements;

[0140] A cleaning submodule, used for inputting the vector layer into the fusion tool, eliminating the target surface components in the vector layer based on the fusion tool, and obtaining and outputting a cleaned vector layer;

[0141] A merging submodule, used for inputting the cleaned vector layer into the fusion tool, merging adjacent surface elements with similar attributes corresponding to the cleaned vector layer into a single surface element based on the fusion tool, and obtaining and outputting a fused vector layer;

[0142] A buffer submodule, used for obtaining a buffer vector layer including a buffer zone based on the fused vector layer, wherein the buffer zone is used for ensuring that the cropped image includes complete edge information;

[0143] The cropping submodule is used to use the planar elements of the buffer vector layer as a cropping template, and to crop the corrected image based on a cropping tool and the cropping template to obtain the cropped output image.

[0144] Optionally, before correcting the submodule, also include:

[0145] A fifth acquisition submodule is used to obtain the suitability evaluation results corresponding to the current seven parameters, wherein the seven parameters include seven alignment correction parameters, and the suitability evaluation results include the error between the corrected image data obtained by applying the current seven parameters and the actual measurement data;

[0146] A correction submodule, configured to correct the current seven parameters based on supplementary data to obtain corrected seven parameters when the applicability evaluation result shows that the error is greater than a preset error threshold, wherein the supplementary data is public control point data or measurement data covering different geographical areas and different application scenarios;

[0147] The verification submodule is used to verify the modified seven parameters. If the verification is successful, the modified seven parameters are used as the target seven parameters.

[0148] In one example, the module in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0149] For another example, when the modules in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0150] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical scheme.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0153] Based on the same technical concept, the present application also provides an electronic device, such as Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302 , and may further include an information input / information output (I / O) interface 303 , one or more of a communication component 304 , and a communication bus 305 .

[0154] The processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps in the above-mentioned data management method based on Helava technology; the memory 302 is used to store various types of data to support the operation of the electronic device 300, and these data may include, for example, instructions for any application or method used to operate on the electronic device 300, and application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), programmable read-only memory (Programmable Read-Only Memory, PROM), read-only memory (Read-Only Memory, ROM), magnetic memory, flash memory, magnetic disk or optical disk. One or more.

[0155] The I / O interface 303 provides an interface between the processor 301 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 304 is used to test the wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 304 can include: Wi-Fi components, Bluetooth components, NFC components.

[0156] The communication bus 305 may include a path to transmit information between the above components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0157] The electronic device 300 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the data management method based on Helava technology given in the above embodiment.

[0158] The electronic device 300 may include but is not limited to mobile terminals such as digital broadcast receivers, PDAs (personal digital assistants), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc., and may also be servers, etc.

[0159] Based on the same technical concept, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned data management method based on Helava technology are implemented.

[0160] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0161] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0162] In addition, 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. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0163] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0164] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A data management method based on Helava technology, characterized in that: include: Based on the business process data of the Helawa system, business demand information is determined, wherein the business demand information includes the business links corresponding to the Helawa mapping data and the usage scenarios corresponding to the Helawa mapping data; Based on the business demand information and system performance parameters, classification management rules corresponding to the Heila Wa mapping data are obtained.

2. A data management method based on Helava technology according to claim 1, characterized in that: The obtaining, based on the business demand information and the system performance parameters, the classification management rules corresponding to the Helawa mapping data includes: Based on the three-dimensional digitization target, the Helawa survey data is divided into first data and second data, the first data being key data for display on the three-dimensional platform; Based on the business demand information and the system performance parameter, respectively obtain data classification granularity corresponding to the first data and the second data, wherein the data classification granularity is a parameter representing the degree of detail of the data; Based on each of the data classification granularities, respectively obtain a data classification framework corresponding to the first data and the second data; Based on the first data and the second data, classification management rules of the data classification framework corresponding to each are obtained, and the classification management rules include a classification basis and a classification method.

3. A data management method based on Helava technology according to claim 2, characterized in that: After obtaining the classification management rules of the data classification framework corresponding to each of the first data and the second data, the method further includes: Determine whether the real-time business process data of the Helava system meets the granularity adjustment trigger condition; If so, based on the historical business process data, predict the change trend of the Helava system business process data in the future, and adjust the data classification granularity according to the change trend.

4. A data management method based on Helava technology according to claim 3, characterized in that: The change trend includes fluctuations in the amount of data, and the fluctuation includes a rate of change in the amount of data. Adjusting the data classification granularity according to the change trend includes: If the change rate of the future data volume is a positive value and the change rate exceeds a preset rate range, a refinement rule corresponding to the data classification granularity is obtained based on the business demand information and the system performance parameter, and the data classification granularity is refined based on the refinement rule; If the change rate does not exceed the preset rate range, or the change rate of the future data volume is negative and the change rate exceeds the rate range, then based on the business demand information and the system performance parameters, the coarsening rules corresponding to the data classification granularity are obtained, and the data classification granularity is coarsened based on the coarsening rules.

5. The data management method based on Helava technology according to claim 1, characterized in that: The method further comprises: Based on the constructed image automation processing model, the cropped output image corresponding to the orthophoto generated by Helawa is obtained; Calculating a plurality of evaluation index information corresponding to the output image, wherein the plurality of evaluation index information includes a signal-to-noise ratio, a peak signal-to-noise ratio, and a structural similarity index of the output image; Based on the usage scenario and the plurality of evaluation index information, obtaining quality assessment information corresponding to the output image; Based on the quality assessment information, determine whether the output image needs to be adjusted; if not, store the output image.

6. A data management method based on Helava technology according to claim 5, characterized in that: The image automation processing model includes geographic transformation tools, reclassification tools, raster to polygon tools, fusion tools and clipping tools; The method of obtaining the cropped output image corresponding to the orthophoto generated by Helawa based on the constructed image automatic processing model includes: Inputting the orthophoto generated by Helawa and the target seven parameters into the geographic transformation tool, and based on the geographic transformation tool and the target seven parameters, performing geographic correction processing on the orthophoto so as to align the orthophoto with the specified coordinate system, and obtaining and outputting the corrected image; Inputting the corrected image into the reclassification tool, reclassifying the corrected image based on the reclassification tool, replacing the target pixel values ​​in the corrected image, obtaining and outputting a reclassified image, wherein the target pixel values ​​include noise and outliers; Input the reclassified image into the raster-to-polygon tool, convert the raster image corresponding to the reclassified image into a vector surface element based on the raster-to-polygon tool, and obtain and output a vector layer containing a plurality of the vector surface elements; Inputting the vector layer into the fusion tool, eliminating the target surface component in the vector layer based on the fusion tool, and obtaining and outputting a cleaned vector layer; Inputting the cleaned vector layer into the fusion tool, merging adjacent surface elements with similar attributes corresponding to the cleaned vector layer into a single surface element based on the fusion tool, and obtaining and outputting a fused vector layer; Based on the fused vector layer, a buffer vector layer including a buffer is obtained, wherein the buffer is used to ensure that the cropped image includes complete edge information; The planar elements of the buffer vector layer are used as a clipping template, and the corrected image is clipped based on a clipping tool and the clipping template to obtain the clipped output image.

7. A data management method based on Helava technology according to claim 6, characterized in that: Before the orthophoto and target seven parameters generated by Helawa are input into the geographic transformation tool, it also includes: Obtaining suitability evaluation results corresponding to the current seven parameters, wherein the seven parameters include seven alignment correction parameters, and the suitability evaluation results include errors between corrected image data obtained by applying the current seven parameters and actual measurement data; If the error is greater than a preset error threshold, the current seven parameters are corrected based on supplementary data to obtain corrected seven parameters, wherein the supplementary data is public control point data or measurement data covering different geographical areas and different application scenarios; The modified seven parameters are verified, and if the verification passes, the modified seven parameters are used as the target seven parameters.

8. A data management device based on Helava technology, characterized in that: include: A determination module, used to determine business demand information based on the business process data of the Helawa system, wherein the business demand information includes the business links corresponding to the Helawa mapping data and the usage scenarios corresponding to the Helawa mapping data; The first acquisition module is used to acquire the classification management rules corresponding to the Helawa mapping data based on the business demand information and system performance parameters.

9. An electronic device, characterized in that: comprising a processor and a memory, wherein the processor is coupled to the memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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