Road construction method and system for topographic feature analysis

Abstract

The invention discloses a road construction method and system for topographic feature analysis, and mainly relates to the technical field of data processing. Comprising the following steps: equally dividing a target road construction route according to a preset division scale to obtain K sub-construction road sections; carrying out terrain multi-modal data acquisition to obtain a terrain multi-modal data set of K sub-construction road sections; obtaining a topographic feature set of K sub-construction road sections; k topographic feature iteration correlation coefficient sets are determined; performing sub-construction road section topographic feature fusion analysis on the K sub-construction road section topographic feature sets based on the K topographic feature iteration correlation coefficient sets to obtain K sub-construction road section fused topographic feature sets; and obtaining a target road construction scheme. The method has the advantages that the technical problem that in the prior art, deep analysis on the continuity of topographic features of different road sections is lacked, and consequently the deviation between the construction scheme and the actual situation is large is solved, and the technical effect of improving the road construction reliability is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a road construction method and system for terrain feature analysis. Background Art

[0002] Currently, terrain feature analysis in road construction usually relies on different data collection techniques, such as remote sensing technology, geological exploration, etc. These techniques can provide basic data such as the elevation, slope, soil type, and geological conditions of the terrain, providing support for road planning and construction. However, due to the complexity of terrain features and regional differences, existing terrain analysis methods often only focus on a specific type of terrain data (such as elevation, slope, etc.), ignoring the internal connections and interactions between different terrain data. Especially in areas with complex terrain or a combination of multiple terrains, a single data analysis method often cannot comprehensively reflect the terrain features, resulting in uncertainties during the construction process and affecting the accuracy and reliability of the road construction plan.

[0003] There is a technical problem in the prior art that there is a lack of in-depth analysis of the continuity of terrain features in different road sections, resulting in a large deviation between the construction plan and the actual situation. Summary of the Invention

[0004] This application provides a road construction method and system for terrain feature analysis, which is used to solve the technical problem in the prior art that there is a lack of in-depth analysis of the continuity of terrain features in different road sections, resulting in a large deviation between the construction plan and the actual situation.

[0005] In view of the above problems, this application provides a road construction method and system for terrain feature analysis.

[0006] In the first aspect of this application, a road construction method for terrain feature analysis is provided. The method includes: Equally divide the target road construction route according to a preset division scale to obtain K sub-construction sections, where K is a positive integer; Traverse the K sub-construction sections to collect terrain multi-modal data, and obtain a set of terrain multi-modal data for the K sub-construction sections; Traverse the set of terrain multi-modal data for the K sub-construction sections to perform terrain feature analysis, and obtain a set of terrain features for the K sub-construction sections; Perform forward and backward terrain feature iterative correlation analysis on the set of terrain features for the K sub-construction sections to determine a set of K terrain feature iterative correlation coefficients; Based on the set of K terrain feature iterative correlation coefficients, perform sub-construction section terrain feature fusion analysis on the set of terrain features for the K sub-construction sections to obtain a set of fused terrain features for the K sub-construction sections; Based on the set of integrated terrain features of the K sub-construction sections, identify the road construction plan to obtain the target road construction plan.

[0007] In the second aspect of this application, a road construction system for terrain feature analysis is provided. The system includes: A sub-construction section acquisition module, configured to equally divide the target road construction route according to a preset division scale to obtain K sub-construction sections, where K is a positive integer; A multi-modal data set acquisition module, configured to traverse the K sub-construction sections to collect terrain multi-modal data and obtain a set of terrain multi-modal data for the K sub-construction sections; A terrain feature set acquisition module, configured to traverse the set of terrain multi-modal data for the K sub-construction sections to perform terrain feature analysis and obtain a set of terrain features for the K sub-construction sections; An iterative correlation coefficient set acquisition module, configured to perform forward and backward terrain feature iterative correlation analysis on the set of terrain features of the K sub-construction sections to determine K sets of terrain feature iterative correlation coefficients; An integrated terrain feature set acquisition module, configured to perform sub-construction section terrain feature fusion analysis on the set of terrain features of the K sub-construction sections based on the K sets of terrain feature iterative correlation coefficients to obtain a set of integrated terrain features for the K sub-construction sections; A target road construction plan acquisition module, configured to identify a road construction plan based on the set of integrated terrain features of the K sub-construction sections to obtain the target road construction plan.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: In this application, the target road construction route is equally divided according to a preset division scale to obtain K sub-construction sections, where K is a positive integer. Then, the K sub-construction sections are traversed to collect terrain multi-modal data to obtain a set of terrain multi-modal data for the K sub-construction sections; the set of terrain multi-modal data for the K sub-construction sections is traversed to perform terrain feature analysis to obtain a set of terrain features for the K sub-construction sections. Furthermore, forward and backward terrain feature iterative correlation analysis is performed on the set of terrain features of the K sub-construction sections to determine K sets of terrain feature iterative correlation coefficients. Then, sub-construction section terrain feature fusion analysis is performed on the set of terrain features of the K sub-construction sections based on the K sets of terrain feature iterative correlation coefficients to obtain a set of integrated terrain features for the K sub-construction sections. A road construction plan is identified based on the set of integrated terrain features of the K sub-construction sections to obtain the target road construction plan. It achieves the technical effect of analyzing complex and variable terrain conditions and improving the reliability of road construction. Description of the Drawings

[0009] Appendix Figure 1It is a schematic flow chart of a road construction method for terrain feature analysis provided by an embodiment of the present invention.

[0010] Appendix Figure 2 It is a schematic structural diagram of a road construction system for terrain feature analysis provided by an embodiment of the present invention.

[0011] Reference numerals shown in the appendix: Sub - construction section acquisition module 11, multi - modal data set acquisition module 12, terrain feature set acquisition module 13, iterative correlation coefficient set acquisition module 14, fused terrain feature set acquisition module 15, target road construction plan acquisition module 16. Specific embodiments

[0012] The present invention will be further elaborated below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application. It should be noted that the terms "including" and "having" are intended to cover non - exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0013] Embodiment 1, as shown in the appendix Figure 1 The present application provides a road construction method for terrain feature analysis, wherein the method includes: S1: Evenly divide the target road construction route according to a preset division scale to obtain K sub - construction sections, where K is a positive integer; In a possible embodiment, the target road construction route refers to the overall planned route of the road to be constructed, which usually has made design decisions based on geographical and traffic requirements. The target road construction route is the core content of the construction, and all subsequent analysis, division, and construction steps are carried out based on this route. The preset division scale is a standard set in advance by those skilled in the art for segmenting the target road construction route according to construction requirements, construction efficiency, or other factors. The K sub - construction sections are the final number of divided sections. Through even division, the target road will be divided into K sub - sections, and these sub - sections will be used as individual construction units for terrain feature analysis and subsequent construction work.

[0014] First, it is necessary to equally divide the target road according to the construction route of the target road and the preset division scale, so that the construction route of the target road is divided into K relatively equal sub-construction sections. By dividing the road into multiple sub-construction sections, the analysis of each section can focus on relatively small and specific terrain data. This not only makes the acquisition of terrain features more detailed, but also ensures that the characteristics of each sub-section can be fully analyzed during the subsequent construction plan identification and construction process, ultimately improving the overall construction accuracy and reliability.

[0015] S2: Traverse the K sub-construction sections to collect terrain multi-modal data, and obtain a set of terrain multi-modal data for the K sub-construction sections; In a possible embodiment, the set of terrain multi-modal data for the K sub-construction sections is the set of all terrain data obtained after data collection for each sub-construction section. Each sub-construction section generates a set of terrain multi-modal data, which is aggregated into K sets, and each set contains all the terrain feature data of that sub-section. Preferably, any set of terrain multi-modal data for a sub-construction section includes elevation data, slope data, soil type, rock layer distribution, meteorological conditions, remote sensing images, etc., for reflecting the terrain conditions of the sub-construction section.

[0016] Specifically, traverse each sub-construction section and use different data collection techniques and tools (such as remote sensing technology, terrestrial laser scanner, UAV aerial photography, etc.) to collect terrain data in multiple modalities including elevation, slope, soil type, rock layer distribution, etc. These data together constitute the terrain feature set of each sub-construction section.

[0017] By collecting comprehensive terrain information, it provides data support for subsequent terrain feature analysis, and through multi-modal data collection, it can ensure a comprehensive analysis of the terrain features of each sub-section. Especially under complex terrain conditions, it can ensure that data in different modalities complement each other, thereby improving the accuracy and reliability of terrain analysis.

[0018] S3: Traverse the set of terrain multi-modal data for the K sub-construction sections to perform terrain feature analysis, and obtain a set of terrain features for the K sub-construction sections; In an embodiment of the present application, the set of terrain features for the K sub-construction sections refers to the set of terrain feature analysis results for each sub-construction section obtained after terrain feature analysis. Each set contains various terrain feature data of that sub-section. Optionally, each set of terrain features for a sub-construction section includes slope change, geological features, soil properties, terrain undulation, etc.

[0019] Preferably, a plurality of sample sub-construction section terrain multi-modal data sets and a plurality of sample sub-construction section terrain feature sets are obtained as training sample data, and the framework constructed based on the feed-forward neural network is supervised and trained using the training sample data, and the network parameters of the framework are continuously adjusted according to the output during the training until the training converges, and a trained terrain feature analyzer is obtained. Furthermore, the terrain feature analyzer is used to analyze the terrain features of the K sub-construction section terrain multi-modal data sets, and K sub-construction section terrain feature sets are obtained.

[0020] By deeply analyzing the terrain data of each sub-construction section, specific terrain features are obtained, providing a basis for subsequent terrain feature iterative correlation analysis and construction plan optimization. The technical effect of ensuring that the construction plan can more accurately reflect the actual terrain conditions, thereby improving the accuracy and reliability of the construction design is achieved.

[0021] S4: Perform iterative correlation analysis of the front and rear terrain features on the K sub-construction section terrain feature sets to determine K terrain feature iterative correlation coefficient sets; In an embodiment of the present application, the K terrain feature iterative correlation coefficient sets are a result set obtained by performing iterative correlation analysis of the terrain conditions of the front and rear neighboring sub-construction sections on the K sub-construction section terrain feature sets. Each sub-construction section will be compared and analyzed with the adjacent sections to obtain a specific correlation coefficient, indicating the degree of association between the terrain features of the sub-section and the front and rear sections.

[0022] Based on the K sub-construction section terrain feature sets obtained in step S3, perform front and rear correlation analysis on these terrain features. For the purpose of considering the continuity and mutual influence of the terrain features between each sub-section and the front and rear sections, through an iterative method, continuously adjust and update the degree of association between the sections. Through the iterative correlation analysis of the front and rear sections, deep-level connections between terrain features can be obtained, so as to better understand the law of terrain change.

[0023] Furthermore, perform iterative correlation analysis of the front and rear terrain features on the K sub-construction section terrain feature sets to determine K terrain feature iterative correlation coefficient sets. Step S4 of the embodiment of the present application further includes: Extract the first sub-construction section terrain feature set and the second sub-construction section terrain feature set in the K sub-construction section terrain feature sets in the order from front to back of the K sub-construction sections; Perform iterative correlation analysis of the front and rear terrain features on the first sub-construction section terrain feature set and the second sub-construction section terrain feature set to obtain a first terrain feature iterative correlation coefficient set and a second terrain feature iterative correlation coefficient set; Perform iterative correlation analysis on the topographic feature sets of the remaining K - 2 sub - construction sections before and after to obtain K - 2 sets of topographic feature iterative correlation coefficients. Combine the first set of topographic feature iterative correlation coefficients and the second set of topographic feature iterative correlation coefficients to obtain the K sets of topographic feature iterative correlation coefficients.

[0024] In a possible embodiment, the topographic feature set of the first sub - construction section refers to the topographic feature set of the first sub - construction section in the target road construction route, including all topographic data of this sub - section, such as elevation, slope, etc. The topographic feature set of the second sub - construction section refers to the topographic feature set of the second sub - construction section in the target road construction route, including all topographic feature data of this sub - section. The first set of topographic feature iterative correlation coefficients reflects the degree of association between different types of topographic features of the first construction sub - section and adjacent sections. The larger the first topographic feature iterative correlation coefficient, the greater the association of this type of topographic feature with the adjacent sub - construction section. The second set of topographic feature iterative correlation coefficients reflects the degree of association between different types of topographic features of the first construction sub - section and adjacent sections.

[0025] Preferably, by comparing the topographic features between the topographic feature set of the first sub - construction section and the topographic feature set of the second sub - construction section, calculate the degree of association between them, so as to obtain the corresponding set of topographic feature iterative correlation coefficients. Based on the same principle as obtaining the second set of topographic feature iterative correlation coefficients, perform iterative correlation analysis on the topographic feature sets of the remaining K - 2 sub - construction sections before and after to obtain K - 2 sets of topographic feature iterative correlation coefficients.

[0026] By analyzing the topographic features of each adjacent sub - section one by one, extract the association information between them, and ensure the accuracy of the correlation coefficients through iterative update. This iterative correlation analysis can better reveal the continuity or variation law of topographic features between different sections, ensure the proper fusion and connection of topographic features of different sub - construction sections, and provide an effective basis for subsequent topographic feature fusion analysis.

[0027] Furthermore, perform iterative correlation analysis on the topographic feature sets of the first sub - construction section and the second sub - construction section before and after to obtain the first set of topographic feature iterative correlation coefficients and the second set of topographic feature iterative correlation coefficients. Step S4 of the embodiment of the present application further includes: Perform overall topographic feature iterative correlation analysis on the topographic feature sets of the first sub - construction section and the second sub - construction section to obtain the first set of overall iterative correlation topographic features; Associate and identify the first overall iterative associated terrain feature set with the first sub-construction section terrain feature set and the second sub-construction section terrain feature set respectively to obtain the backward overall association coefficient of the first sub-construction section and the forward overall association coefficient of the second sub-construction section; Conduct independent terrain feature iterative association analysis on the first sub-construction section terrain feature set and the second sub-construction section terrain feature set respectively to obtain the first set of independent association coefficients, where each independent association coefficient corresponds to a terrain feature type; Integrate the backward overall association coefficient of the first sub-construction section and the first set of independent association coefficients to obtain the first set of iterative association coefficients of terrain features; Obtain the backward overall association coefficient of the second sub-construction section and the second set of independent association coefficients, and integrate the coefficients in combination with the forward overall association coefficient of the second sub-construction section and the first set of independent association coefficients to obtain the second set of iterative association coefficients of terrain features.

[0028] In an embodiment of the present application, the overall terrain feature iterative association analysis only comprehensively considers all different types of terrain features to analyze the association between the first sub-construction section and the second sub-construction section. The first overall iterative associated terrain feature set is a terrain feature set obtained after integrating the association relationships between the same-type features in the first sub-construction section terrain feature set and the second sub-construction section terrain feature set. It can also be understood as a feature set obtained by enhancing the collected second sub-construction section terrain feature set after considering the influence of the first sub-construction section terrain feature set on the second sub-construction section.

[0029] Preferably, the backward overall association coefficient of the first sub-construction section reflects the overall association degree between the first sub-construction section and the enhanced second sub-construction section. The forward overall association coefficient of the second sub-construction section reflects the overall association degree between the second sub-construction section and the enhanced second sub-construction section. The first set of independent association coefficients reflects the similarity degree between different terrain feature types in the first sub-construction section and the second sub-construction section. The first set of iterative association coefficients of terrain features is the association degree between the first sub-construction section and the second sub-construction section obtained after considering the overall similarity of the first sub-construction section and the second sub-construction section and the similarity of independent terrain features.

[0030] The second topographic feature iterative correlation coefficient set is the degree of association between the second sub-construction section and its neighboring sub-construction sections obtained by considering the overall similarity between the first and second sub-construction sections and the similarity of their independent topographic features, as well as the overall similarity between the third and second sub-construction sections and the similarity of their independent topographic features. The backward overall correlation coefficient of the second sub-construction section reflects the overall degree of association between the second sub-construction section and the third sub-construction section. The second independent correlation coefficient set reflects the degree of similarity between different types of topographic features in the third and second sub-construction sections.

[0031] Preferably, first, perform an overall topographic feature iterative correlation analysis on the topographic feature sets of the first and second sub-construction sections to obtain the first overall iterative correlation topographic feature set. Then, perform correlation recognition between this overall set and the topographic feature sets of each sub-construction section respectively to obtain the corresponding backward and forward overall correlation coefficients. Next, perform iterative correlation analysis on each topographic feature independently to obtain the independent correlation coefficient sets of the first and second sub-construction sections, and these correlation coefficients correspond to different types of topographic features, such as slope, soil type, etc. Subsequently, integrate the backward overall correlation coefficient of the first sub-construction section with its independent correlation coefficient set to obtain the final topographic feature iterative correlation coefficient set. Similarly, combine the forward overall correlation coefficient of the second sub-construction section with its independent correlation coefficient set to finally obtain the second topographic feature iterative correlation coefficient set.

[0032] Preferably, the specific process of integrating the backward overall correlation coefficient of the first sub-construction section and the first independent correlation coefficient set is to weight each first independent correlation coefficient in the first independent correlation coefficient set with a preset weight (a weight preset by those skilled in the art) and the backward overall correlation coefficient of the first sub-construction section, so as to obtain the first topographic feature iterative correlation coefficient set.

[0033] Preferably, the specific process of obtaining the backward overall correlation coefficient of the second sub-construction section and the second independent correlation coefficient set, and integrating the coefficients in combination with the forward overall correlation coefficient of the second sub-construction section and the first independent correlation coefficient set is to weight the backward overall correlation coefficient of the second sub-construction section and the second independent correlation coefficient set, as well as the forward overall correlation coefficient of the second sub-construction section and the first independent correlation coefficient set respectively with a preset weight, and calculate the mean value of the two weighted results to obtain the second topographic feature iterative correlation coefficient set.

[0034] Through iterative correlation analysis in both overall and independent ways, it is ensured that the topographic features of each sub-construction section can be accurately analyzed and integrated. These analyses not only consider the direct correlations between sub-sections, but also integrate the influencing factors of various topographic features, thus providing reliable data support for the optimization of the entire construction plan. This multi-level and multi-dimensional analysis method enhances the accuracy and adaptability of the road construction plan. Especially in complex terrain areas, it can ensure that every detail in the construction process is properly processed and optimized.

[0035] Furthermore, overall topographic feature iterative correlation analysis is performed on the topographic feature set of the first sub-construction section and the topographic feature set of the second sub-construction section to obtain the first overall iterative correlation topographic feature set. Step S4 of the embodiment of the present application further includes: Calculate the mapping similarity of the same type of topographic features between the topographic feature set of the first sub-construction section and the topographic feature set of the second sub-construction section to obtain the first iterative similarity set; Use the softmax function to standardize the first iterative similarity set and add the processing result into an initially empty matrix to obtain the first iterative matrix; Perform convolution calculation on the topographic feature set of the second sub-construction section and the first iterative matrix to obtain the first overall iterative correlation topographic feature set.

[0036] Furthermore, the first overall iterative correlation topographic feature set is respectively associated and identified with the topographic feature set of the first sub-construction section and the topographic feature set of the second sub-construction section to obtain the backward overall correlation coefficient of the first sub-construction section and the forward overall correlation coefficient of the second sub-construction section. Step S4 of the embodiment of the present application further includes: Use the cosine similarity calculation formula to calculate the overall similarity between the first overall iterative correlation topographic feature set and the topographic feature set of the first sub-construction section to obtain the first overall iterative correlation topographic feature set and the topographic feature set of the first sub-construction section respectively; Use the cosine similarity calculation formula to calculate the overall similarity between the topographic feature set of the second sub-construction section and the topographic feature set of the first sub-construction section to obtain the forward overall correlation coefficient of the second sub-construction section.

[0037] In a possible embodiment, the cosine similarity calculation formula is used to calculate the similarity of the same type of terrain features in the first sub-construction section terrain feature set and the second sub-construction section terrain feature set, and the first iteration similarity set is obtained. Among them, the first iteration similarity set reflects the similarity degree between the same type of terrain features in the first sub-construction section terrain feature set and the second sub-construction section terrain feature set. The first iteration matrix refers to the standardized matrix processed by the softmax function, and this matrix stores the standardized values of the first iteration similarity set for subsequent convolution calculations and further correlation analysis. Among them, the softmax function is ; is the standard value corresponding to the i-th first iteration similarity in the first iteration similarity set, e is the base of the natural logarithm, and m is the total number of the first iteration similarity set in the first iteration similarity set; is the similarity between the i-th same type of terrain features in the first sub-construction section terrain feature set and the second sub-construction section terrain feature set, is the i-th sub-construction section terrain feature in the first sub-construction section terrain feature set, is the i-th sub-construction section terrain feature in the second sub-construction section terrain feature set.

[0038] In a possible embodiment, multiple sample sub-construction section terrain feature sets, multiple sample iteration matrices, and corresponding multiple sample overall iteration correlation terrain feature sets are obtained as training data, and the framework constructed based on the convolutional network is supervised and trained using the training data until the training converges, and a trained convolutional calculator is obtained. The convolutional calculator is used to perform convolutional calculations on the second sub-construction section terrain feature set and the first iteration matrix to obtain the first overall iteration correlation terrain feature set.

[0039] Preferably, the cosine similarity calculation formula is used to calculate the similarity between the first sub-construction section and the first overall iteration correlation terrain feature set, and the similarity between the second sub-construction section and the first sub-construction section, respectively, to obtain the first sub-construction section backward overall correlation coefficient and the second sub-construction section forward overall correlation coefficient. These two correlation coefficients represent the correlation degree of these two sections in terms of terrain features.

[0040] Further, to obtain the second sub-construction section backward overall correlation coefficient, step S4 of this application embodiment further includes: Extract the third sub-construction section terrain feature set from the K sub-construction section terrain feature sets; Calculate the overall similarity between the topographic feature set of the third sub-construction section and the first overall iterative associated topographic feature set, and determine whether the calculation result meets the preset similarity threshold. If so, perform overall topographic feature iterative association recognition on the topographic feature set of the third sub-construction section and the first overall iterative associated topographic feature set to obtain a second overall iterative associated topographic feature set; Conduct independent topographic feature iterative association analysis on the topographic feature set of the third sub-construction section and the first overall iterative associated topographic feature set respectively to obtain a second set of independent correlation coefficients, where each independent correlation coefficient corresponds to a topographic feature type; Associate and identify the second overall iterative associated topographic feature set with the topographic feature set of the second sub-construction section to obtain the backward overall correlation coefficient of the second sub-construction section.

[0041] Furthermore, step S4 of the embodiment of the present application further includes: When the calculation result does not meet the preset similarity threshold, perform overall topographic feature iterative association recognition on the topographic feature set of the third sub-construction section and the topographic feature set of the second sub-construction section to obtain a second overall iterative associated topographic feature set; Conduct independent topographic feature iterative association analysis on the topographic feature set of the third sub-construction section and the topographic feature set of the second sub-construction section respectively to obtain a second set of independent correlation coefficients, where each independent correlation coefficient corresponds to a topographic feature type; Associate and identify the second overall iterative associated topographic feature set with the topographic feature set of the second sub-construction section to obtain the backward overall correlation coefficient of the second sub-construction section.

[0042] In a possible embodiment, the set of topographic feature data of the third sub-construction section refers to the set of topographic feature data of the third sub-construction section in the target road construction route. This set contains all the topographic feature data of this sub-section, such as elevation, slope, soil type, etc. Using the cosine similarity calculation formula, calculate the overall similarity between the set of topographic features of the third sub-construction section and the set of overall iterative associated topographic features of the first sub-construction section. Furthermore, determine whether the calculation result meets the preset similarity threshold (the minimum similarity that can be used for iterative association analysis set by those skilled in the art). If so, in accordance with the same principle of obtaining the set of overall iterative associated topographic features of the first sub-construction section, perform overall topographic feature iterative association recognition on the set of topographic features of the third sub-construction section and the set of overall iterative associated topographic features of the first sub-construction section to obtain the set of second overall iterative associated topographic features. And based on the same principle as obtaining the set of first independent association coefficients, perform independent topographic feature iterative association analysis on the set of topographic features of the third sub-construction section and the set of overall iterative associated topographic features of the first sub-construction section respectively to obtain the set of second independent association coefficients. And, based on the same principle as obtaining the backward overall association coefficient of the second sub-construction section, perform association recognition on the set of second overall iterative associated topographic features and the set of topographic features of the second sub-construction section to obtain the backward overall association coefficient of the second sub-construction section.

[0043] When the calculation result does not meet the preset similarity threshold, it indicates that the correlation between the third sub-construction section and the first sub-construction section is relatively low. Then, perform overall topographic feature iterative association recognition on the set of topographic features of the third sub-construction section and the set of topographic features of the second sub-construction section to obtain the set of second overall iterative associated topographic features. And based on the same principle as above, perform independent topographic feature iterative association analysis on the set of topographic features of the third sub-construction section and the set of topographic features of the second sub-construction section respectively to obtain the set of second independent association coefficients, where each independent association coefficient corresponds to a topographic feature type. Perform association recognition on the set of second overall iterative associated topographic features and the set of topographic features of the second sub-construction section to obtain the backward overall association coefficient of the second sub-construction section.

[0044] S5: Based on the K sets of topographic feature iterative association coefficients, perform sub-construction section topographic feature fusion analysis on the K sets of topographic features of the sub-construction sections to obtain K sets of fused topographic features of the sub-construction sections; In a possible embodiment, according to the magnitudes of the topographic feature iterative association coefficients corresponding to different topographic feature types in the K sets of topographic feature iterative association coefficients, perform forward and backward fusion analysis on the K sets of topographic features of the sub-construction sections. Exemplarily, use the coefficients in the K sets of topographic feature iterative association coefficients as weights to weight the adjacent sets of topographic features of the sub-construction sections to obtain the K sets of fused topographic features of the sub-construction sections.

[0045] S6: Identify the road construction plan based on the integrated terrain feature set of the K sub-construction sections to obtain the target road construction plan.

[0046] Further, for identifying the road construction plan based on the integrated terrain feature set of the K sub-construction sections to obtain the target road construction plan, step S6 of the embodiment of the present application further includes: Obtain multiple sample integrated terrain feature clusters of sub-construction sections and corresponding multiple sample road construction plans as training data; Use the training data to perform supervised training on the framework constructed based on the feedforward neural network until the training converges to obtain a trained plan identifier; Use the plan identifier to identify the road construction plan for the integrated terrain feature set of the K sub-construction sections to obtain the target road construction plan.

[0047] In a possible embodiment, the plan identifier refers to a system constructed based on a feedforward neural network model. After supervised learning, it can identify the corresponding construction plan according to the new terrain feature input data. The target road construction plan is the optimal construction plan planned for the target road construction route, aiming to ensure the efficiency, safety, and economy of road construction.

[0048] Preferably, first, it is necessary to identify the road construction plan based on the integrated terrain feature set of the K sub-construction sections that have been integrated. The goal of this process is to identify the construction plan most suitable for the road construction in this area according to these comprehensive terrain data. Further, to train the plan identifier, first obtain multiple sample integrated terrain feature clusters of sub-construction sections and corresponding sample road construction plans. These sample data are used as training data to help the model understand the relationship between terrain features and construction plans. By inputting these sample data into a framework constructed based on a feedforward neural network and performing supervised training until the model training converges. The training process enables the neural network to automatically adjust its weights and parameters according to the terrain feature data, thus learning how to identify the best construction plan. After training, the obtained plan identifier can identify a new road construction plan according to the integrated terrain feature set of the K sub-construction sections, and finally obtain the target road construction plan. This process of plan identification effectively matches the terrain data with the actual construction requirements, providing a reliable decision-making basis for the planning of road construction.

[0049] In summary, the embodiment of the present application has at least the following technical effects: 1. This application evenly divides the target road construction route according to a preset division scale, and collects and analyzes terrain multi-modal data for each sub-construction section, ensuring that the terrain features of each sub-section are analyzed independently and comprehensively. This method of segment-by-segment analysis can effectively avoid information loss or deviation that may occur in the overall analysis, thereby improving the accuracy of terrain feature analysis.

[0050] 2. By performing iterative correlation analysis of terrain features before and after on the terrain feature sets of K sub-construction sections, this application can identify and maintain the continuity of terrain features between different sub-sections, achieving the technical effect of effectively integrating and correlating the terrain features between sub-sections, improving the consistency of analysis results, and providing a stable and reliable data basis for the subsequent formulation of construction plans.

[0051] Embodiment 2, based on the same inventive concept as the road construction method for terrain feature analysis in the foregoing embodiment, as shown in the appendix Figure 2 This application provides a road construction system for terrain feature analysis. The system in the embodiment of this application and the method embodiment are based on the same inventive concept. Among them, the system includes: The sub-construction section acquisition module 11 is used to evenly divide the target road construction route according to a preset division scale to obtain K sub-construction sections, where K is a positive integer; The multi-modal data set acquisition module 12 is used to traverse the K sub-construction sections to collect terrain multi-modal data and obtain a terrain multi-modal data set for the K sub-construction sections; The terrain feature set acquisition module 13 is used to traverse the terrain multi-modal data sets of the K sub-construction sections to perform terrain feature analysis and obtain a terrain feature set for the K sub-construction sections; The iterative correlation coefficient set acquisition module 14 is used to perform iterative correlation analysis of terrain features before and after on the terrain feature sets of the K sub-construction sections to determine K terrain feature iterative correlation coefficient sets; The fused terrain feature set acquisition module 15 is used to perform sub-construction section terrain feature fusion analysis on the terrain feature sets of the K sub-construction sections based on the K terrain feature iterative correlation coefficient sets to obtain a fused terrain feature set for the K sub-construction sections; The target road construction plan acquisition module 16 is used to identify a road construction plan based on the fused terrain feature sets of the K sub-construction sections to obtain a target road construction plan.

[0052] Furthermore, the system is used to perform the following functions: Extract the topographic feature set of the first sub-construction section and the topographic feature set of the second sub-construction section from the topographic feature sets of the K sub-construction sections in the order from front to back; Perform forward and backward topographic feature iterative correlation analysis on the topographic feature set of the first sub-construction section and the topographic feature set of the second sub-construction section to obtain the first topographic feature iterative correlation coefficient set and the second topographic feature iterative correlation coefficient set; Perform forward and backward topographic feature iterative correlation analysis on the topographic feature sets of the remaining K - 2 sub-construction sections to obtain K - 2 topographic feature iterative correlation coefficient sets, and combine the first topographic feature iterative correlation coefficient set and the second topographic feature iterative correlation coefficient set to obtain the K topographic feature iterative correlation coefficient sets.

[0053] Furthermore, the system is used to perform the following functions: Perform overall topographic feature iterative correlation analysis on the topographic feature set of the first sub-construction section and the topographic feature set of the second sub-construction section to obtain the first overall iterative correlation topographic feature set; Perform correlation recognition on the first overall iterative correlation topographic feature set with the topographic feature set of the first sub-construction section and the topographic feature set of the second sub-construction section respectively to obtain the backward overall correlation coefficient of the first sub-construction section and the forward overall correlation coefficient of the second sub-construction section; Perform independent topographic feature iterative correlation analysis on the topographic feature set of the first sub-construction section and the topographic feature set of the second sub-construction section respectively to obtain the first independent correlation coefficient set, where each independent correlation coefficient corresponds to a topographic feature type; Integrate the backward overall correlation coefficient of the first sub-construction section and the first independent correlation coefficient set to obtain the first topographic feature iterative correlation coefficient set; Obtain the backward overall correlation coefficient of the second sub-construction section and the second independent correlation coefficient set, and integrate the forward overall correlation coefficient of the second sub-construction section and the first independent correlation coefficient set to obtain the second topographic feature iterative correlation coefficient set.

[0054] Furthermore, the system is used to perform the following functions: Calculate the mapping similarity of the same type of topographic features between the topographic feature set of the first sub-construction section and the topographic feature set of the second sub-construction section to obtain the first iterative similarity set; Use the softmax function to perform normalization processing on the first iterative similarity set and add the processing result into an initially empty matrix to obtain the first iterative matrix; Perform convolution calculation on the topographic feature set of the second sub-construction section and the first iterative matrix to obtain the first overall iterative correlation topographic feature set.

[0055] Furthermore, the system is used to perform the following functions: Calculate the overall similarity between the first overall iterative associated terrain feature set and the first sub-construction section terrain feature set by using the cosine similarity calculation formula, and obtain the first overall iterative associated terrain feature set and the first sub-construction section terrain feature set respectively; Calculate the overall similarity between the second sub-construction section terrain feature set and the first sub-construction section terrain feature set by using the cosine similarity calculation formula, and obtain the forward overall correlation coefficient of the second sub-construction section.

[0056] Furthermore, the system is used to perform the following functions: Extract the third sub-construction section terrain feature set from the K sub-construction section terrain feature sets; Calculate the overall similarity between the third sub-construction section terrain feature set and the first overall iterative associated terrain feature set, and determine whether the calculation result meets the preset similarity threshold. If so, perform overall terrain feature iterative association recognition on the third sub-construction section terrain feature set and the first overall iterative associated terrain feature set to obtain the second overall iterative associated terrain feature set; Perform independent terrain feature iterative association analysis on the third sub-construction section terrain feature set and the first overall iterative associated terrain feature set respectively to obtain the second set of independent correlation coefficients, where each independent correlation coefficient corresponds to a terrain feature type; Perform association recognition on the second overall iterative associated terrain feature set and the second sub-construction section terrain feature set to obtain the backward overall correlation coefficient of the second sub-construction section.

[0057] Furthermore, the system is used to perform the following functions: When the calculation result does not meet the preset similarity threshold, perform overall terrain feature iterative association recognition on the third sub-construction section terrain feature set and the second sub-construction section terrain feature set to obtain the second overall iterative associated terrain feature set; Perform independent terrain feature iterative association analysis on the third sub-construction section terrain feature set and the second sub-construction section terrain feature set respectively to obtain the second set of independent correlation coefficients, where each independent correlation coefficient corresponds to a terrain feature type; Perform association recognition on the second overall iterative associated terrain feature set and the second sub-construction section terrain feature set to obtain the backward overall correlation coefficient of the second sub-construction section.

[0058] Furthermore, the system is used to perform the following functions: Obtain multiple sample sub-construction section integrated terrain feature clusters and corresponding multiple sample road construction plans as training data; Supervise and train the framework constructed based on the feedforward neural network using the training data until the training converges to obtain a trained solution recognizer; Use the solution recognizer to identify the road construction plan for the set of integrated terrain features of K sub-construction road sections to obtain the target road construction plan.

[0059] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0061] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A road construction method for terrain feature analysis, characterized in that The method includes: Equally divide the target road construction route according to a preset division scale to obtain K sub-construction sections, where K is a positive integer; Traverse the K sub-construction sections to collect terrain multi-modal data, and obtain a set of terrain multi-modal data for the K sub-construction sections; Traverse the set of terrain multi-modal data for the K sub-construction sections to perform terrain feature analysis, and obtain a set of terrain features for the K sub-construction sections; Perform iterative correlation analysis of front and rear terrain features on the set of terrain features for the K sub-construction sections to determine K sets of iterative correlation coefficients for terrain features; Based on the K sets of iterative correlation coefficients for terrain features, perform fusion analysis of terrain features for the sub-construction sections on the set of terrain features for the K sub-construction sections to obtain a set of fused terrain features for the K sub-construction sections; Based on the set of fused terrain features for the K sub-construction sections, identify a road construction plan to obtain the target road construction plan.

2. The road construction method for terrain feature analysis according to claim 1, wherein Performing iterative correlation analysis of front and rear terrain features on the set of terrain features for the K sub-construction sections to determine K sets of iterative correlation coefficients for terrain features includes: Extract the terrain feature set of the first sub-construction section and the terrain feature set of the second sub-construction section from the set of terrain features for the K sub-construction sections in the order from front to back; Perform iterative correlation analysis of front and rear terrain features on the terrain feature set of the first sub-construction section and the terrain feature set of the second sub-construction section to obtain the first set of iterative correlation coefficients for terrain features and the second set of iterative correlation coefficients for terrain features; Perform iterative correlation analysis of front and rear terrain features on the remaining K - 2 sets of terrain features for the sub-construction sections to obtain K - 2 sets of iterative correlation coefficients for terrain features, and combine the first set of iterative correlation coefficients for terrain features and the second set of iterative correlation coefficients for terrain features to obtain the K sets of iterative correlation coefficients for terrain features.

3. The road construction method for terrain feature analysis according to claim 2, characterized in that, Performing iterative correlation analysis of front and rear terrain features on the terrain feature set of the first sub-construction section and the terrain feature set of the second sub-construction section to obtain the first set of iterative correlation coefficients for terrain features and the second set of iterative correlation coefficients for terrain features includes: Perform overall terrain feature iterative correlation analysis on the terrain feature set of the first sub-construction section and the terrain feature set of the second sub-construction section to obtain the first set of overall iterative correlated terrain features; Perform correlation identification on the first set of overall iterative correlated terrain features with the terrain feature set of the first sub-construction section and the terrain feature set of the second sub-construction section respectively to obtain the backward overall correlation coefficient of the first sub-construction section and the forward overall correlation coefficient of the second sub-construction section; Perform independent terrain feature iterative correlation analysis on the terrain feature set of the first sub-construction section and the terrain feature set of the second sub-construction section respectively to obtain the first set of independent correlation coefficients, where each independent correlation coefficient corresponds to a terrain feature type; Integrate the backward overall correlation coefficient of the first sub-construction section and the first set of independent correlation coefficients to obtain the first set of iterative correlation coefficients for terrain features; Obtain the second sub-construction section's backward overall correlation coefficient and the second set of independent correlation coefficients. Combine the second sub-construction section's forward overall correlation coefficient and the first set of independent correlation coefficients for coefficient integration to obtain the second set of terrain feature iterative correlation coefficients.

4. The road construction method for terrain feature analysis according to claim 3, characterized in that, Conduct an overall terrain feature iterative correlation analysis on the terrain feature sets of the first sub-construction section and the second sub-construction section to obtain the first overall iterative correlation terrain feature set, including: Calculate the mapping similarity of the same type of terrain features between the terrain feature set of the first sub-construction section and the terrain feature set of the second sub-construction section to obtain the first set of iterative similarities; Use the softmax function to standardize the first set of iterative similarities and add the processing results into an initially empty matrix to obtain the first iterative matrix; Perform convolution calculation on the terrain feature set of the second sub-construction section and the first iterative matrix to obtain the first overall iterative correlation terrain feature set.

5. The road construction method for terrain feature analysis according to claim 3, characterized in that, Perform correlation identification on the first overall iterative correlation terrain feature set with the terrain feature sets of the first sub-construction section and the second sub-construction section respectively to obtain the backward overall correlation coefficient of the first sub-construction section and the forward overall correlation coefficient of the second sub-construction section, including: Use the cosine similarity calculation formula to calculate the overall similarity between the first overall iterative correlation terrain feature set and the terrain feature set of the first sub-construction section to obtain the first overall iterative correlation terrain feature set and the terrain feature set of the first sub-construction section respectively; Use the cosine similarity calculation formula to calculate the overall similarity between the terrain feature set of the second sub-construction section and the terrain feature set of the first sub-construction section to obtain the forward overall correlation coefficient of the second sub-construction section.

6. The road construction method for terrain feature analysis according to claim 3, characterized in that, Obtain the backward overall correlation coefficient of the second sub-construction section, including: Extract the terrain feature set of the third sub-construction section from the K terrain feature sets of the sub-construction sections; Calculate the overall similarity between the terrain feature set of the third sub-construction section and the first overall iterative correlation terrain feature set, and determine whether the calculation result meets the preset similarity threshold. If so, conduct an overall terrain feature iterative correlation identification on the terrain feature set of the third sub-construction section and the first overall iterative correlation terrain feature set to obtain the second overall iterative correlation terrain feature set; Conduct an independent terrain feature iterative correlation analysis on the terrain feature set of the third sub-construction section and the first overall iterative correlation terrain feature set respectively to obtain the second set of independent correlation coefficients, where each independent correlation coefficient corresponds to a terrain feature type; Perform correlation identification on the second overall iterative correlation terrain feature set and the terrain feature set of the second sub-construction section to obtain the backward overall correlation coefficient of the second sub-construction section.

7. The road construction method for terrain feature analysis according to claim 6, characterized in that, It also includes: When the calculation result does not meet the preset similarity threshold, conduct an overall terrain feature iterative correlation identification on the terrain feature set of the third sub-construction section and the terrain feature set of the second sub-construction section to obtain the second overall iterative correlation terrain feature set; Perform independent terrain feature iterative correlation analysis on the terrain feature sets of the third sub-construction section and the second sub-construction section respectively to obtain a second set of independent correlation coefficients, where each independent correlation coefficient corresponds to a terrain feature type; Perform correlation identification on the second overall iterative correlation terrain feature set and the terrain feature set of the second sub-construction section to obtain the backward overall correlation coefficient of the second sub-construction section.

8. The road construction method for terrain feature analysis according to claim 1, wherein Based on the fused terrain feature sets of the K sub-construction sections, identify a road construction plan to obtain a target road construction plan, including: Obtain multiple sample fused terrain feature clusters of sub-construction sections and corresponding multiple sample road construction plans as training data; Use the training data to perform supervised training on a framework constructed based on a feedforward neural network until the training converges to obtain a trained plan identifier; Use the plan identifier to identify a road construction plan for the fused terrain feature sets of the K sub-construction sections to obtain the target road construction plan.

9. A road construction system for terrain feature analysis, characterized in that The system is used to implement a road construction method for terrain feature analysis according to any one of claims 1-8. The system includes: A sub-construction section obtaining module, configured to evenly divide a target road construction route according to a preset division scale to obtain K sub-construction sections, where K is a positive integer; A multi-modal data set obtaining module, configured to traverse the K sub-construction sections to collect terrain multi-modal data to obtain a set of terrain multi-modal data for the K sub-construction sections; A terrain feature set obtaining module, configured to traverse the set of terrain multi-modal data for the K sub-construction sections to perform terrain feature analysis to obtain a set of terrain features for the K sub-construction sections; An iterative correlation coefficient set obtaining module, configured to perform forward and backward terrain feature iterative correlation analysis on the set of terrain features of the K sub-construction sections to determine K sets of terrain feature iterative correlation coefficients; A fused terrain feature set obtaining module, configured to perform sub-construction section terrain feature fusion analysis on the set of terrain features of the K sub-construction sections based on the K sets of terrain feature iterative correlation coefficients to obtain a set of fused terrain features for the K sub-construction sections; A target road construction plan obtaining module, configured to identify a road construction plan based on the set of fused terrain features of the K sub-construction sections to obtain a target road construction plan.

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