A GIS-based integrated management system for geographical information of pipe networks

Abstract

The present invention discloses a GIS-based integrated management system for pipe network geographic information, which relates to the field of geographic information technology. The pipe network information acquisition module can accurately extract detailed attribute information of graphic elements in each layer from the GIS management system, and generate feature vectors after preprocessing, so as to lay a solid data foundation for subsequent analysis. The layer display module can determine the overlap rate of graphic elements between layers and analyze the overall overlap distribution through a rigorous calculation process, and then determine whether there is a visual conflict hotspot area. On this basis, the inter-layer interference analysis module quantitatively calculates the visual conflict measurement coefficient and the logical correlation coefficient from the perspective of appearance and internal association, and deeply analyzes the multi-dimensional relationship between layers. The comprehensive analysis module uses deep learning to build a model, integrates the above coefficients to output a comprehensive conflict measurement index, and comprehensively evaluates the overall coordination performance between layers.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a GIS-based integrated management system for geographical information of a pipe network. Background Art

[0002] With the acceleration of urbanization, the underground pipe network system is becoming increasingly complex, covering various types of pipeline networks such as water supply, drainage, gas, and heat, and intertwined with urban roads, buildings and other geographical elements. In terms of multi-layer data processing, there are many challenges. A more specific visual conflict is that when multiple layers (such as water supply pipeline layer, drainage pipeline layer, road layer, etc.) are superimposed and displayed, the graphics may cover each other, affecting the user's observation and understanding of the overall layout of the pipe network. In terms of data logic conflicts, data in different layers may have logical associations. If these associations are not properly handled during data update or editing, data inconsistency may occur. For example, when an urban road is expanded, the road layer changes, and if the underground pipe network layer (such as gas pipelines and communication pipelines) that intersects with it does not update its spatial relationship with the road in time, the actual location of the pipe network may not match the location displayed on the map. These problems seriously affect the accuracy, efficiency and practicality of the GIS-based pipe network geographic information integrated management system, and need to be solved urgently.

[0003] As for the above-mentioned specific visual conflicts and data logic conflicts, the current traditional management methods of pipe network geographic information have obvious shortcomings. In terms of visual conflict processing, most existing systems do not conduct in-depth analysis and effective processing of the overlapping of graphic elements, which causes users to spend a lot of time and energy to distinguish the obscured parts when viewing pipe network data, reducing work efficiency. In addition, there is a logical association between data in different layers, but improper processing may lead to data inconsistency during update or editing, affecting the overall coordination performance between layers. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a GIS-based integrated management system for geographical information of pipe networks, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a GIS-based pipe network geographical information integrated management system, including a pipe network information acquisition module, a layer display module, an inter-layer interference analysis module, a comprehensive analysis module and a feedback and operation and maintenance module;

[0006] The pipe network information acquisition module is used to obtain the layer attribute information of different graphic elements in each layer according to the layer list in the SWSoft-Pipeline Geographic Information GIS management system, and generate the attribute feature vector of each layer after preprocessing;

[0007] The layer display module is used to determine the overlap rate Cdz of the graphic elements in each region between the layers when displaying multiple layers for observation at the same time, using the attribute feature vector of each layer, and based on the overlap rate Cdz, judge the spatial aggregation between the layers to identify whether there is a visual conflict hot spot area;

[0008] The inter-layer interference analysis module is used to analyze the visual conflict between layers and the logical association strength between layers based on the existence of visual conflict hotspot areas, so as to obtain the visual conflict measurement coefficient Scxs and the logical association coefficient Lgxs;

[0009] The comprehensive analysis module is used to construct a layer combination evaluation model using deep learning technology, and input the visual conflict measurement coefficient Scxs and the logical association coefficient Lgxs into the layer combination evaluation model, and after linear normalization processing, fit and output the comprehensive conflict measurement index Zczs;

[0010] The feedback and operation and maintenance module is used to pre-set the evaluation threshold K, and compare and analyze it with the comprehensive conflict measurement index Zczs to comprehensively judge the overall coordination performance between the currently displayed layers, and execute corresponding operation and maintenance measures based on the comprehensive judgment results.

[0011] Preferably, the pipe network information acquisition module includes a layer information unit and a preprocessing unit;

[0012] The layer information unit is used to log in to SWSoft-Pipeline Geographic Information GIS Management System through a browser, and select "Map Operation" in the menu bar of SWSoft-Pipeline Geographic Information GIS Management System to pull down and display a function menu, wherein the function menu includes zoom in, zoom out, full map, measure distance, measure area, clear measurement, bookmark, view print and layer list, and then by selecting the pipe network layer in the layer list, determine the layer to be displayed currently, and through the "Query Statistics" in the menu bar, obtain the layer attribute information of different graphic elements in the layer to be presented currently, and the layer attribute information of different graphic elements in the layer to be presented currently includes the position coordinates, pipeline size, pipeline characteristics, laying age, display attributes, creation time, update time and identification attributes of different graphic elements in the layer to be presented currently; wherein the pipe network layer includes a water supply pipeline layer, a drainage pipeline layer, a gas pipeline layer, a thermal pipeline layer and ancillary facilities layer;

[0013] The preprocessing unit is used to perform data preprocessing on the layer attribute information of different graphic elements in the layer to be currently presented, pair the features of each dimension in the same order to construct the attribute feature vector of each layer, and mark the attribute feature vector of each layer.

[0014] Preferably, the layer display module includes a visual presentation unit, an inter-layer distribution unit and a hot spot identification unit;

[0015] The visual presentation unit is used to select multiple groups of layers in the SWSoft-pipeline network geographic information GIS management system, and superimpose the selected multiple groups of layers to present the multiple groups of layers to be displayed, and divide the multiple groups of layers into several groups of detection areas in the same way. According to the detection areas divided by each layer, the area pairs at the same position between each layer are determined, and when multiple groups of layers are displayed for observation at the same time, the attribute feature vectors of each layer are used to determine the overlap rate Cdz of the graphic elements in each area pair between the layers, which is specifically obtained in the following way:

[0016] ;

[0017] In the formula, Indicates the overlap rate of graphic elements within the jth region between layers, Represents the overlapping area of ​​graphic elements within the jth region between layers. It represents the area of ​​graphic elements in the detection area corresponding to the j-th region pair between layers in the i-th layer. It represents the area of ​​graphic elements in the detection area corresponding to the j-th region pair in the k-th layer, i and k represent the corresponding layer numbers, and j represents the corresponding region pair number.

[0018] Preferably, the inter-layer distribution unit is used to analyze the overall overlapping distribution between layers according to the overlapping rate Cdz of the graphic elements in each region between the layers obtained in the visual presentation unit, so as to calculate and obtain the spatial distribution coefficient Kfxs, and the spatial distribution coefficient Kfxs is obtained by the following formula:

[0019] ;

[0020] In the formula, j and g represent the corresponding region pair numbers; represents the total number of region pairs; represents the spatial weight matrix element; Indicates the overlap ratio of graphic elements within the jth region between layers; Indicates the overlap ratio of graphic elements within the g-th region between layers; represents the average overlap ratio among all region pairs.

[0021] Preferably, the hot spot identification unit is used to identify whether there is a hot spot area with visual conflict. According to the calculation method of the spatial distribution coefficient Kfxs obtained in the inter-layer distribution unit, it is known that the value range of the spatial distribution coefficient Kfxs is When the spatial distribution coefficient Kfxs=1, it means that the spatial distribution is random; when the spatial distribution coefficient Kfxs>1, it means that there is a negative spatial correlation, that is, there is no aggregation between the layers; when the spatial distribution coefficient Kfxs<1, it means that there is a positive spatial correlation, that is, there is aggregation between the layers. At this time, it will be determined that there is a hot spot area with visual conflict between the current layers.

[0022] Preferably, the inter-layer interference analysis module includes a representation analysis unit and an internal association unit;

[0023] The image analysis unit is used to extract the spatial coordinate points of different graphic elements in the corresponding layer from the attribute feature vectors of each layer based on the existence of the visual conflict hotspot area. ; and the spatial coordinate points of different graphic elements in the corresponding layer , converted to spherical coordinates to mark the spherical coordinate points of different graphic elements in the corresponding layer , and from the spherical coordinate points of different graphic elements in the corresponding layer Extract the spherical coordinate points of overlapping graphic elements between layers , based on the spherical coordinate points of overlapping graphic elements between layers , analyze the visual conflict between layers, calculate and obtain the visual conflict measurement coefficient Scxs, and the visual conflict measurement coefficient Scxs is obtained by the following formula:

[0024] ;

[0025] Where m represents the number of the overlapping graphic elements, M represents the number of overlapping graphic elements, Represents the spherical coordinate point of the mth overlapping graphic element in the i-th layer, express The radial distance in express The azimuth in express The elevation angle in Represents the spherical coordinate point of the mth overlapping graphic element in the kth layer, express The radial distance in express The azimuth in express The elevation angle in represents pi, Indicates the weight value.

[0026] Preferably, the internal association unit is used to divide the feature values ​​in the attribute feature vector of each layer into different attribute categories, and define an attribute classification function based on a , attribute classification function Indicates that the index a of the corresponding attribute category is returned according to the value of a to analyze the logical association strength between layers and obtain the logical association coefficient Lgxs. The logical association coefficient Lgxs is obtained by the following formula:

[0027] ;

[0028] In the formula, a represents the index of the attribute category, A represents the number of attribute categories, c represents the index of the feature value under the corresponding attribute category, and G represents the number of feature values ​​under the corresponding attribute category. represents the cth eigenvalue under the ath attribute condition in the layer attribute feature vector of the i-th layer, Represents the cth eigenvalue under the ath attribute condition in the layer attribute feature vector of the kth layer, Indicates the adjustment parameter.

[0029] Preferably, the comprehensive analysis module is used to construct an initial model using deep learning technology, and train and test the initial model with layer attribute information of different graphic elements in each layer, and use the trained initial model as a state recognition model, respectively obtain feature information in the state recognition model, and train and test the state recognition model with the acquired feature information, and then use the trained state recognition model as a layer combination evaluation model, and after the trained layer combination evaluation model, fit and output a comprehensive conflict measurement index Zczs, and the comprehensive conflict measurement index Zczs is obtained in the following manner:

[0030] ;

[0031] In the formula, represents the logical association coefficient, represents the visual conflict measurement coefficient, and The logical correlation coefficients are and visual conflict metric coefficient The weight value of and The specific value is set by the user according to the situation.

[0032] Preferably, the feedback and operation and maintenance module is used to pre-set an evaluation threshold K, and compare and analyze it with the comprehensive conflict measurement index Zczs to comprehensively judge the overall coordination performance between the currently displayed layers. The specific judgment content is as follows:

[0033] If the comprehensive conflict measurement index Zczs exceeds the evaluation threshold K, it is comprehensively judged that the overall coordination performance between the currently displayed layers is in an abnormal state. At this time, the display level of each layer will be adjusted, the graphic elements of one of the layers will be set to be semi-transparent, and prompt information will be added to the hot spot area of ​​visual conflict. The prompt information includes text labels and icons. At the same time, the layer attribute information of each layer in the pipe network layer will be re-updated and checked, and logical relationship prompt information will be added to each layer;

[0034] If the comprehensive conflict measurement index Zczs does not exceed the evaluation threshold K, it is comprehensively judged that the overall coordination performance between the currently displayed layers is not in an abnormal state. At this time, the user's feedback on the layer display and system functions will be actively collected. By setting a feedback button on the system interface and sending user questionnaires regularly, the user's feelings and suggestions on the coordination performance between layers during use can be understood.

[0035] The present invention provides a GIS-based integrated management system for geographical information of pipe networks, which has the following beneficial effects:

[0036] (1) The pipeline information acquisition module can systematically obtain the rich layer attribute information of different graphic elements in each layer from the SWSoft-Pipeline Geographic Information GIS Management System, and generate the attribute feature vector of each layer through preprocessing. This process not only ensures the integrity of data collection, but also lays a solid data foundation for subsequent in-depth analysis through the construction of feature vectors, so that the system can accurately quantify and structure the pipeline geographic information, avoiding management decision errors caused by missing or confused data. The layer display module can keenly judge the spatial aggregation between layers by accurately calculating the overlap rate Cdz of graphic elements in each region between layers, so as to accurately identify the hot spots of visual conflict. This helps operators to quickly focus on the key areas with visual conflict problems in the complex multi-layer pipeline information display, discover potential graphic display confusion or information occlusion problems in advance, improve the clarity and accuracy of pipeline layout visualization, and thus improve work efficiency and scientific decision-making. Based on the determination of the hotspot area of ​​visual conflict, the system can further analyze the visual conflict situation and obtain the visual conflict measurement coefficient Scxs. This coefficient provides a reliable basis for quantifying the degree of visual conflict, so that managers can intuitively understand the severity of visual conflicts in different layer combinations or regions, and take targeted adjustment measures, such as optimizing the layer display order, adjusting the graphic element style, etc., to improve the visualization effect of pipe network information and reduce the risk of information misunderstanding or misjudgment caused by visual interference. While analyzing the visual conflict, the inter-layer interference analysis module deeply explores the logical association strength between layers and obtains the logical association coefficient Lgxs, which helps to fully understand the internal connection and interaction between the various elements of the pipe network and ensure the consistency and accuracy of the data at the logical level. The comprehensive analysis module uses the layer combination evaluation model constructed by deep learning technology to integrate the visual conflict measurement coefficient Scxs and the logical association coefficient Lgxs, and output the comprehensive conflict measurement index Zczs. This index can comprehensively and objectively reflect the overall coordination performance between the currently displayed layers, and provides a comprehensive performance evaluation indicator for the system. Through this indicator, managers can monitor and evaluate the operation status of the pipe network geographic information system in real time and discover potential problems and risk points in time. The feedback and operation and maintenance module compares and analyzes the pre-set evaluation threshold K and the comprehensive conflict measurement index Zczs, and can automatically and intelligently judge the overall coordination performance status of the system, and execute corresponding operation and maintenance measures based on the judgment results. When the index exceeds the threshold, the system can automatically trigger operations such as data update inspection, layer display adjustment, and logical relationship optimization to ensure that the system always maintains a good operating state, reduce manual intervention and operation and maintenance costs, improve the automation level and reliability of pipe network geographic information management, and ensure the stable and efficient operation of the urban pipe network system.

[0037] (2) Compared with some of the current traditional pipe network geographic information management technologies, this system has significant improvements. In terms of data acquisition and processing, it is no longer limited to simple data collection, but has carried out in-depth preprocessing to make the data format unified and the features orderly, which is more conducive to subsequent complex calculations and analysis, and avoids analysis errors caused by data clutter. For the judgment of visual conflicts, it has gotten rid of the limitations of relying solely on subjective feelings or rough observations in the past. Relying on scientific calculation methods and quantitative indicators, it accurately locks the hot spots of visual conflicts and measures the degree of conflict, thereby improving the accuracy and objectivity of judgment as much as possible. In the mining of logical relationships between layers, through detailed attribute classification and rigorous coefficient calculation, the hidden logical associations are deeply explored to change the past situation of insufficient grasp of logical relationships and make the connection between the various elements of the pipe network clearer.

[0038] (3) The visual presentation unit uses rigorous regional division and overlap rate calculation to obtain an overlap rate Cdz that can accurately quantify the degree of overlap of graphic elements. This precise quantitative result enables network managers to objectively judge the visual conflict situation based on data rather than relying solely on subjective feelings. For example, in urban planning, when considering the relationship between new construction projects and existing network layouts, the overlap rate Cdz can be used to clearly understand the overlap between network and building layers in different regions, thereby accurately assessing the possible impact of construction on the network, planning protection or diversion measures in advance, and avoiding blind construction that causes damage to the network and causes water and gas outages, thereby ensuring the stable operation of urban infrastructure and providing reliable data support for urban construction decisions. The spatial distribution coefficient Kfxs calculated by the inter-layer distribution unit further displays the overlapping distribution pattern of layers from a macro perspective, which helps managers quickly grasp the overall visual conflict situation and determine the focus of resource allocation. For example, when allocating resources for pipeline maintenance, if the spatial distribution coefficient Kfxs of a certain area shows that there are concentrated visual conflict hotspots, human and material resources can be prioritized to conduct detailed inspections and optimizations of the area, such as adjusting the direction of the pipeline network or optimizing the layer display settings, improving maintenance efficiency, reducing maintenance costs, ensuring that the pipeline system operates efficiently with limited resources, and ensuring the normal functioning of urban functions.

[0039] (4) Through the attribute classification function, the complex layer attribute features can be divided in an orderly manner. For example, in pipeline network management, the categories such as pipeline material, laying time, and pressure level can be clearly distinguished. This makes the research on the logical association between layers no longer blind, and can focus on specific attribute categories to explore their internal connections in a targeted manner, thereby improving the accuracy and efficiency of the analysis. Second, the logical association coefficient Lgxs provides a quantitative decision-making basis. Its calculation formula integrates multiple factors, and the exponential function's nonlinear measurement of attribute differences can keenly capture the impact of even small attribute changes on logical associations. For example, when comparing the gas pipeline and thermal pipeline layers, it can accurately reflect the degree of influence of the subtle differences in pipeline pressure (attribute category) between the two on the overall logical association, allowing managers to grasp the logical relationship more meticulously. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is a block diagram of a GIS-based integrated management system for geographical information of pipe networks. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Example 1

[0043] See also Figure 1 , the present invention provides a GIS-based pipe network geographical information integrated management system, including a pipe network information acquisition module, a layer display module, an inter-layer interference analysis module, a comprehensive analysis module and a feedback and operation and maintenance module;

[0044] The pipe network information acquisition module is used to obtain the layer attribute information of different graphic elements in each layer according to the layer list in the SWSoft-Pipeline Geographic Information GIS management system, and generate the attribute feature vector of each layer after preprocessing;

[0045] The layer display module is used to determine the overlap rate Cdz of the graphic elements in each region between the layers when displaying multiple layers for observation at the same time, using the attribute feature vector of each layer, and based on the overlap rate Cdz, judge the spatial aggregation between the layers to identify whether there is a visual conflict hot spot area;

[0046] The inter-layer interference analysis module is used to analyze the visual conflict between layers and the logical association strength between layers based on the existence of visual conflict hotspot areas, so as to obtain the visual conflict measurement coefficient Scxs and the logical association coefficient Lgxs;

[0047] The comprehensive analysis module is used to construct a layer combination evaluation model using deep learning technology, and input the visual conflict measurement coefficient Scxs and the logical association coefficient Lgxs into the layer combination evaluation model, and after linear normalization processing, fit and output the comprehensive conflict measurement index Zczs;

[0048] The feedback and operation and maintenance module is used to pre-set the evaluation threshold K, and compare and analyze it with the comprehensive conflict measurement index Zczs to comprehensively judge the overall coordination performance between the currently displayed layers, and execute corresponding operation and maintenance measures based on the comprehensive judgment results.

[0049] During the operation of this system, the GIS-based pipe network geographic information integrated management system has completed a number of key tasks through the coordinated operation of five core modules. The pipe network information acquisition module can accurately extract the detailed attribute information of the graphic elements in each layer from the SWSoft-pipe network geographic information GIS management system, and generate feature vectors after preprocessing, laying a solid data foundation for subsequent analysis. The layer display module can determine the overlap rate of graphic elements between layers and analyze the overall overlap distribution through a rigorous calculation process, and then determine whether there is a visual conflict hotspot area, so that potential visual problems can be presented intuitively. On this basis, the inter-layer interference analysis module quantifies the visual conflict measurement coefficient and the logical correlation coefficient from the perspective of appearance and internal association, and deeply analyzes the multi-dimensional relationship between layers. The comprehensive analysis module uses deep learning to build a model, integrates the above coefficients to output a comprehensive conflict measurement index, and comprehensively evaluates the overall coordination performance between layers. The feedback and operation and maintenance module compares the index according to the set threshold, and executes the corresponding operation and maintenance measures as needed to ensure that the system is always in a good operating state.

[0050] Example 2

[0051] Please refer to Figure 1 ,Specifically: the pipe network information acquisition module includes a layer information unit and a preprocessing unit;

[0052] The layer information unit is used to log in to SWSoft-Pipeline Network Geographic Information GIS Management System through a browser, and select "Map Operation" in the menu bar of SWSoft-Pipeline Network Geographic Information GIS Management System to pull down and display a function menu, wherein the function menu includes zoom in, zoom out, full map, measure distance, measure area, clear measurement, bookmark, view print and layer list, and then by selecting the pipe network layer in the layer list, determine the layer to be displayed currently, and through the "Query Statistics" in the menu bar, obtain the layer attribute information of different graphic elements in the layer to be presented currently, and the layer attribute information of different graphic elements in the layer to be presented currently includes the position coordinates, pipeline size, pipeline characteristics, laying age, display attributes (such as color, transparency, line style), creation time, update time and identification attributes of different graphic elements in the layer to be presented currently; wherein the pipe network layer includes a water supply pipeline layer, a drainage pipeline layer, a gas pipeline layer, a thermal pipeline layer and ancillary facilities layer;

[0053] The preprocessing unit is used to preprocess the layer attribute information of different graphic elements in the layer to be presented, pair the dimensional features in the same order to construct the attribute feature vectors of each layer, and mark the attribute feature vectors of each layer; the benefit of pairing is that it lays a solid foundation for the subsequent one-to-one calculation. When performing comprehensive analysis, such as calculating a certain correlation or difference index between different pipeline layers, mathematical operations can be directly performed on the paired feature values.

[0054] In this embodiment, through the operation process of the layer information unit, the browser can be used to conveniently log in to the SWSoft-Pipeline Geographic Information GIS Management System and obtain rich layer information. This method breaks the platform limitations and operational complexity that may exist in traditional geographic information systems in data acquisition, allowing pipeline network managers to quickly start data query and analysis work in a familiar browser environment, greatly improving work efficiency and reducing time waste caused by system switching and complex operation processes. The diverse operation options in the function menu, such as zoom in, zoom out, full map, etc., provide users with flexible view control capabilities when browsing the pipeline network layer. Whether it is an in-depth view of the details of the local pipeline network or a macro grasp of the overall pipeline network layout, it can be easily achieved, effectively improving the user's visual perception and understanding of the pipeline network geographic information, and helping to more accurately discover potential problems and make decision-making analysis. This module can obtain extensive and detailed layer attribute information of different graphic elements in the layer to be presented, covering multiple dimensions such as location coordinates, pipeline size, pipeline characteristics, and laying years. Position coordinate information provides a basis for accurate spatial positioning and spatial analysis of the pipeline network. For example, when analyzing the spatial relationship between the pipeline network and surrounding buildings, it can accurately determine whether there are problems such as insufficient safety spacing. Pipeline size and characteristic information are of key guiding significance for flow calculation, material selection, and maintenance strategy formulation of the pipeline network. Pipes of different sizes and characteristics have differences in carrying capacity and durability. Accurately mastering this information can ensure the safe and stable operation of the pipeline network. The laying age helps to formulate a reasonable pipeline network update plan, replace aging pipeline facilities in a timely manner, and prevent potential leakage and failure risks. The acquisition of display attributes (such as color, transparency, and line style) not only enriches the visualization of pipeline information on the map, but can also be personalized according to different analysis needs and user preferences. For example, by distinguishing different types of pipelines by color (such as blue for water supply and green for drainage), the pipeline network information can be clearly seen on the map, improving the information recognition; the transparency setting can better display the relationship between different layers when multiple layers are superimposed, avoid information occlusion, and facilitate users to fully observe the comprehensive layout of the pipeline network and other geographical elements; line styles can be used to represent different states of pipelines (such as solid lines for normal operation, dotted lines for planning or maintenance, etc.), further enhancing the expressive power of visual information. The record of creation time and update time information provides strong support for version management and historical traceability of pipeline network data. When the pipeline network data is abnormal or it is necessary to compare the changes in the pipeline network layout in different periods, the relevant data can be quickly located and searched based on these time information, the reasons and processes of data changes can be analyzed, the integrity and traceability of the data can be ensured, and a more comprehensive basis can be provided for pipeline network management decisions. The identification attribute gives each graphic element a unique identity, which facilitates rapid retrieval and precise positioning in complex pipeline network data, and improves the convenience and accuracy of data management.The preprocessing unit performs data preprocessing on the layer attribute information and constructs the attribute feature vector. This method of pairing the features of each dimension in the same order simplifies the complexity of subsequent calculations as much as possible. When calculating the correlation or difference index between different pipeline layers, there is no need to perform tedious data sorting and matching work. Efficient mathematical operations can be performed directly on the paired feature values, which significantly improves the data processing speed and analysis efficiency.

[0055] Example 3

[0056] Please refer to Figure 1 ,Specifically: the layer display module includes a visual presentation unit, an inter-layer distribution unit and a hot spot identification unit;

[0057] The visual presentation unit is used to select multiple groups of layers in the SWSoft-pipeline network geographic information GIS management system, and superimpose the selected multiple groups of layers to present the multiple groups of layers to be displayed, and divide the multiple groups of layers into several groups of detection areas in the same way. According to the detection areas divided by each layer, the area pairs at the same position between each layer are determined, and when multiple groups of layers are displayed for observation at the same time, the attribute feature vectors of each layer are used to determine the overlap rate Cdz of the graphic elements in each area pair between the layers, which is specifically obtained in the following way:

[0058] ;

[0059] In the formula, Indicates the overlap rate of graphic elements within the jth region between layers, Represents the overlapping area of ​​graphic elements within the jth region between layers. It represents the area of ​​graphic elements in the detection area corresponding to the j-th region pair between layers in the i-th layer. represents the area of ​​the graphic element in the detection area corresponding to the j-th region pair in the k-th layer, i and k both represent the corresponding layer numbers, and j represents the corresponding region pair number; Indicates the selection of a relatively small graphic element area;

[0060] The overlap rate Cdz of graphic elements in each region between layers ranges from 0 to 1. When the overlap rate Cdz=0, it means that the two graphic elements have no overlapping parts and the visual conflict may be small (but this is only considered from the perspective of shape overlap, excluding other factors such as position, color, etc.); when the overlap rate Cdz=1, it means that the smaller graphic element is completely covered by another graphic element, which usually means a higher visual conflict because it may cause the smaller graphic element to be completely visually obscured or difficult to distinguish.

[0061] The above overlap area Cdmj and graphic element area All of them are obtained through SWSoft-pipeline network geographic information GIS management system;

[0062] The inter-layer distribution unit is used to analyze the overall overlapping distribution between layers according to the overlapping rate Cdz of the graphic elements in each region between the layers obtained in the visual presentation unit, so as to calculate and obtain the spatial distribution coefficient Kfxs. The spatial distribution coefficient Kfxs is obtained by the following formula:

[0063] ;

[0064] In the formula, j and g represent the corresponding region pair numbers; represents the total number of region pairs, i.e. the total number of detection areas; represents the spatial weight matrix element; Indicates the overlap ratio of graphic elements within the jth region between layers; Indicates the overlap ratio of graphic elements within the g-th region between layers; represents the average overlap ratio among all region pairs.

[0065] The above spatial weight matrix elements The spatial weight matrix is ​​first constructed based on the spatial position relationship of the graphic elements. There are many commonly used construction methods, such as distance-based (weights are set according to the distance between graphic elements, the closer the distance, the greater the weight, etc.). The spatial weight matrix reflects the relationship between the various graphic elements in space.

[0066] The hot spot identification unit is used to identify whether there is a hot spot area with visual conflict. According to the calculation method of the spatial distribution coefficient Kfxs obtained in the inter-layer distribution unit, it is known that the value range of the spatial distribution coefficient Kfxs is When the spatial distribution coefficient Kfxs=1, it means that the spatial distribution is random; when the spatial distribution coefficient Kfxs>1, it means that there is a negative spatial correlation, that is, there is no aggregation between the layers, and the spatial distribution is relatively dispersed; when the spatial distribution coefficient Kfxs<1, it means that there is a positive spatial correlation, that is, there is aggregation between the layers, and it will be determined that there is a hot spot area of ​​visual conflict between the current layers.

[0067] In this embodiment, the visual presentation unit can accurately determine the overlap rate Cdz of the graphic elements in each region between the layers through the area division and overlap rate calculation method. This quantitative overlap rate indicator provides an objective and accurate basis for the evaluation of visual conflict, and no longer relies on subjective visual judgment, making the analysis of the overlap between graphic elements more scientific and rigorous. Whether it is when the pipe network layer is superimposed and displayed with other geographic information layers (such as building layers and road layers), or in the scene where different types of pipe network layers (such as water supply pipeline layers and drainage pipeline layers) interact with each other, the overlap degree of graphic elements in each area can be clearly understood, so as to fully grasp the distribution details of visual conflicts within the entire map range, and lay a solid foundation for the subsequent targeted processing of visual conflicts. Based on the calculation of the overlap rate Cdz, the inter-layer distribution unit further obtains the spatial distribution coefficient Kfxs, which reflects the overall overlap distribution characteristics between layers from a macro level. Through this coefficient, it can be intuitively judged whether the overlap between layers is randomly distributed, clustered or dispersed, which provides key information for the system to fully understand the spatial pattern of visual conflicts. For example, in the pipe network geographic information system of a large city, the spatial distribution coefficient Kfxs can quickly determine which areas have relatively concentrated visual conflict hotspots and which areas have relatively few visual conflicts and are more evenly distributed, which helps to reasonably allocate resources in urban planning, pipe network maintenance and other work, give priority to dealing with visual conflict problems in key areas, and improve work efficiency and resource utilization efficiency. Compared with the traditional manual investigation method, it can filter out the areas that need to be focused on from the massive layer information in a short time, saving a lot of manpower, material resources and time costs for pipe network management.

[0068] Example 4

[0069] Please refer to Figure 1 ,Specifically: the inter-layer interference analysis module includes a representation analysis unit and an internal association unit;

[0070] The image analysis unit is used to extract the spatial coordinate points of different graphic elements in the corresponding layer from the attribute feature vectors of each layer based on the existence of the visual conflict hotspot area. These coordinates determine the position of the graphic elements in the three-dimensional geographic space; and the spatial coordinate points of different graphic elements in the corresponding layer , converted to spherical coordinates to mark the spherical coordinate points of different graphic elements in the corresponding layer , and from the spherical coordinate points of different graphic elements in the corresponding layer Extract the spherical coordinate points of overlapping graphic elements between layers , based on the spherical coordinate points of overlapping graphic elements between layers , analyze the visual conflict between layers, calculate and obtain the visual conflict measurement coefficient Scxs, and the visual conflict measurement coefficient Scxs is obtained by the following formula:

[0071] ;

[0072] Where m represents the number of the overlapping graphic elements, M represents the number of overlapping graphic elements, Represents the spherical coordinate point of the mth overlapping graphic element in the i-th layer, express The radial distance in express The azimuth in express The elevation angle in Represents the spherical coordinate point of the mth overlapping graphic element in the kth layer, express The radial distance in express The azimuth in express The elevation angle in represents pi, which is approximately equal to 3.141592654. Represents the weight value;

[0073] The radial distance r in the above spherical coordinates is obtained by the following formula:

[0074] Position angle in spherical coordinates Obtained by the following formula: in, is the inverse tangent function;

[0075] Elevation angle in spherical coordinates Obtained by the following formula: in, is the arccosine function;

[0076] In this embodiment, first, by extracting and converting the spatial coordinate points into spherical coordinates, a more accurate spatial description dimension is introduced for visual conflict analysis. Spherical coordinates not only contain location information, but also reflect the direction and angle relationship, which makes the understanding of the spatial relationship between graphic elements more comprehensive and detailed. In a complex pipe network geographic information system, for example, when multiple pipelines are superimposed with surrounding geographic element layers, the specific orientation and mutual posture of the overlapping graphic elements in three-dimensional space can be accurately captured, and it is no longer limited to simple plane position judgment. The visual conflict measurement coefficient Scxs calculated based on this presents the degree of visual conflict in a quantitative manner. For the pipe network operation and maintenance team, the visual conflict measurement coefficient Scxs can intuitively reflect the severity of the visual conflict between the pipe network layers in different regions, so that it can give priority to the areas with serious conflicts in a targeted manner, such as marking and detailed inspection of overlapping and visually conflicting pipeline lines, troubleshooting potential safety hazards or data errors, and improving the accuracy and efficiency of operation and maintenance work. Furthermore, this quantitative visual conflict assessment method is conducive to communication and collaboration between different departments. In the past, differences in subjective perceptions of visual conflict may have led to poor communication. However, the unified visual conflict measurement coefficient Scxs standard now allows all parties to communicate based on objective data and jointly explore strategies to optimize layer display and pipeline network layout, promote the coordinated and systematic development of urban geographic information management, and improve overall management efficiency and decision-making quality.

[0077] Example 5

[0078] Please refer to Figure 1 Specifically: the internal association unit is used to divide the feature values ​​in the attribute feature vector of each layer into different attribute categories, and define an attribute classification function based on a , attribute classification function Indicates that the index a of the corresponding attribute category is returned according to the value of a. For example, when a=1, the attribute classification function Returns the index corresponding to the pipe size attribute; when a=2, the attribute classification function Returns the index corresponding to the pipeline characteristic attribute; analyzes the logical association strength between layers to obtain the logical association coefficient Lgxs, which is obtained by the following formula:

[0079] ;

[0080] In the formula, a represents the index of the attribute category, A represents the number of attribute categories, c represents the index of the feature value under the corresponding attribute category, and G represents the number of feature values ​​under the corresponding attribute category. represents the cth eigenvalue under the ath attribute condition in the layer attribute feature vector of the i-th layer, Represents the cth eigenvalue under the ath attribute condition in the layer attribute feature vector of the kth layer, Represents the adjustment parameter, which is used to adjust the sensitivity of the logical association coefficient to the difference in layer attributes;

[0081] Represents the part used to measure the difference between the two layers in the selected attribute (given by According to the degree of difference in a (determine the attribute category), the exponential function makes the measurement of this difference nonlinear, and the smaller the difference between the two layers in this attribute, the larger the value of this part, and vice versa;

[0082] Part 1: Different a values ​​(representing different factor levels) and adjustment parameters Comprehensive consideration is taken to adjust the weights.

[0083] In this embodiment, first, by dividing the eigenvalues ​​in the layer attribute feature vector into attribute categories, and using the attribute classification function, the complex layer attribute information is organized more systematically, which helps managers to clearly analyze the logical associations between layers from different dimensions. For example, in the pipe network geographic information system, it can accurately distinguish the associations of different aspects such as pipe size and characteristics, and avoid misjudgment of logical relationships due to information confusion. Furthermore, the logical association coefficient Lgxs comprehensively considers multiple factors such as the difference in eigenvalues ​​under each attribute category. And the use of exponential functions allows it to measure attribute differences nonlinearly, and can more delicately reflect the subtle differences in selected attributes of different layers, making the evaluation of logical association strength more accurate. For example, when comparing the water supply pipeline and drainage pipeline layers, the degree of fit or difference at each attribute level can be accurately known.

[0084] Example 6

[0085] Please refer to Figure 1 Specifically: the comprehensive analysis module is used to use deep learning technology to build an initial model, and train and test the initial model with layer attribute information of different graphic elements in each layer, and use the trained initial model as a state recognition model, respectively obtain feature information in the state recognition model, and train and test the state recognition model with the acquired feature information, and then use the trained state recognition model as a layer combination evaluation model, and after the trained layer combination evaluation model, fit and output a comprehensive conflict measurement index Zczs, and the comprehensive conflict measurement index Zczs is obtained in the following manner:

[0086] ;

[0087] In the formula, represents the logical association coefficient, represents the visual conflict measurement coefficient, and The logical correlation coefficients are and visual conflict metric coefficient The weight value of <1,0< <1, and The specific value is set by the user according to the situation.

[0088] The feedback and operation and maintenance module is used to pre-set the evaluation threshold K, and compare and analyze it with the comprehensive conflict measurement index Zczs to comprehensively judge the overall coordination performance between the currently displayed layers. The specific judgment content is as follows:

[0089] If the comprehensive conflict measurement index Zczs exceeds the evaluation threshold K, it is comprehensively judged that the overall coordination performance between the currently displayed layers is in an abnormal state. At this time, the display levels of each layer will be adjusted, and the graphic elements of one layer will be set to semi-transparent, so that it is visually located behind another layer to reduce direct overlap and occlusion. For example, if some pipeline graphic elements in layer A are in serious conflict with building graphic elements in layer B, the transparency of the pipeline graphic elements can be set to 0.5, or the building layer can be displayed above the pipeline layer; and prompt information is added to the hot spot area of ​​visual conflict, and the prompt information includes text labels and icons to explain the possible visual conflict. and solution suggestions to guide users to perform further operations and analysis; at the same time, the layer attribute information of each layer in the pipe network layer is re-updated and checked. For example, the logical corresponding data of the pipe connection relationship, pipe diameter data, etc. in the pipe network layer and other related layers (such as the geographical terrain layer and the surrounding building layer) are reviewed. If inconsistencies or erroneous information is found, corrections are made based on reliable data sources or field measurement data. For example, if a pipe is found to have an unreasonable crossing relationship with a building in the layer, the engineering drawings are rechecked or the actual position relationship is determined by underground detection equipment before the data is modified; if the data updates of different layers are not synchronized and cause logical conflicts, a unified data update mechanism is established. For example, when there is a new laying or renovation project in the pipe network, the relevant information is synchronized to other related layers in a timely manner, such as the road layer (if there is a road excavation associated with the pipe network construction), the regional planning layer (if the change in the pipe network layout affects the surrounding land use planning), etc., by setting a data update trigger, when the pipe network data is updated, the related layers are automatically notified to update the data or provide data update suggestions, so that the data of each layer is consistent in time and logic, and the logical correlation coefficient is stabilized. Logical relationship prompts are added to each layer. For example, when the pipe network layer has important logical connections with other layers (such as the water supply relationship with the water source map layer, the drainage relationship with the sewage treatment plant layer, etc.), relevant logical prompts are displayed next to the graphic elements or through floating information boxes to help users better understand the logical relationship between the layers. Even if there are certain visual conflicts, the overall coordination performance can be improved through clear logical display.

[0090] If the comprehensive conflict measurement index Zczs does not exceed the evaluation threshold K, it is comprehensively judged that the overall coordination performance between the currently displayed layers is not in an abnormal state. At this time, the user's feedback on the layer display and system functions will be actively collected. By setting a feedback button on the system interface and sending user questionnaires regularly, the user's feelings and suggestions on the coordination performance between layers during use are understood. For example, users may suggest that the display color matching of certain layers will cause visual fatigue after long-term use, or that the display method of certain logical relationships is not intuitive enough. Targeted optimization is carried out based on these feedback opinions.

[0091] In this embodiment, in the comprehensive analysis module, deep learning is used to build a model and train to obtain a comprehensive conflict measurement index Zczs, which can comprehensively and accurately quantify the coordination status between layers. It integrates the logical association coefficient and the visual conflict measurement coefficient to overcome the one-sidedness of single factor evaluation. For example, in a large urban pipe network system, the logical connection of the pipeline and the conflict of graphic display are comprehensively considered, so that managers can quickly know the advantages and disadvantages of each layer combination, provide a scientific basis for pipe network planning and transformation, avoid resource mismatch and project delays due to subjective judgment errors, and improve the accuracy and efficiency of overall management decisions. The advantages of the feedback and operation and maintenance modules are also outstanding. When the comprehensive conflict measurement index is abnormal, the layer display level and transparency are automatically adjusted, which can immediately optimize the visual effect, reduce visual interference, and make key information clearly presented. Re-updating and checking the layer attribute information can timely discover and correct data errors and logical deviations, and ensure the accuracy and consistency of the pipe network data. Adding logical relationship prompt information assists staff to deeply understand the layer association, reduce cognitive burden, and improve work efficiency. In normal conditions, actively collecting user feedback can accurately grasp user needs and experience pain points, and provide direction for continuous optimization of the system. For example, by improving the layer color matching or operation interface design based on user suggestions, the friendliness and ease of use of the system can be enhanced, and personnel from different departments can be encouraged to use the system more smoothly for collaborative work, thereby promoting the continuous improvement of the level of informationization of urban pipeline management and achieving efficient, intelligent and precise pipeline operation and management goals.

[0092] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A GIS-based integrated management system for geographical information of pipe networks, characterized by: It includes pipe network information acquisition module, layer display module, inter-layer interference analysis module, comprehensive analysis module and feedback and operation and maintenance module; The pipe network information acquisition module is used to obtain the layer attribute information of different graphic elements in each layer according to the layer list in the SWSoft-Pipeline Geographic Information GIS management system, and generate the attribute feature vector of each layer after preprocessing; The layer display module is used to determine the overlap rate Cdz of the graphic elements in each region between the layers when displaying multiple layers for observation at the same time, using the attribute feature vector of each layer, and based on the overlap rate Cdz, judge the spatial aggregation between the layers to identify whether there is a visual conflict hot spot area; The inter-layer interference analysis module is used to analyze the visual conflict between layers and the logical association strength between layers based on the existence of visual conflict hotspot areas, so as to obtain the visual conflict measurement coefficient Scxs and the logical association coefficient Lgxs; The inter-layer interference analysis module includes an appearance analysis unit and an internal association unit; The image analysis unit is used to extract the spatial coordinate points of different graphic elements in the corresponding layer from the attribute feature vectors of each layer based on the existence of the visual conflict hotspot area. ; And the spatial coordinate points of different graphic elements in the corresponding layer , converted to spherical coordinates to mark the spherical coordinate points of different graphic elements in the corresponding layer , and from the spherical coordinate points of different graphic elements in the corresponding layer Extract the spherical coordinate points of overlapping graphic elements between layers , based on the spherical coordinate points of overlapping graphic elements between layers , analyze the visual conflict between layers, calculate and obtain the visual conflict measurement coefficient Scxs, and the visual conflict measurement coefficient Scxs is obtained by the following formula: ； Where m represents the number of the overlapping graphic elements, M represents the number of overlapping graphic elements, Represents the spherical coordinate point of the mth overlapping graphic element in the i-th layer, express The radial distance in express The azimuth in express The elevation angle in Represents the spherical coordinate point of the mth overlapping graphic element in the kth layer, express The radial distance in express The azimuth in express The elevation angle in represents pi, Represents the weight value; The internal association unit is used to divide the feature values ​​in the attribute feature vector of each layer into different attribute categories and define an attribute classification function based on a , attribute classification function Indicates that the index a of the corresponding attribute category is returned according to the value of a to analyze the logical association strength between layers and obtain the logical association coefficient Lgxs. The logical association coefficient Lgxs is obtained by the following formula: ； In the formula, a represents the index of the attribute category, A represents the number of attribute categories, c represents the index of the feature value under the corresponding attribute category, and G represents the number of feature values ​​under the corresponding attribute category. represents the cth eigenvalue under the ath attribute condition in the layer attribute feature vector of the i-th layer, Represents the cth eigenvalue under the ath attribute condition in the layer attribute feature vector of the kth layer, represents the adjustment parameter; The comprehensive analysis module is used to construct a layer combination evaluation model using deep learning technology, and input the visual conflict measurement coefficient Scxs and the logical association coefficient Lgxs into the layer combination evaluation model, and after linear normalization processing, fit and output the comprehensive conflict measurement index Zczs; The feedback and operation and maintenance module is used to pre-set the evaluation threshold K, and compare and analyze it with the comprehensive conflict measurement index Zczs to comprehensively judge the overall coordination performance between the currently displayed layers, and execute corresponding operation and maintenance measures based on the comprehensive judgment results.

2. A GIS-based integrated management system for geographical information of pipe networks according to claim 1, characterized in that: The pipe network information acquisition module includes a layer information unit and a preprocessing unit; The layer information unit is used to log in to SWSoft-Pipeline Geographic Information GIS Management System through a browser, and select "Map Operation" in the menu bar of SWSoft-Pipeline Geographic Information GIS Management System to pull down and display the function menu, wherein the function menu includes zoom in, zoom out, full map, measure distance, measure area, clear measurement, bookmark, view print and layer list, and then determine the layer to be displayed by selecting the pipe network layer in the layer list, and obtain the layer attribute information of different graphic elements in the layer to be presented through "Query Statistics" in the menu bar, the layer attribute information of different graphic elements in the layer to be presented includes the position coordinates, pipeline size, pipeline characteristics, laying age, display attributes, creation time, update time and identification attributes of different graphic elements in the layer to be presented; wherein the pipe network layer includes water supply pipeline layer, drainage pipeline layer, gas pipeline layer, thermal pipeline layer and auxiliary facilities layer; The preprocessing unit is used to perform data preprocessing on the layer attribute information of different graphic elements in the layer to be currently presented, pair the features of each dimension in the same order to construct the attribute feature vector of each layer, and mark the attribute feature vector of each layer.

3. A GIS-based integrated management system for geographical information of pipe networks according to claim 2, characterized in that: The layer display module includes a visual presentation unit, an inter-layer distribution unit and a hot spot identification unit; The visual presentation unit is used to select multiple groups of layers in the SWSoft-pipeline network geographic information GIS management system, and superimpose the selected multiple groups of layers to present the multiple groups of layers to be displayed, and divide the multiple groups of layers into several groups of detection areas in the same way. According to the detection areas divided by each layer, the area pairs at the same position between each layer are determined, and when multiple groups of layers are displayed for observation at the same time, the attribute feature vectors of each layer are used to determine the overlap rate Cdz of the graphic elements in each area pair between the layers, which is specifically obtained in the following way: ； In the formula, Indicates the overlap rate of graphic elements within the jth region between layers, Represents the overlapping area of ​​graphic elements within the jth region between layers. It represents the area of ​​graphic elements in the detection area corresponding to the j-th region pair between layers in the i-th layer. It represents the area of ​​graphic elements in the detection area corresponding to the j-th region pair in the k-th layer, i and k represent the corresponding layer numbers, and j represents the corresponding region pair number.

4. The GIS-based integrated management system for pipe network geographic information according to claim 3, characterized in that: The inter-layer distribution unit is used to analyze the overall overlapping distribution between layers according to the overlapping rate Cdz of the graphic elements in each region between the layers obtained in the visual presentation unit, so as to calculate and obtain the spatial distribution coefficient Kfxs. The spatial distribution coefficient Kfxs is obtained by the following formula: ； In the formula, j and g represent the corresponding region pair numbers; represents the total number of region pairs; represents the spatial weight matrix element; Indicates the overlap ratio of graphic elements within the jth region between layers; Indicates the overlap ratio of graphic elements within the g-th region between layers; represents the average overlap ratio among all region pairs.

5. A GIS-based integrated management system for pipe network geographic information according to claim 4, characterized in that: The hot spot identification unit is used to identify whether there is a hot spot area with visual conflict. According to the calculation method of the spatial distribution coefficient Kfxs obtained in the inter-layer distribution unit, it is known that the value range of the spatial distribution coefficient Kfxs is When the spatial distribution coefficient Kfxs=1, it means that the spatial distribution is random; when the spatial distribution coefficient Kfxs>1, it means that there is a negative spatial correlation, that is, there is no aggregation between the layers; when the spatial distribution coefficient Kfxs<1, it means that there is a positive spatial correlation, that is, there is aggregation between the layers. At this time, it will be determined that there is a hot spot area with visual conflict between the current layers.

6. The GIS-based integrated management system for pipe network geographic information according to claim 1, characterized in that: The comprehensive analysis module is used to construct an initial model using deep learning technology, and train and test the initial model with layer attribute information of different graphic elements in each layer, and use the trained initial model as a state recognition model, respectively obtain feature information in the state recognition model, and train and test the state recognition model with the acquired feature information, and then use the trained state recognition model as a layer combination evaluation model, and fit the trained layer combination evaluation model to output a comprehensive conflict measurement index Zczs, and the comprehensive conflict measurement index Zczs is obtained in the following manner: ； In the formula, represents the logical association coefficient, represents the visual conflict measurement coefficient, and The logical correlation coefficients are and visual conflict metric coefficient The weight value of and The specific value is set by the user according to the situation.

7. The GIS-based integrated management system for pipe network geographic information according to claim 1, characterized in that: The feedback and operation and maintenance module is used to pre-set the evaluation threshold K, and compare and analyze it with the comprehensive conflict measurement index Zczs to comprehensively judge the overall coordination performance between the currently displayed layers. The specific judgment content is as follows: If the comprehensive conflict measurement index Zczs exceeds the evaluation threshold K, it is comprehensively judged that the overall coordination performance between the currently displayed layers is in an abnormal state. At this time, the display level of each layer will be adjusted, the graphic elements of one of the layers will be set to be semi-transparent, and prompt information will be added to the hot spot area of ​​visual conflict. The prompt information includes text labels and icons. At the same time, the layer attribute information of each layer in the pipe network layer will be re-updated and checked, and logical relationship prompt information will be added to each layer; If the comprehensive conflict measurement index Zczs does not exceed the evaluation threshold K, it is comprehensively judged that the overall coordination performance between the currently displayed layers is not in an abnormal state. At this time, the user's feedback on the layer display and system functions will be actively collected. By setting a feedback button on the system interface and sending user questionnaires regularly, the user's feelings and suggestions on the coordination performance between layers during use can be understood.

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