Visualizing method and system based on land remediation and ecological restoration data

Abstract

This invention provides a visualization method and system based on land consolidation and ecological restoration data, belonging to the field of data processing and visualization technology. First, land consolidation and ecological restoration data are encapsulated into scenarios, generating a set of scenario-specific data units carrying interactive attribute identifiers. Next, a visualization carrier adapted to the scenario is dynamically generated based on the interactive attribute identifiers, and data embedding processing is performed to generate basic visualization data. Then, user interaction behavior is captured, and a set of interaction feature information is extracted. Finally, the presentation parameters of the visualization carrier and the data unit embedding method are adjusted according to the interaction feature information to form a final visualization result that dynamically responds to user interaction. This invention improves the interactivity and adaptability of data visualization.

Description

Technical Field

[0001] This invention relates to the field of data processing and visualization technology, and more specifically, to a visualization method and system based on land consolidation and ecological restoration data. Background Technology

[0002] With land consolidation and ecological restoration becoming increasingly important, visualizing relevant data to support decision-making and showcase results has become crucial. However, existing data visualization methods have many limitations when processing land consolidation and ecological restoration data.

[0003] On the one hand, traditional methods often simply present data statically without fully considering the complexity and dynamism of land consolidation and ecological restoration data. Land consolidation data includes information on the characteristics of the consolidation area and the implementation of consolidation actions, while ecological restoration data covers information on the evolution of the restoration status and the adaptation of restoration measures. There are close internal connections between these data, but traditional methods struggle to present these complex relationships intuitively.

[0004] On the other hand, existing visualization methods lack interactivity. Users can only passively view the data display results and cannot interact with the data according to their own needs and interests, making it difficult to quickly extract valuable information from massive amounts of data. Moreover, when facing different scenario requirements, traditional visualization methods cannot flexibly adjust the form of the visualization medium and the way the data is embedded, resulting in visualization effects that cannot meet diverse practical application needs. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, the present invention provides a visualization method based on land consolidation and ecological restoration data, the method comprising: The land consolidation data and ecological restoration data are encapsulated in a scenario-based manner to generate a set of scenario-based data units carrying interactive attribute identifiers. The land consolidation data includes the characteristic information of the consolidation area and the execution information of the consolidation behavior, and the ecological restoration data includes the restoration status evolution information and the restoration measure adaptation information. The interactive attribute identifiers correspond to the visual interactive triggering conditions of the data units. Based on the interactive attribute identifiers in the set of scenario-based data units carrying interactive attribute identifiers, a visualization carrier adapted to the scenario requirements is dynamically generated. The presentation form of the visualization carrier is associated with the interactive attribute identifiers in the set of scenario-based data units carrying interactive attribute identifiers. The set of contextualized data units carrying interactive attribute identifiers is embedded according to the presentation rules of the visualization carrier to generate basic visualization data with interactive response capabilities. The basic visualization data retains the complete interactive attribute identifiers of the contextualized data units in the set of contextualized data units carrying interactive attribute identifiers. Capture user interaction behaviors of visual basic data with interactive response capabilities, extract the set of interactive feature information corresponding to the user interaction behaviors, and form an association mapping between the set of interactive feature information and the presentation form of the visualization carrier; Based on the set of interactive feature information, the presentation parameters of the visualization carrier and the embedding method of the scenario-based data units in the set of scenario-based data units carrying interactive attribute identifiers are adjusted to form the final result of land management and ecological restoration data visualization for dynamic response to user interaction.

[0006] Furthermore, this invention also provides a visualization system based on land consolidation and ecological restoration data, characterized by comprising: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned visualization method based on land consolidation and ecological restoration data by executing the machine-executable instructions.

[0007] In another aspect, the present invention also provides a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of the visualization system based on land consolidation and ecological restoration data reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the visualization system based on land consolidation and ecological restoration data to perform the above-mentioned visualization method based on land consolidation and ecological restoration data.

[0008] Based on the above, by encapsulating land consolidation and ecological restoration data into scenarios, a set of scenario-based data units carrying interactive attribute identifiers is generated. Then, based on the interactive attribute identifiers, a visualization carrier adapted to the scenario requirements is dynamically generated, and data is embedded according to the presentation rules of the visualization carrier to generate basic visualization data with interactive response capabilities. This achieves precise matching and flexible presentation between the visualization carrier and data units, and can quickly adjust the visualization form according to different scenario requirements, improving the adaptability and flexibility of visualization. Next, user interaction behavior is captured and a set of interaction feature information is extracted. Based on this information, the presentation parameters of the visualization carrier and the embedding method of the data units are adjusted to form a final visualization result that dynamically responds to user interaction. This realizes deep interaction between users and data. Users can adjust and explore the visualization results in real time according to their needs and concerns, thereby more efficiently obtaining valuable information from massive amounts of data. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution flow of the visualization method based on land consolidation and ecological restoration data provided in the embodiments of the present invention.

[0010] Figure 2 This is a schematic diagram of exemplary hardware and software components of the visualization system based on land consolidation and ecological restoration data provided in the embodiments of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a visualization method based on land consolidation and ecological restoration data provided in one embodiment of the present invention. The visualization method based on land consolidation and ecological restoration data will be described in detail below.

[0012] Step S110: Perform scenario-based encapsulation processing on land consolidation data and ecological restoration data to generate a set of scenario-based data units carrying interactive attribute identifiers. Land consolidation data includes consolidation area feature information and consolidation behavior execution information, while ecological restoration data includes restoration status evolution information and restoration measure adaptation information. The interactive attribute identifiers correspond to the visualization interaction trigger conditions of the data units.

[0013] This embodiment uses a land consolidation and ecological restoration project in a hilly area as an application scenario. The land consolidation data for this area includes regional characteristic information and implementation information of consolidation activities. The regional characteristic information covers information such as regional terrain type, soil texture distribution, and current land use. The implementation information of consolidation activities includes records of the implementation process of activities such as land leveling, soil improvement, and terrace construction. The ecological restoration data includes restoration status evolution information and restoration measure adaptation information. The restoration status evolution information covers records of changes in regional vegetation coverage, soil organic matter content, and soil and water loss intensity over time. The restoration measure adaptation information includes vegetation configuration schemes for different soil textures and soil and water conservation measures for different slopes. Interactive attribute identifiers include trigger conditions for clicking to view changes in vegetation coverage before and after soil improvement of a plot, trigger conditions for sliding to view the trend of soil and water loss intensity changes over a certain period of time, and trigger conditions for zooming to view the spatial matching relationship between terrace construction and vegetation configuration in a certain area.

[0014] Step S111: Extract the regional feature information from the land consolidation data, identify the regional identifier content that represents the geographic attributes of the region in the regional feature information, and obtain the regional identifier content by parsing the geographic description field in the regional feature information.

[0015] In this embodiment, information is extracted from the geographic description field of the remediation area feature information. This geographic description field includes the latitude and longitude range of the area, the division of terrain units, and the distribution range of soil texture. Parsing this field yields the area identifier content, which includes the spatial distribution range of the area, such as the latitude and longitude intervals it covers, and boundary features, such as the polygon vertex coordinate sequence of the boundary and the boundary outline description of the terrain units. For example, the spatial distribution range in the area identifier content is from latitude and longitude interval A to latitude and longitude interval B, and the boundary feature is a closed region formed by the polygon vertex coordinate sequences C, D, E, and F.

[0016] Step S112: Extract the execution information of the land consolidation behavior from the land consolidation data, capture the execution identifier content that represents the implementation process of the behavior in the execution information of the land consolidation behavior, the execution identifier content corresponds to the implementation steps and execution sequence of the consolidation behavior, and obtain the execution identifier content by extracting the process record field in the execution information of the land consolidation behavior.

[0017] In this embodiment, information is extracted from the process record field of the remediation behavior execution information. This process record field includes the sequence of implementation steps for behaviors such as land leveling, soil improvement, and terrace construction, the execution time intervals for each step, and information about the implementing entity. The implementation steps in this field are captured, such as the clearing, tilling, leveling, and compaction steps for land leveling; the soil testing, organic fertilizer application, and deep tillage mixing steps for soil improvement; and the layout, excavation, embankment construction, and leveling steps for terrace construction, as well as the execution sequence, such as the start and end time intervals for each step, to obtain the execution identifier content. For example, the implementation steps for land leveling in the execution identifier content are clearing, tilling, leveling, and compaction, with the execution sequence being the time intervals from G to H for the clearing step, I to J for the tilling step, K to L for the leveling step, and M to N for the compaction step.

[0018] Step S113: Extract restoration status evolution information from ecological restoration data, track the evolution marker content that represents the state change process in the restoration status evolution information, the evolution marker content corresponds to the stage change and transition characteristics of ecological restoration status, and obtain the evolution marker content by analyzing the state record field in the restoration status evolution information.

[0019] In this embodiment, information is extracted from the state record field of the restoration state evolution information. This state record field includes monitoring data of states such as vegetation cover, soil organic matter content, and soil erosion intensity over time, state stage division criteria, and transition time intervals for each stage. The stage changes in this field are analyzed, such as the changes in vegetation cover from low cover to medium cover and then to high cover; the changes in soil organic matter content from deficient to moderate and then to abundant; the changes in soil erosion intensity from severe erosion to moderate erosion and then to slight erosion; and the transition characteristics, such as the transition time intervals between each stage and the magnitude of state change, to obtain the evolution identifier content. For example, the vegetation cover stage changes in the evolution identifier content are low cover, medium cover, and high cover; the transition characteristics are the transition time interval from low cover to medium cover (O to P), with the state change magnitude being the coverage increase magnitude Q; and the transition time interval from medium cover to high cover (R to S), with the state change magnitude being the coverage increase magnitude T.

[0020] Step S114: Extract the adaptation information of restoration measures from the ecological restoration data, lock the adaptation identifier content that represents the correlation between measures in the adaptation information of restoration measures, and obtain the adaptation identifier content by interpreting the correlation record field in the adaptation information of restoration measures.

[0021] In this embodiment, information is extracted from the associated record field of the remediation measure adaptation information. This associated record field includes remediation measure configuration schemes for different remediation behaviors, matching conditions between remediation measures and remediation behaviors, and feedback on the implementation effect of remediation measures. The corresponding matching features between remediation measures and remediation behaviors in this field are interpreted. For example, the vegetation configuration scheme adapted to land leveling behavior is a mixed herbaceous plant sowing scheme; the vegetation configuration scheme adapted to soil improvement behavior is a shrub and herbaceous intercropping scheme; and the soil and water conservation measure scheme adapted to terrace construction behavior is a terrace embankment grass planting scheme, thus obtaining the adaptation identifier content. For example, the matching condition between land leveling behavior and the mixed herbaceous plant sowing scheme in the adaptation identifier content is that the soil texture is loam and the slope is less than a certain angle; the matching condition between soil improvement behavior and the shrub and herbaceous intercropping scheme is that the soil organic matter content is in a deficient stage and the slope is between a certain angle and a certain angle.

[0022] Step S115: Based on the spatial attributes of the regional identifier content and the temporal attributes of the evolution identifier content, classify the application scenario types of land consolidation and ecological restoration data. Different application scenario types correspond to different combinations of regional characteristics and restoration status.

[0023] In this embodiment, application scenario types are categorized based on the spatial attributes of the regional identifier content, such as different terrain units and soil texture distribution ranges, and the temporal attributes of the evolving identifier content, such as different vegetation cover stages and soil organic matter content stages. For example, there are application scenario types where the terrain unit is a gentle hillside with loam soil and low vegetation cover; application scenario types where the terrain unit is a steep hillside with sandy loam soil and medium vegetation cover; and application scenario types where the terrain unit is a valley with clay soil and low soil organic matter content.

[0024] Step S116: Perform combination operations on the core feature fields of each application scenario type to generate a unique scenario identifier. Assign a scenario identifier to the dataset corresponding to each application scenario type. Add a unique identification field to each scenario identifier. This unique identification field is generated by combining the core feature fields of the application scenario type.

[0025] In this embodiment, for each application scenario type, its core feature fields are selected, such as terrain unit, soil texture, vegetation cover stage, and soil organic matter content stage. These core feature fields are then combined and calculated to generate a unique scenario identifier. For example, for an application scenario type with a terrain unit of gentle hills and slopes, soil texture of loam, and low vegetation cover, its core feature fields are gentle hills and slopes, loam, and low cover stage. Combining these fields generates scenario identifier U. For an application scenario type with a terrain unit of steep hills and slopes, soil texture of sandy loam, and medium vegetation cover, its core feature fields are steep hills and slopes, sandy loam, and medium cover stage. Combining these fields generates scenario identifier V. A unique identification field is added to the generated scenario identifier. This unique identification field is obtained by combining and calculating the core feature fields of the application scenario type. For example, the unique identification field for scenario identifier U is the result of combining the calculations for gentle hills and slopes, loam, and low cover stage; the unique identification field for scenario identifier V is the result of combining the calculations for steep hills and slopes, sandy loam, and medium cover stage.

[0026] Step S117: Based on the usage scenario characteristics of the application scenario type, define the interaction attribute identifier for the dataset corresponding to each scenario identifier. The interaction attribute identifier includes the interaction trigger type, the interaction response method, and the interaction feedback content. The interaction trigger type corresponds to the possible operation behavior of the user, the interaction response method corresponds to the adjustment method of the visualization presentation, and the interaction feedback content corresponds to the information feedback form after the operation.

[0027] In this embodiment, interactive attribute identifiers are defined for the dataset corresponding to each scenario identifier, based on the usage scenario characteristics of the application scenario type. For example, the application scenario type corresponding to scenario identifier U is a hilly and gentle slope terrain unit with loam soil and low vegetation cover. Its usage scenario characteristic is that users are concerned about the matching effect between land leveling behavior and herbaceous plant mixed sowing scheme in this area. Therefore, the interaction trigger type is defined as clicking the land leveling behavior identifier in this area, the interaction response method is highlighting the distribution of herbaceous plant mixed sowing scheme in this area in the visualization interface, and the interaction feedback content is displaying data on the increase in vegetation cover and changes in soil organic matter content after herbaceous plant mixed sowing in this area. The application scenario type corresponding to the identifier V is a terrain unit with steep slopes and sandy loam soil, and the vegetation coverage is at the medium coverage stage. Its usage scenario characteristics are that users pay attention to the matching effect between the terrace construction behavior and the grass planting scheme on the terrace ridges in this area. Therefore, the interaction trigger type is defined as sliding the terrace construction behavior identifier in this area, the interaction response method is to dynamically display the trend of soil erosion intensity change after grass planting on the terrace ridges in this area in the visualization interface, and the interaction feedback content is to display the data on the reduction of soil erosion intensity and the change of vegetation coverage after grass planting on the terrace ridges in this area.

[0028] Step S118: Arrange the datasets with scene identifiers and interaction attribute identifiers in logical order according to the application scenario type, encapsulate the datasets with scene identifiers and interaction attribute identifiers into independent scenario-based data units, integrate all independent scenario-based data units to generate a set of scenario-based data units carrying interaction attribute identifiers, and establish association entries between each independent scenario-based data unit through scene identifiers and interaction attribute identifiers.

[0029] In this embodiment, datasets with scene identifiers and interaction attribute identifiers are arranged according to the logical order of application scenario types, such as from low vegetation coverage to high vegetation coverage. Each arranged dataset is encapsulated into an independent scenario-based data unit, and each scenario-based data unit contains dataset content, scene identifier, interaction attribute identifier, and other information. All independent scenario-based data units are integrated to generate a set of scenario-based data units carrying interaction attribute identifiers. Each independent scenario-based data unit is associated with another through scene identifiers and interaction attribute identifiers. For example, the scenario-based data unit corresponding to scene identifier U is associated with the scenario-based data unit corresponding to scene identifier V through the fact that both of them are hilly terrain. The scenario-based data unit corresponding to scene identifier U is associated with the scenario-based data units corresponding to other scene identifiers through the interaction attribute identifier that the interaction trigger type is click.

[0030] Step S120: Based on the interaction attribute identifiers in the set of scenario-based data units carrying interaction attribute identifiers, dynamically generate a visualization carrier that adapts to the scenario requirements. The presentation form of the visualization carrier is associated with the interaction attribute identifiers in the set of scenario-based data units carrying interaction attribute identifiers.

[0031] In this embodiment, based on the interaction attribute identifiers in the contextualized data unit set, such as interaction trigger types like click, swipe, and zoom, a visualization carrier adapted to the scenario requirements is dynamically generated. For example, for an interaction attribute identifier with a click trigger type, a visualization carrier containing clickable area identifiers is generated; for an interaction attribute identifier with a swipe trigger type, a visualization carrier containing a swipeable timeline is generated; and for an interaction attribute identifier with a zoom trigger type, a spatial distribution visualization carrier supporting zoom operations is generated. The presentation form of the visualization carrier is correspondingly associated with the interaction attribute identifier; for example, clickable area identifiers correspond to the click interaction trigger type, swipeable timelines correspond to the swipe interaction trigger type, and spatial distributions supporting zoom operations correspond to the zoom interaction trigger type.

[0032] Step S121: For each scenario-based data unit in the scenario-based data unit set carrying interaction attribute identifiers, perform field splitting of the interaction attribute identifier, parse the interaction attribute identifiers in the scenario-based data unit set carrying interaction attribute identifiers, extract the interaction trigger type, interaction response method and interaction feedback content in each interaction attribute identifier, and determine the interaction requirements corresponding to different scenario-based data units in the scenario-based data unit set carrying interaction attribute identifiers.

[0033] In this embodiment, the interaction attribute identifier of each scenario-based data unit in the scenario-based data unit set is split into fields, and the field content of the interaction attribute identifier is parsed to extract the interaction trigger type, interaction response method, and interaction feedback content. For example, the interaction attribute identifier of the scenario-based data unit corresponding to scenario identifier U is split into fields, and the parsed results show that the interaction trigger type is click, the interaction response method is to highlight the distribution of the herbaceous plant mixed sowing scheme, and the interaction feedback content is to display data on the increase in vegetation coverage and the change in soil organic matter content. The interaction attribute identifier of the scenario-based data unit corresponding to scenario identifier V is split into fields, and the parsed results show that the interaction trigger type is swipe, the interaction response method is to dynamically display the trend of soil erosion intensity, and the interaction feedback content is to display data on the decrease in soil erosion intensity and the change in vegetation coverage. Based on the extracted content, the interaction requirements corresponding to different scenario-based data units are determined. For example, the interaction requirement of the scenario-based data unit corresponding to scenario identifier U is to support click operation to view the effect of the herbaceous plant mixed sowing scheme, and the interaction requirement of the scenario-based data unit corresponding to scenario identifier V is to support swipe operation to view the trend of soil erosion intensity.

[0034] Step S122: Classify the interactive trigger types according to their operation logic and implementation methods. Divide the visualization presentation categories based on the differences in interactive trigger types. Different visualization presentation categories correspond to different combinations of interactive trigger types. The visualization presentation categories are adapted to the corresponding interactive operation requirements.

[0035] In this embodiment, interaction trigger types are categorized based on their operational logic and implementation methods, including click-based, swipe-based, and zoom-based types. Visual presentation formats are further categorized based on the differences in interaction trigger types. For example, visualization presentation formats containing clickable area markers correspond to click-based interaction trigger types, those containing a swipeable timeline correspond to swipe-based interaction trigger types, and spatial distribution visualization presentation formats supporting zoom operations correspond to zoom-based interaction trigger types. Different visualization presentation formats are adapted to corresponding interactive operation requirements. For instance, visualization presentation formats with clickable area markers are adapted to the interactive operation requirement of clicking to view the effect of a specific solution, and visualization presentation formats with a swipeable timeline are adapted to the interactive operation requirement of swiping to view a time trend.

[0036] Step S123: Combining the implementation requirements and technical specifications of the interactive response method, determine the core presentation parameters for each type of visualization presentation based on the characteristics of the interactive response method. The core presentation parameters include information display density, spatial layout method, and dynamic response speed.

[0037] In this embodiment, the core presentation parameters for each visualization presentation category are determined based on the characteristics of the interactive response method, taking into account the implementation requirements and technical specifications of the interactive response method. For example, for a visualization presentation category containing clickable area markers, its interactive response method is to highlight the distribution of specific schemes, therefore the information display density is determined to be medium density, the spatial layout method is to be divided into areas, and the dynamic response speed is fast response; for a visualization presentation category containing a sliding timeline, its interactive response method is to dynamically display time trends, therefore the information display density is determined to be low density, the spatial layout method is to be laid out according to time series, and the dynamic response speed is medium speed; for a spatial distribution visualization presentation category that supports zooming operations, its interactive response method is to display spatial distributions at different zoom levels, therefore the information display density is determined to be adjustable density, the spatial layout method is to be laid out according to spatial coordinates, and the dynamic response speed is high speed.

[0038] Step S1231: Classify interactive response methods based on their functional characteristics, distinguishing between display-type responses involving information display adjustments, layout-type responses involving spatial layout changes, and dynamic-type responses involving dynamic effects presentation. Different types of responses correspond to different implementation requirements.

[0039] In this embodiment, interactive response methods are categorized based on their functional characteristics. For example, interactive response methods that highlight the distribution of specific schemes involve adjustments to information display and belong to the display type; interactive response methods that arrange layouts according to time sequences involve changes in spatial layout and belong to the layout type; and interactive response methods that dynamically display time trends involve dynamic effects and belong to the dynamic type. Different types of responses correspond to different implementation requirements: the implementation requirement for display type responses is to clearly display specific information, the implementation requirement for layout type responses is to rationally arrange the spatial position of information, and the implementation requirement for dynamic type responses is to smoothly present the dynamic change process.

[0040] Step S1232: Generate corresponding setting standards based on the information processing logic of the display type response, analyze the scope and depth of information adjustment for the display type response, determine the setting standards for information display density, and ensure that the setting standards for information display density meet the requirements for information addition, subtraction, and detail expansion operations in the display type response.

[0041] In this embodiment, corresponding setting standards are generated based on the information processing logic of display-type responses. For display-type responses that highlight the distribution of specific schemes, the range of information adjustment is analyzed as the distribution area of ​​the specific scheme, and the depth of information adjustment is the detailed parameters of the display scheme. The setting standard for information display density is determined to be displaying medium-density information in the distribution area of ​​the specific scheme and low-density information in the non-distribution area of ​​the specific scheme, so as to meet the requirements of information addition, subtraction, and detail expansion operations.

[0042] Step S1233: Generate corresponding design rules based on the spatial change pattern of layout-type responses. Analyze the dimensions and methods of spatial changes for layout-type responses, determine the design rules for spatial layout methods, and support the implementation of area switching and position adjustment operations in layout-type responses.

[0043] In this embodiment, corresponding design rules are generated based on the spatial variation patterns of layout-type responses. For layout-type responses with time-series layouts, the dimension of their spatial variation is analyzed as the time dimension, and the method of spatial variation is the arrangement of information in chronological order. The design rule for determining the spatial layout method is to arrange the information from left to right in chronological order, with each time node corresponding to an information module, in order to support the implementation of area switching and position adjustment operations.

[0044] Step S1234: Generate corresponding configuration parameters based on the time change characteristics of dynamic responses, analyze the speed and form of dynamic effects for dynamic responses, determine the configuration parameters of dynamic response speed, and match the configuration parameters of dynamic response speed with the implementation of animation display and real-time update operations in dynamic responses.

[0045] In this embodiment, corresponding configuration parameters are generated based on the time-varying characteristics of dynamic responses. For dynamic responses that dynamically display time trends, the speed of the dynamic effect is analyzed as medium, and the form of the dynamic effect is a smooth transition. Therefore, the configuration parameter for the dynamic response speed is determined to be medium response speed to match the implementation of animation display and real-time update operations.

[0046] Step S1235: According to the functional correlation of the parameters, the parameters are structurally combined, and the setting standards of information display density, the design rules of spatial layout and the configuration parameters of dynamic response speed are integrated to form the core presentation parameter framework for each type of visualization presentation. The core presentation parameter framework includes parameter type, parameter range and parameter adjustment rules.

[0047] In this embodiment, parameters are structurally combined according to their functional relationships, integrating the setting standards for information display density, the design rules for spatial layout, and the configuration parameters for dynamic response speed to form a core presentation parameter framework for each type of visualization. For example, in the core presentation parameter framework for a visualization category that includes clickable area markers, the parameter types are information display density, spatial layout, and dynamic response speed; the parameter range is medium information display density, spatial layout divided by area, and fast dynamic response speed; and the parameter adjustment rule is to adjust the information display density according to the size of the clickable area. In the core presentation parameter framework for a visualization category that includes a scrollable timeline, the parameter types are information display density, spatial layout, and dynamic response speed; the parameter range is low information display density, spatial layout based on time sequence, and medium dynamic response speed; and the parameter adjustment rule is to adjust the dynamic response speed according to the scrolling speed.

[0048] Step S1236: Based on the scene characteristics of the application scene type corresponding to the scene identifier, adjust the parameter range in the core presentation parameter framework by expanding or narrowing the parameter value range so that the parameter range can adapt to the information display needs under different application scene types.

[0049] In this embodiment, the parameter range in the core presentation parameter framework is adjusted based on the scene characteristics of the application scene type corresponding to the scene identifier. For example, for an application scene type where the terrain unit is a hilly slope with medium vegetation coverage, the scene characteristic is high information complexity. Therefore, the range of information display density in the core presentation parameter framework for the visualization presentation format category containing clickable area identifiers is expanded from medium density to medium-high density. For an application scene type where the terrain unit is a valley with low soil organic matter content, the scene characteristic is low information complexity. Therefore, the range of information display density in the core presentation parameter framework for the visualization presentation format category containing a sliding time axis is narrowed from low density to extremely low density.

[0050] Step S1237: Sort the core presentation parameters according to the degree of impact of the interaction response method on the overall visualization effect, and set the adjustment priority of the core presentation parameters based on the priority of the interaction response method. The parameters corresponding to the key interaction response methods are optimized first.

[0051] In this embodiment, the interaction response methods are ranked according to their impact on the overall visualization effect. For example, the interaction response method that dynamically displays time trends has a greater impact on the overall visualization effect than the interaction response method that highlights the distribution of specific schemes, and the interaction response method that highlights the distribution of specific schemes has a greater impact on the overall visualization effect than the interaction response method that arranges the layout according to time sequence. Based on the priority of the interaction response methods, the adjustment priority of the core presentation parameters is set, and the parameters corresponding to the key interaction response methods are optimized first. For example, the adjustment priority of the dynamic response speed parameter is higher than that of the information display density parameter, and the adjustment priority of the information display density parameter is higher than that of the spatial layout method parameter.

[0052] Step S1238: Construct test environments with different parameter combinations for testing. Verify the performance of the core presentation parameters under different interactive response methods through parameter simulation. Based on the verification results, finally determine the core presentation parameters for each type of visualization presentation.

[0053] In this embodiment, test environments with different parameter combinations are constructed for testing. For example, a test environment with a medium information display density, a spatial layout divided by region, and a fast dynamic response speed is constructed; another test environment with a medium-high information display density, a spatial layout divided by region, and a fast dynamic response speed is constructed. The performance of the core presentation parameters under different interactive response modes is verified through parameter simulation. For instance, the performance of the dynamic response speed parameter is tested under an interactive response mode that dynamically displays time trends, and the performance of the information display density parameter is tested under an interactive response mode that highlights the distribution of specific schemes. Based on the verification results, the core presentation parameters for each visualization presentation category are finally determined. For example, the core presentation parameters for the visualization presentation category containing clickable area markers are determined to be a medium-high information display density, a spatial layout divided by region, and a fast dynamic response speed.

[0054] Step S124: For each type of visualization presentation, determine its interactive entry style based on the interactive feedback content. The interactive entry style includes the trigger area location, operation prompt format, and feedback display location.

[0055] In this embodiment, for each type of visualization presentation, the style of the interactive entry point is determined based on the interactive feedback content. For example, for a visualization presentation type that includes clickable area markers, the interactive feedback content displays data on the increase in vegetation cover and changes in soil organic matter content. Therefore, the trigger area for the interactive entry point style is determined to be the location of the area marker in the visualization carrier, the operation prompt is a text prompt next to the area marker, and the feedback display location is the right side of the visualization carrier. For a visualization presentation type that includes a scrollable timeline, the interactive feedback content displays data on the decrease in soil erosion intensity and changes in vegetation cover. Therefore, the trigger area for the interactive entry point style is determined to be the timeline location in the visualization carrier, the operation prompt is an arrow prompt next to the timeline, and the feedback display location is the lower area of ​​the visualization carrier.

[0056] Step S125: Establish the correspondence between scene features of application scenario types and visualization presentation category. Based on the scene features of application scenario types, match the corresponding visualization presentation category for each scene data unit in the set of scene data units carrying interactive attribute identifiers. The matching is based on scene identifiers and interactive attribute identifiers.

[0057] In this embodiment, a correspondence is established between scene features of application scenario types and visualization presentation categories. For example, an application scenario type with a terrain unit of gentle hills and low vegetation coverage corresponds to a visualization presentation category containing clickable area markers, while an application scenario type with a terrain unit of steep hills and medium vegetation coverage corresponds to a visualization presentation category containing a scrollable timeline. Based on the scene features of the application scenario type, and using scene identifiers and interactive attribute identifiers as the basis, a corresponding visualization presentation category is matched to the scene-based data unit corresponding to each application scenario type. For example, the application scenario type corresponding to scene identifier U matches a visualization presentation category containing clickable area markers, and the application scenario type corresponding to scene identifier V matches a visualization presentation category containing a scrollable timeline.

[0058] Step S126: Perform the carrier construction operation according to the input core presentation parameters and interactive entry style requirements. Based on the matching results, call the visualization carrier generation tool, input the core presentation parameters and interactive entry style, and generate an initial visualization carrier with basic interactive response capabilities.

[0059] In this embodiment, the carrier construction operation is performed according to the input core presentation parameters and interactive entry style requirements. A visualization carrier generation tool is invoked based on the matching results. For example, a visualization carrier generation tool supporting clickable area icons is invoked for the application scenario type corresponding to scene identifier U, and a visualization carrier generation tool supporting a sliding timeline is invoked for the application scenario type corresponding to scene identifier V. The core presentation parameters and interactive entry style are input into the visualization carrier generation tool to generate an initial visualization carrier with basic interactive response capabilities. For example, the core presentation parameters include high-density information display, spatial layout divided by region, and fast dynamic response speed. The interactive entry style includes a trigger area location (region icon location), operation prompts in text format, and feedback display location in the right-side area, generating an initial visualization carrier containing clickable area icons.

[0060] Step S127: Verify the initial visualization carrier by simulating interactive operations to verify the fit of its interactive attribute identifiers, including the correspondence between the interactive trigger type and the interactive entry style, the adaptability of the interactive response method and the core presentation parameters, and the consistency between the interactive feedback content and the display format.

[0061] In this embodiment, the initial visualization carrier is validated through simulated interactive operations. For example, a click operation is simulated on the initial visualization carrier containing clickable area markers to verify the correspondence between the interaction trigger type (click) and the interaction entry style (clickable area marker); to verify the adaptability of the interaction response method (highlighting specific scheme distribution) and the core presentation parameter (high density of information display); and to verify the consistency between the interactive feedback content (displaying vegetation coverage increase data and soil organic matter content change data) and the display format (right-side area display). A sliding operation is simulated on the initial visualization carrier containing a sliding timeline to verify the correspondence between the interaction trigger type (sliding) and the interaction entry style (sliding timeline); to verify the adaptability of the interaction response method (dynamically displaying time trends) and the core presentation parameter (medium dynamic response speed); and to verify the consistency between the interactive feedback content (displaying soil erosion intensity reduction data and vegetation coverage change data) and the display format (bottom area display).

[0062] Step S128: Based on the fit verification results, adjust the core presentation parameters and interactive entry style of the initial visualization carrier so that the adjusted visualization carrier and the interactive attribute identifier of the scenario-based data unit form a corresponding association.

[0063] In this embodiment, based on the fit verification results, the core presentation parameters and interactive entry styles of the initial visualization carrier are adjusted. For example, if verification reveals that the interactive response method in the initial visualization carrier containing clickable area markers is not well-suited to the core presentation parameters, and the information display density is too high, causing the interface to be crowded, then the parameter value of information display density is reduced from a high value to a medium value. If verification reveals that the operation prompts of the interactive entry style are not obvious, then the operation prompts are changed from text prompts to icon prompts. The adjusted visualization carrier and the interactive attribute markers of the contextualized data units are associated accordingly; for example, the adjusted clickable area markers correspond to click interaction trigger types, and the adjusted swipeable timeline corresponds to swipe interaction trigger types.

[0064] Step S130: The set of contextualized data units carrying interactive attribute identifiers is embedded according to the presentation rules of the visualization carrier to generate visualization base data with interactive response capabilities. The visualization base data retains the complete interactive attribute identifiers of the contextualized data units in the set of contextualized data units carrying interactive attribute identifiers.

[0065] In this embodiment, the set of contextualized data units is embedded according to the presentation rules of the visualization carrier. For example, the presentation rules of the visualization carrier containing clickable area identifiers are set to embed data by region, and the contextualized data unit corresponding to scene identifier U is embedded into the visualization carrier according to the region; the presentation rules of the visualization carrier containing a scrollable timeline are set to embed data by time series, and the contextualized data unit corresponding to scene identifier V is embedded into the visualization carrier according to the time series. Interactive basic visualization data is generated, which retains the complete interactive attribute identifiers of the contextualized data units, such as interaction trigger types like click, swipe, and zoom, interactive response methods like highlighting and dynamic display, and interactive feedback content displaying specific data.

[0066] Step S131: Extract the presentation rules of the visualization carrier by parsing the configuration file of the visualization carrier. The presentation rules include data embedding format, information association method and interaction trigger mapping relationship. Among them, the data embedding format specifies the storage structure of the scenario-based data unit, the information association method specifies the association logic between different scenario-based data units, and the interaction trigger mapping relationship specifies the corresponding rules of interactive operation and data call.

[0067] In this embodiment, presentation rules are extracted by parsing the configuration file of the visualization carrier. For example, parsing the configuration file of the visualization carrier containing clickable area identifiers, the extracted data embedding format is stored according to area identifiers, the information association method is associated according to terrain units, and the interaction trigger mapping relationship is that clicking on an area identifier calls the scene-based data unit of the corresponding area; parsing the configuration file of the visualization carrier containing a scrollable timeline, the extracted data embedding format is stored according to time series, the information association method is associated according to time stages, and the interaction trigger mapping relationship is that scrolling the timeline calls the scene-based data unit of the corresponding time stage.

[0068] Step S132: Based on the field requirements and encoding specifications of the data embedding format, perform format conversion processing on each scenario-based data unit in the scenario-based data unit set so that the converted scenario-based data unit can adapt to the storage requirements of the visualization carrier, and the conversion process retains the scenario identifier, interactive attribute identifier and original data content of the scenario-based data unit.

[0069] In this embodiment, each scenario-based data unit undergoes format conversion processing according to the field requirements and encoding specifications of the data embedding format. For example, to meet the requirement that the data embedding format is stored according to the classification of region identifiers, the region identifier field of the scenario-based data unit is converted into a format that meets the storage requirements; to meet the requirement that the encoding specification is a specific character encoding, the text content of the scenario-based data unit is converted into that character encoding format. The conversion process preserves the scenario identifier, interaction attribute identifier, and original data content of the scenario-based data unit, such as retaining the scenario identifier U, the interaction attribute identifier with the interaction trigger type of click, and the original data content such as vegetation coverage increase data and soil organic matter content change data.

[0070] Step S133: According to the logical rules of information association, the converted scenario-based data units are associated to establish information association links between different scenario-based data units. The information association links are based on scenario identifiers and interaction attribute identifiers.

[0071] In this embodiment, the format-converted scenario-based data units are associated according to the logical rules of information association. For example, scenario-based data units whose terrain units are all hills are associated according to the logical rules of association by terrain unit; scenario-based data units whose time periods are all time periods are associated according to the logical rules of association by time period. Information association links between different scenario-based data units are established. The information association links are based on scenario identifiers and interaction attribute identifiers. For example, the scenario-based data unit corresponding to scenario identifier U is associated with the scenario-based data unit corresponding to scenario identifier V through the scenario identifier that the terrain units are all hills. The scenario-based data unit corresponding to scenario identifier U is associated with the scenario-based data units corresponding to other scenario identifiers through the interaction attribute identifier that the interaction trigger type is click.

[0072] Step S1331: By analyzing the information association method, determine the association dimension and association rule. The association dimension includes spatial association dimension, temporal association dimension and content association dimension. The association rule specifies the matching conditions and association form under different association dimensions.

[0073] In this embodiment, the association dimension and association rules are determined by parsing the information association methods. For example, parsing the information association method based on terrain units determines the association dimension as spatial association dimension, and the association rule is to associate scenario-based data units with the same terrain units; parsing the information association method based on time stages determines the association dimension as time association dimension, and the association rule is to associate scenario-based data units with overlapping time stages; parsing the information association method based on matching remediation measures with rectification actions determines the association dimension as content association dimension, and the association rule is to associate scenario-based data units that match remediation measures with rectification actions.

[0074] Step S1332: Based on the spatial association dimension, extract the region identifier content from the format-converted scenario-based data units, and match scenario-based data units with the same or adjacent region identifier content to generate spatial association groups. Spatial association groups reflect the relevant data of the same region under different application scenario types.

[0075] In this embodiment, based on the spatial association dimension, the region identifier content is extracted from the format-converted scenario-based data units, such as region identifiers like "Hillary Gentle Slope Region A" and "Hillary Steep Slope Region B". Scenario-based data units with the same region identifier content are matched, such as scenario-based data units where both are identified as "Hillary Gentle Slope Region A". Scenario-based data units with adjacent region identifier content are also matched, such as scenario-based data units where the region identifiers are adjacent to both "Hillary Gentle Slope Region A" and "Hillary Steep Slope Region B". Spatial association groups are generated, reflecting related data for the same region under different application scenario types. For example, the spatial association group for "Hillary Gentle Slope Region A" includes scenario-based data units at different vegetation cover stages in that region.

[0076] Step S1333: Based on the time association dimension, extract the execution identifier content and evolution identifier content from the format-converted contextualized data units, and match the contextualized data units corresponding to the execution identifier content and evolution identifier content that have a time sequence or synchronous relationship to generate time association groups. The time association groups reflect related data within the same time period.

[0077] In this embodiment, based on the time correlation dimension, execution identifiers and evolution identifiers are extracted from the converted scenario-based data units. Execution identifiers include the execution time phase of land leveling activities, and evolution identifiers include the time phase of vegetation cover changes. Scenario-based data units corresponding to execution identifiers and evolution identifiers with a temporal order are matched, such as scenario-based data units where the land leveling execution time phase precedes the vegetation cover change time phase. Scenario-based data units corresponding to execution identifiers and evolution identifiers with a synchronous relationship are also matched, such as scenario-based data units where the soil improvement execution time phase is synchronized with the soil organic matter content change time phase. Time correlation groups are generated, reflecting related data within the same time period, such as a time correlation group composed of scenario-based data units related to land leveling activities and vegetation cover changes within a certain time period.

[0078] Step S1334: Based on the content association dimension, extract the adaptation identifier content from the format-converted contextualized data units, and match the contextualized data units corresponding to the adaptation identifier content with corresponding adaptation relationships to generate content association groups. The content association groups reflect the data related to the rectification behavior and remediation measures.

[0079] In this embodiment, based on the content association dimension, the adaptation identifier content in the format-converted scenario-based data units is extracted, such as the adaptation identifier for land leveling behavior and herbaceous plant intercropping scheme, and the adaptation identifier for soil improvement behavior and shrub and herb intercropping scheme. Scenario-based data units corresponding to the adaptation identifier content with corresponding adaptation relationships are matched, such as scenario-based data units for land leveling behavior and herbaceous plant intercropping scheme, and scenario-based data units for soil improvement behavior and shrub and herb intercropping scheme. Content association groups are generated, reflecting data related to remediation behaviors and restoration measures, such as content association groups composed of scenario-based data units for land leveling behavior and herbaceous plant intercropping scheme.

[0080] Step S1335: According to the priority of the association dimension, the spatial association group, the temporal association group and the content association group are hierarchically integrated to form a multi-dimensional association set, which contains a combination of scenario-based data units under different association dimensions.

[0081] In this embodiment, spatial association groups, temporal association groups, and content association groups are hierarchically integrated according to the priority of the association dimensions, such as spatial association dimension having higher priority than temporal association dimension, and temporal association dimension having higher priority than content association dimension. For example, spatial association groups are integrated first, then temporal association groups are integrated based on spatial association groups, and finally content association groups are integrated based on temporal association groups to form a multi-dimensional association set. The multi-dimensional association set contains scenario-based data unit combinations under different association dimensions, such as a scenario-based data unit combination with the region identifier being hilly and gentle slope area A, the time stage being a certain time period, and the content being land leveling behavior and herbaceous plant mixed sowing scheme.

[0082] Step S1336: Assign an association identifier to each association group and multi-dimensional association set. Each association identifier is generated by combining the core features and association dimension information of its corresponding association group and adding a unique identification field.

[0083] In this embodiment, association identifiers are assigned to each association group and multi-dimensional association set. For example, an association identifier is assigned to a spatial association group, which is generated by combining the core features of the spatial association group, such as the region identifier being a hilly and gentle slope region A, and the association dimension information, such as spatial association dimensions; an association identifier is assigned to a temporal association group, which is generated by combining the core features of the temporal association group, such as the time stage being a certain time period, and the association dimension information, such as time association dimensions; an association identifier is assigned to a content association group, which is generated by combining the core features of the content association group, such as land leveling behavior and herbaceous plant mixed sowing scheme, and the association dimension information, such as content association dimensions; an association identifier is assigned to a multi-dimensional association set, which is generated by combining the core features of the multi-dimensional association set, such as the region identifier being a hilly and gentle slope region A, the time stage being a certain time period, the content being land leveling behavior and herbaceous plant mixed sowing scheme, and the association dimension information, such as spatial + temporal + content association dimensions. A unique identification field is added to each association identifier to ensure the uniqueness of the association identifier.

[0084] Step S1337: Based on the association identifier, construct the information association link between different scenario-based data units after format conversion. The information association link includes the association start point, association end point and association dimension identifier. The association start point and association end point correspond to the scenario-based data units participating in the association, and the association dimension identifier corresponds to the dimension type to which the association belongs.

[0085] In this embodiment, information association links between different scenario-based data units after format conversion are constructed based on association identifiers. For example, based on the association identifiers of spatial association groups, information association links are constructed between scenario-based data units with the region identifier "hilly and gentle slope region A," with the association starting point being a certain scenario-based data unit and the association ending point being another scenario-based data unit, and the association dimension identifier being the spatial association dimension; based on the association identifiers of temporal association groups, information association links are constructed between scenario-based data units with a time period, with the association starting point being a certain scenario-based data unit and the association ending point being another scenario-based data unit, and the association dimension identifier being the temporal association dimension; based on the association identifiers of content association groups, information association links are constructed between scenario-based data units of land leveling behavior and herbaceous plant mixed sowing scheme, with the association starting point being a certain scenario-based data unit and the association ending point being another scenario-based data unit, and the association dimension identifier being the content association dimension; based on the association identifiers of multi-dimensional association sets, information association links are constructed between scenario-based data units with multi-dimensional associations, with the association starting point being a certain scenario-based data unit and the association ending point being another scenario-based data unit, and the association dimension identifier being the multi-dimensional association.

[0086] Step S134: Based on the interaction trigger mapping relationship, bind the corresponding visualization carrier interaction entry to the interaction attribute identifier of each format-converted scenario-based data unit. The binding includes comparing the interaction trigger type and the entry operation method to make them correspond, and verifying the interaction response method and the entry feedback effect to make them match.

[0087] In this embodiment, based on the interaction trigger mapping relationship, the interaction attribute identifier of each format-converted scenario-based data unit is bound to the corresponding visualization carrier interaction entry. For example, the interaction trigger mapping relationship is that a click operation corresponds to a clickable area entry, and a swipe operation corresponds to a swipeable timeline entry. The interaction trigger type of the scenario-based data unit is compared with the entry operation mode of the visualization carrier interaction entry; for example, the interaction trigger type of "click" corresponds to the entry operation mode of "clickable area". The interaction response mode of the scenario-based data unit is verified with the entry feedback effect of the visualization carrier interaction entry; for example, the interaction response mode of "highlighting specific scheme distribution" matches the entry feedback effect of "area highlighting". After the binding is completed, a corresponding relationship is formed between the interaction attribute identifier of the scenario-based data unit and the interaction entry of the visualization carrier.

[0088] Step S135: Extract core data content from the format-converted scenario-based data units. Extraction is achieved by filtering core fields and merging duplicate information, and the data is processed according to the information display requirements of the visualization carrier to retain core information features.

[0089] In this embodiment, core data content is extracted from the converted scenario-based data units. For example, core fields in the scenario-based data units are filtered, such as vegetation coverage data, soil organic matter content data, and soil erosion intensity data; duplicate information, such as duplicate regional identification information and duplicate time period information, is merged. The data is then processed according to the information display requirements of the visualization platform, such as converting vegetation coverage data into a time-series format and soil organic matter content data into a regional distribution format, to retain core information features.

[0090] Step S136: Integrate the processed core data content with the information association links and interaction entry binding information, and combine them according to the storage order of the data embedding format to generate a data embedding package. The data embedding package contains the format-converted scenario-based data units, information association links, and interaction entry binding information.

[0091] In this embodiment, the processed core data content is integrated with information association links and interaction entry binding information. For example, core data content such as vegetation coverage data arranged by time series and soil organic matter content data arranged by regional distribution are integrated with information association links such as spatial association links, temporal association links, and content association links, as well as interaction entry binding information such as click interaction entry binding information and swipe interaction entry binding information. The data is then combined according to the storage order of the data embedding format, such as first storing the format-converted contextualized data units, then storing the information association links, and finally storing the interaction entry binding information to generate a data embedding package.

[0092] Step S137: Write the data embedding package into the designated storage area of ​​the visualization carrier through the storage interface of the visualization carrier, and arrange it according to the storage order in the presentation rules to maintain the integrity and relevance of the data embedding package.

[0093] In this embodiment, the data embedding package is written to a designated storage area through the storage interface of the visualization carrier. For example, the designated storage area of ​​the visualization carrier containing clickable area identifiers is set as the area data storage area, and the data embedding package is written to this area data storage area; the designated storage area of ​​the visualization carrier containing a scrollable timeline is set as the time data storage area, and the data embedding package is written to this time data storage area. The data is arranged according to the storage order in the presentation rules, such as arranging the format-converted contextualized data units according to the order of area identifiers, arranging the information association links according to the order of association dimensions, and arranging the interaction entry binding information according to the order of interaction trigger types, to maintain the integrity and relevance of the data embedding package.

[0094] Step S138: Conduct interactive response tests on the visualization carrier after embedding the data. The tests are implemented by simulating different types of interactive operations to verify the correspondence between interactive operations and data display, check the accuracy of information association, detect the smoothness of dynamic response, and adjust the data embedding method according to the test results to generate basic visualization data with interactive response capabilities.

[0095] In this embodiment, interactive response testing is performed on the visualization carrier after embedding data. For example, simulated click operations test the correspondence between interactive operations and data display, such as whether the core data content of the corresponding area is displayed after clicking on an area marker; simulated swiping operations test the accuracy of information association, such as whether the contextualized data units associated with the corresponding time stage are displayed after swiping the timeline; simulated zooming operations test the smoothness of dynamic response, such as whether the core data content is smoothly displayed at different zoom ratios when zooming in on the spatial distribution. Based on the test results, the data embedding method is adjusted, such as adjusting the storage order of the data embedding package and adjusting the association rules of the information association links, to generate basic visualization data with interactive response capabilities.

[0096] Step S140: Capture user interaction behavior of the visual basic data with interactive response capabilities, extract the set of interactive feature information corresponding to the user interaction behavior, and form an association mapping between the set of interactive feature information and the presentation form of the visualization carrier.

[0097] In this embodiment, user interaction behaviors on the visualized basic data are captured, such as clicking clickable area markers, sliding the slideable timeline, and zooming in and out of spatial distribution. A set of interaction feature information corresponding to the user's interaction behavior is extracted. This set of interaction feature information includes information such as the type, location, time, and duration of the interaction behavior. A mapping is formed between the set of interaction feature information and the presentation format of the visualization carrier; for example, clicking is associated with a visualization carrier presentation format containing clickable area markers, and sliding is associated with a visualization carrier presentation format containing a slideable timeline.

[0098] Step S141: Activate the interactive behavior capture module of the visualization basic data with interactive response capability. The activation of the interactive behavior capture module combines the interactive response speed and storage capacity of the visualization carrier. The module is configured to operate according to the set capture frequency and capture range. The capture frequency is used to record user interaction operations in real time, and the capture range covers all interactive entry points of the visualization carrier.

[0099] In this embodiment, the interactive behavior capture module is activated. Upon activation, the interaction response speed and storage capacity of the visualization platform are considered to ensure that the operation of the capture module does not affect the interaction response speed and storage stability of the visualization platform. The interactive behavior capture module is configured to operate at a set capture frequency, which is set to record user interactions in real time. The capture range is configured to cover all interactive entry points of the visualization platform, such as clickable area markers, scrollable timelines, zoom control buttons, etc.

[0100] Step S142: Record all user operations on the visual carrier through the interaction behavior capture module to generate an interaction behavior log. The interaction behavior log includes operation time, operation location, operation type and operation number. The operation time corresponds to the moment when the interaction occurs, the operation location corresponds to the specific location of the interaction entry, the operation type corresponds to the triggered interaction action, and the operation number corresponds to the trigger frequency of the same interaction entry.

[0101] In this embodiment, the interaction behavior capture module records all user operations to generate an interaction behavior log. For example, the log records an operation at a certain time, at the clickable area marker A, with the operation type being a click and the number of operations being a certain number; it also records an operation at a certain time, at the position on a scrollable timeline, with the operation type being a swipe and the number of operations being a certain number; and it further records an operation at a certain time, at the position of the zoom control button, with the operation type being zoom and the number of operations being a certain number.

[0102] Step S143: Extract the operation type from the interaction behavior log to distinguish different interaction actions, which include click operations, swipe operations, zoom operations and toggle operations.

[0103] In this embodiment, an operation type field is extracted from the interaction behavior log to distinguish different interaction actions. For example, an interaction action with the operation type field "click" is a click operation, an interaction action with the operation type field "swipe" is a swipe operation, an interaction action with the operation type field "zoom" is a zoom operation, and an interaction action with the operation type field "toggle" is a toggle operation.

[0104] Step S144: Analyze the correspondence between the operation location and the interaction entry of the visualization carrier. By matching the coordinates of the operation location with the coordinate range of the interaction entry, determine the interaction entry corresponding to each operation behavior, and then associate the corresponding contextual data unit and interaction attribute identifier.

[0105] In this embodiment, the correspondence between the operation location and the interaction entry point of the visualization carrier is analyzed. For example, the coordinates of the operation location are obtained, such as the coordinates of a certain pixel; the coordinate range of the interaction entry point is obtained, such as the coordinate range of the clickable area identifier A being from a certain pixel coordinate to a certain pixel coordinate. The coordinates of the operation location are matched with the coordinate range of the interaction entry point. If the coordinates of the operation location are within the coordinate range of the clickable area identifier A, then the interaction entry point corresponding to the operation behavior is determined to be the clickable area identifier A. Then, the corresponding contextual data unit and interaction attribute identifier are associated, such as the clickable area identifier A being associated with the contextual data unit corresponding to the scene identifier U and the interaction attribute identifier with the interaction trigger type being click.

[0106] Step S145: Count the number of occurrences of each operation type in the interaction behavior log to analyze the usage of different operation types.

[0107] In this embodiment, the frequency of each operation type in the interaction behavior log is counted. For example, the frequency of clicks, swipes, zooms, and toggles is counted. The usage of different operation types is analyzed based on the statistical results. For instance, if the click operation is used the most, it indicates that users prefer the click interaction method.

[0108] Step S146: Extract the time distribution characteristics of the operation time. By dividing the operation time into time intervals and counting the number of operations in each interval, the time pattern of user interaction behavior can be analyzed.

[0109] In this embodiment, the temporal distribution characteristics of operation time are extracted. For example, the operation time is divided into multiple time intervals, such as a certain time period in the morning to a certain time period, a certain time period in the afternoon to a certain time period, and a certain time period in the evening to a certain time period. The number of operations within each time interval is counted, such as the number of operations in the morning, the number of operations in the afternoon, and the number of operations in the evening. The statistical results are used to analyze the temporal patterns of user interaction behavior, such as whether users tend to perform interactive operations in the morning.

[0110] Step S147: Based on the operation type, corresponding interaction entry, number of uses and time distribution characteristics, the interaction feature information is structured and integrated according to the feature dimensions to construct an interaction feature information set. Each piece of information in the interaction feature information set contains the complete features of a single interaction behavior, as well as the statistical features of the overall interaction behavior.

[0111] In this embodiment, the system is structurally integrated based on operation type, corresponding interaction entry point, usage frequency, and time distribution characteristics, according to feature dimensions. For example, it is integrated according to the dimensions of operation type, interaction entry point, usage frequency, and time distribution to construct an interaction feature information set. Each piece of information in the interaction feature information set contains complete features of a single interaction behavior, such as the operation type, corresponding interaction entry point, usage frequency, and time distribution of a click operation; it also contains statistical features of the overall interaction behavior, such as the total usage frequency of all operation types, the usage frequency distribution of different interaction entry points, and the operation frequency distribution of different time intervals.

[0112] Step S1471: Classify and encode the operation types, and assign a unique type code to each operation type according to the logical order of the operation types.

[0113] In this embodiment, operation types are categorized and coded. For example, according to the logical order of operation types, click operations are assigned type code 01, swipe operations are assigned type code 02, zoom operations are assigned type code 03, and toggle operations are assigned type code 04.

[0114] Step S1472: Encode the location of the corresponding interactive entry point. Based on the spatial coordinates and hierarchical relationship of the interactive entry point, assign a unique location code to each interactive entry point according to the spatial coordinate system of the visualization carrier.

[0115] In this embodiment, the corresponding interactive entry points are encoded in location. For example, combining the spatial coordinates of the interactive entry point, the spatial coordinates of clickable area identifier A are given as a certain pixel coordinate, and the spatial coordinates of clickable area identifier B are given as a certain pixel coordinate; combining the hierarchical relationship of the interactive entry points, such as clickable area identifier A being in the first layer and clickable area identifier B being in the second layer. Based on the spatial coordinate system of the visualization carrier, a unique location code is assigned to each interactive entry point, such as the location code of clickable area identifier A being 0101 and the location code of clickable area identifier B being 0201.

[0116] Step S1473: The number of uses is segmented and statistically analyzed according to a preset time period to generate periodic usage data. The periodic usage data includes the number of uses and percentage of each operation type within different statistical periods.

[0117] In this embodiment, the usage frequency is statistically analyzed in segments according to a preset time period. For example, the preset time period is daily, weekly, monthly, etc. The daily usage frequency and percentage of each operation type are calculated to generate daily usage frequency data, the weekly usage frequency and percentage of each operation type are calculated to generate weekly usage frequency data, and the monthly usage frequency and percentage of each operation type are calculated to generate monthly usage frequency data.

[0118] Step S1474: Segment the time distribution characteristics, divide the time intervals according to the continuity of time and the user's possible usage habits, and count the number of interaction behaviors and operation types in each time interval to generate time interval distribution data.

[0119] In this embodiment, the time distribution characteristics are segmented. For example, based on the continuity of time and users' possible usage habits, time is divided into multiple intervals, such as weekday mornings, weekday afternoons, weekend mornings, and weekend afternoons. The number of interactive behaviors occurring within each time interval is counted, such as a certain number of interactive behaviors occurring on weekday mornings; the distribution of operation types within each time interval is also counted, such as a certain percentage of click operations and a certain percentage of swipe operations on weekday mornings, generating time interval distribution data.

[0120] Step S1475: Associate and bind the type code, location code, periodic usage count data and time interval distribution data with the corresponding interaction behavior log entries, establish association relationship entries through log entry identifiers, and generate a single interaction behavior feature record, wherein each single interaction behavior feature record contains all feature information of a single interaction behavior.

[0121] In this embodiment, type code, location code, periodic usage count data, and time interval distribution data are associated and bound to the corresponding interaction behavior log entries. For example, type code 01, location code 0101, daily periodic usage count data, and weekday morning time interval distribution data are associated with a certain interaction behavior log entry through the log entry identifier. By establishing these associated entries, a single interaction behavior feature record is generated. Each record contains all the feature information of a single interaction behavior, such as operation type code, interaction entry location code, periodic usage count, and time interval distribution.

[0122] Step S1476: Summarize the single interaction behavior feature records, classify and sort them according to the encoding order of operation type and the chronological order of time interval, so as to generate a set of classified and sorted feature records.

[0123] In this embodiment, the single interactive behavior feature records are summarized. The single interactive behavior feature records are categorized and sorted according to the encoding order of the operation type (e.g., 01 click operation, 02 swipe operation, 03 zoom operation, and 04 switch operation) and according to the chronological order of time intervals (e.g., weekday mornings, weekday afternoons, weekend mornings, and weekend afternoons), generating a categorized and sorted feature record set.

[0124] Step S1477: Extract statistical features from the sorted feature record set. Obtain statistical features by calculating statistical indicators and filtering core data. The statistical features include the average number of times each operation type is used, the highest number of times it is used, the main time interval distribution, and the core interaction entry distribution.

[0125] In this embodiment, statistical features are extracted from the categorized and sorted feature record set. Statistical indicators are calculated, such as the average and highest usage counts for each operation type; core data is filtered, such as the distribution across main time intervals and the distribution of core interaction entry points. Statistical features are obtained, such as the average and highest usage counts for click operations, the main time interval distribution being weekday mornings, and the core interaction entry point distribution being clickable area identifier A; the average and highest usage counts for swipe operations, the main time interval distribution being weekday afternoons, and the core interaction entry point distribution being a swipeable timeline, etc.

[0126] Step S1478: Integrate the single interactive behavior feature records and statistical features, and organize them in a structured manner according to the hierarchical relationship of the features to construct an interactive feature information set, which includes individual interactive behavior features and overall interactive behavior statistical features.

[0127] In this embodiment, individual interactive behavior feature records and statistical features are integrated. The features are organized hierarchically, with the first layer being overall interactive behavior statistical features, including the total number of uses for all operation types and the distribution of usage frequency across different interaction entry points; the second layer is individual interactive behavior features, containing feature information for each individual interactive behavior. An interactive feature information set is constructed, which includes both individual interactive behavior features and overall interactive behavior statistical features.

[0128] Step S150: Adjust the presentation parameters of the visualization carrier and the embedding method of the scenario-based data units in the scenario-based data unit set carrying interactive attribute identifiers according to the set of interactive feature information, so as to form the final result of land management and ecological restoration data visualization for dynamic response to user interaction.

[0129] In this embodiment, the presentation parameters of the visualization carrier are adjusted based on the set of interaction feature information. For example, based on the statistical characteristic that users are more inclined to click, the size and color of the clickable area markers are adjusted to improve visibility; based on the statistical characteristic that users mainly perform interactive operations on weekday mornings, the information display density of the visualization carrier is adjusted to be high on weekday mornings. The embedding method of the contextual data units is adjusted. For example, based on the statistical characteristic that the core interaction entry is distributed as the clickable area marker A, the contextual data units of that area are preferentially embedded into the visualization carrier. This forms the final result of land consolidation and ecological restoration data visualization for dynamically responding to user interactions.

[0130] Step S151: Analyze the usage frequency data and distribution data in the statistical features of the interaction feature information set, parse the statistical features in the interaction feature information set, identify the operation types and core interaction entry points whose usage frequency meets the preset conditions, and determine the interactive functions and data content that users focus on.

[0131] In this embodiment, the usage frequency data and distribution data in the statistical features of the interaction feature information set are analyzed. The statistical features are parsed to identify operation types whose usage frequency meets preset conditions, such as click operations whose usage frequency exceeds a certain threshold; the core interaction entry points whose usage frequency meets preset conditions are also identified, such as clickable area identifier A whose usage frequency exceeds a certain threshold. The interactive function that the user focuses on is determined to be a clickable interactive function, and the data content that the user focuses on is the core data content of the contextualized data unit corresponding to clickable area identifier A.

[0132] Step S152: Based on the interactive functions that users focus on, adjust the information display density parameter value in the core presentation parameters of the visualization carrier, including increasing the information display density parameter value of the contextualized data unit corresponding to the core interactive entry, and adjusting the information display density parameter value of the non-focused area.

[0133] In this embodiment, based on the click-based interactive functions that users focus on, the information display density parameter value in the core presentation parameters of the visualization carrier is adjusted. The information display density parameter value of the contextualized data unit corresponding to the core interactive entry point is increased; for example, the information display density of the contextualized data unit corresponding to the clickable area identifier A is adjusted from medium to high. The information display density parameter value of non-focused areas is adjusted; for example, the information display density of the contextualized data unit corresponding to non-core interactive entry points is adjusted from medium to low.

[0134] Step S153: Based on the location distribution of the core interactive entry, adjust the coordinate parameters and arrangement rules of the spatial layout of the visualization carrier, including adjusting the area where the core interactive entry is located to a preset easy-to-operate location range, and adjusting the spatial arrangement order of the relevant data.

[0135] In this embodiment, based on the location distribution of the core interactive entry point, such as the clickable area identifier A being located in the left area of ​​the visualization carrier, the coordinate parameters and arrangement rules of the spatial layout of the visualization carrier are adjusted. The area where the core interactive entry point is located is adjusted to a preset easily operable location range, such as moving the clickable area identifier A from the left area to the middle area; the spatial arrangement order of related data is also adjusted, such as moving the arrangement order of the core data content corresponding to the clickable area identifier A from the last to the first.

[0136] Step S154: Based on the time interval distribution data, adjust the configuration parameters of the dynamic response speed of the visualization carrier, including adjusting the dynamic response speed parameters within the time interval of the user interaction action set, and maintaining the basic dynamic response speed parameters in other time intervals.

[0137] In this embodiment, based on time interval distribution data, such as when user interaction actions are concentrated in the morning of a weekday, the configuration parameters of the dynamic response speed of the visualization carrier are adjusted. During the time interval where user interaction actions are concentrated, the dynamic response speed parameter is adjusted from the base speed to a high speed; in other time intervals, the dynamic response speed parameter remains at the base speed.

[0138] Step S155: Based on the periodic usage count data, adjust the storage path and priority parameters of the embedding method of the scenario-based data unit, including adopting a priority embedding strategy for scenario-based data units whose usage count meets the preset conditions, and storing them in the high-speed access area of ​​the visualization carrier.

[0139] In this embodiment, based on the periodic usage data, such as if the weekly usage of the scenario-based data unit corresponding to the clickable area identifier A meets preset conditions, the storage path and priority parameters of the embedding method of the scenario-based data unit are adjusted. A priority embedding strategy is adopted for scenario-based data units whose usage meets the preset conditions, adjusting their embedding order to be prioritized over other scenario-based data units; they are stored in the high-speed access area of ​​the visualization carrier to improve data retrieval speed.

[0140] Step S156: Optimize the information association links between contextual data units in the contextual data unit set carrying interactive attribute identifiers according to the association relationships of different operation types, enhance the connectivity entries of the information association links corresponding to the focus operation type, and simplify the number of nodes in the data call path.

[0141] In this embodiment, based on the association relationships of different operation types, such as the association relationship between click and swipe operations (a click operation is often followed by a swipe operation), the information association links between scenario-based data units are optimized. The number of connected entries in the information association links corresponding to focus operation types is enhanced, for example, increasing the number of connected entries in the information association links corresponding to click operations; the number of nodes in the data call path is simplified, for example, reducing the number of intermediate nodes in the information association links corresponding to click operations.

[0142] For example, step S1561: Based on the single interactive behavior feature record in the interactive feature information set, determine the combination of consecutively occurring operation types and their associated order.

[0143] In this embodiment, based on single interactive behavior feature records, combinations of consecutively occurring operation types and their associated order are determined. For example, consecutively occurring operation types are extracted from single interactive behavior feature records, such as combinations of click operations followed by swipe operations, or combinations of swipe operations followed by zoom operations; the associated order of these operation type combinations is determined, such as the order of click operations first followed by swipe operations, or the order of swipe operations first followed by zoom operations, etc.

[0144] Step S1562: Associate the interaction entry point corresponding to the operation type and the contextual data unit in the contextual data unit set carrying interaction attribute identifiers. Based on the association order of the operation type, locate the contextual data unit in the contextual data unit set carrying interaction attribute identifiers and the information association link, and determine the information association link part involved in the continuous operation.

[0145] In this embodiment, the interaction entry point and contextualized data unit corresponding to the operation type are associated. For example, the interaction entry point for a click operation is the clickable area identifier A, and the associated contextualized data unit is the contextualized data unit corresponding to scene identifier U; the interaction entry point for a swipe operation is the swipeable timeline, and the associated contextualized data unit is the contextualized data unit corresponding to scene identifier V. Based on the association order of the operation types, such as the order of click operation first and swipe operation later, the corresponding contextualized data unit and information association link are located, and the information association link part involved in the continuous operation is determined, such as the information association link part from the contextualized data unit corresponding to scene identifier U to the contextualized data unit corresponding to scene identifier V.

[0146] Step S1563: Count the occurrence frequency of each association sequence combination to determine the association sequence combination whose occurrence frequency meets the preset conditions.

[0147] In this embodiment, the frequency of occurrence of each associated sequence combination is counted. For example, the frequency of occurrence of the combination of clicking followed by swiping is counted to a certain number, and the frequency of occurrence of the combination of swiping followed by zooming is counted to a certain number, etc. Associative sequence combinations whose frequency of occurrence meets preset conditions are determined, such as the combination of clicking followed by swiping with an occurrence frequency exceeding a certain threshold.

[0148] Step S1564: Match the correspondence between the association sequence combinations whose occurrence frequency meets the preset conditions and the information association links, extract the information association link parts corresponding to the association sequence combinations whose occurrence frequency meets the preset conditions, and analyze the current connectivity status and data transmission path of these information association link parts.

[0149] In this embodiment, the correspondence between the associated sequence combinations whose occurrence frequency meets preset conditions and the information association links is matched. For example, the combination of clicking and then swiping corresponds to the information association link from scene identifier U to scene identifier V. The information association link portion corresponding to the associated sequence combination is extracted, and its current connectivity status is analyzed, such as whether there are any disconnected nodes; its data transmission path is analyzed, such as the number and order of nodes through which the data transmission passes.

[0150] Step S1565: Optimize the information association link, including removing redundant nodes that do not affect the association logic, in order to simplify the link structure and reduce the number of intermediate nodes in the data transmission process.

[0151] In this embodiment, the information association link is optimized. For example, redundant nodes in the information association link that do not affect the association logic are removed, such as nodes that are only used for identification but do not participate in data transmission. The link structure is simplified to reduce the number of intermediate nodes in the data transmission process, such as adjusting the data transmission path that originally passed through multiple intermediate nodes to a data transmission path that passed through a few intermediate nodes.

[0152] Step S1566: Adjust the connection method between nodes and the connection order of data transmission paths, and use connection paths with fewer nodes.

[0153] In this embodiment, the connection method between nodes is adjusted, such as changing indirect connection to direct connection; the connection order of data transmission path is adjusted, and a connection path with fewer nodes is adopted, such as changing the transmission path through node A, node B, and node C to a transmission path through node A and node C.

[0154] Step S1567: Set priority levels according to the occurrence frequency of the associated sequence combination, assign transmission priority to the optimized information association link part, so that the associated data corresponding to the associated sequence combination whose occurrence frequency meets the preset conditions is transmitted and displayed first.

[0155] In this embodiment, priority levels are set according to the frequency of occurrence of associated sequence combinations. For example, the associated sequence combination with the highest frequency of occurrence is set to the highest priority, and the next most frequent combination is set to the second highest priority. Transmission priorities are assigned to the optimized information association links. For example, the highest priority is assigned to the information association link corresponding to the combination of click operation followed by swipe operation, so that the associated data corresponding to this associated sequence combination is transmitted and displayed first.

[0156] Step S1568: Verify the overall performance of the optimized information association link through a test environment, including the response time of test association data calls, transmission stability and logical accuracy, so that the optimized information association link meets the transmission and display requirements corresponding to the association sequence combination.

[0157] In this embodiment, the overall performance of the optimized information association link is verified through a test environment. The test includes: testing the response time for calling associated data (e.g., whether the response time for calling associated data corresponding to a combination of click and swipe operations is within a preset range); testing transmission stability (e.g., whether multiple calls to associated data can be successfully transmitted); and testing logical accuracy (e.g., whether the transmitted associated data corresponds to the associated order combination). Based on the verification results, the information association link is adjusted to ensure that the optimized link meets the transmission and display requirements.

[0158] Step S157: Adjust the style parameters and display rules of the interactive entry style, including using a preset eye-catching operation prompt for the core interactive entry, and maintaining the uniformity of the style of non-core interactive entry.

[0159] In this embodiment, the style parameters and display rules of the interactive entry points are adjusted. Core interactive entry points use a preset, eye-catching operation prompt format, such as adjusting the color of the clickable area identifier A to a bright color and adding a dynamic flashing effect; non-core interactive entry points maintain a consistent style, such as using a uniform gray color and no dynamic effects for non-core interactive entry points.

[0160] Step S158: Test the effectiveness of the adjusted visualization carrier and data embedding method in a simulated environment. The test includes simulating user interaction behavior to verify the rationality of information display, the convenience of spatial layout, the smoothness of dynamic response, and the efficiency of data retrieval. Fine-tune the results based on the test to form the final result of land management and ecological restoration data visualization.

[0161] In this embodiment, the effects of the adjusted visualization carrier and data embedding method are tested in a simulated environment. User interaction behaviors are simulated, such as clicking on clickable area marker A and swiping the scrollable timeline. The rationality of information display is verified, such as whether core data content is clearly displayed; the convenience of spatial layout is verified, such as whether the core interaction entry point is in an easily accessible location; the smoothness of dynamic response is verified, such as whether the response after an interaction is delayed; and the efficiency of data retrieval is verified, such as whether the retrieval of related data is fast. Based on the test results, fine-tuning is performed, such as adjusting the size of clickable area marker A and adjusting the dynamic response speed, to form the final result of land consolidation and ecological restoration data visualization.

[0162] In one exemplary embodiment, a visualization system based on land consolidation and ecological restoration data is provided. This visualization system can be a terminal, server, etc., and its internal structure diagram can be as follows: Figure 2As shown, this visualization system based on land consolidation and ecological restoration data includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, near-field communication, or other technologies. When the computer program is executed by the processor, it implements a visualization method based on land consolidation and ecological restoration data. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the shell of a visualization system based on land consolidation and ecological restoration data, or an external keyboard, touchpad, or mouse, etc.

[0163] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A visualization method based on land consolidation and ecological restoration data, characterized in that, The method includes: The land consolidation data and ecological restoration data are encapsulated in a scenario-based manner to generate a set of scenario-based data units carrying interactive attribute identifiers. The land consolidation data includes the characteristic information of the consolidation area and the execution information of the consolidation behavior, and the ecological restoration data includes the restoration status evolution information and the restoration measure adaptation information. The interactive attribute identifiers correspond to the visual interactive triggering conditions of the data units. Based on the interactive attribute identifiers in the set of scenario-based data units carrying interactive attribute identifiers, a visualization carrier adapted to the scenario requirements is dynamically generated. The presentation form of the visualization carrier is associated with the interactive attribute identifiers in the set of scenario-based data units carrying interactive attribute identifiers. The set of contextualized data units carrying interactive attribute identifiers is embedded according to the presentation rules of the visualization carrier to generate basic visualization data with interactive response capabilities. The basic visualization data retains the complete interactive attribute identifiers of the contextualized data units in the set of contextualized data units carrying interactive attribute identifiers. Capture user interaction behaviors of visual basic data with interactive response capabilities, extract the set of interactive feature information corresponding to the user interaction behaviors, and form an association mapping between the set of interactive feature information and the presentation form of the visualization carrier; Based on the set of interactive feature information, the presentation parameters of the visualization carrier and the embedding method of the scenario-based data units in the set of scenario-based data units carrying interactive attribute identifiers are adjusted to form the final result of land management and ecological restoration data visualization for dynamic response to user interaction. The process of encapsulating land consolidation data and ecological restoration data into scenario-based data units to generate a set of scenario-based data units carrying interactive attribute identifiers includes: Extract regional feature information from land consolidation data, identify regional identifiers that represent the geographic attributes of the region in the regional feature information, and the regional identifiers correspond to the spatial distribution and boundary features of the consolidation area. The regional identifiers are obtained by parsing the geographic description fields in the regional feature information. Extract the execution information of land consolidation actions from the land consolidation data, capture the execution identifier content that represents the implementation process of the actions in the execution information of the land consolidation actions, the execution identifier content corresponds to the implementation steps and execution sequence of the consolidation actions, and obtain the execution identifier content by extracting the process record field in the execution information of the land consolidation actions; Extract restoration status evolution information from ecological restoration data, track evolution identifiers that characterize the state change process in restoration status evolution information, the evolution identifiers correspond to the stage changes and transition characteristics of ecological restoration status, and obtain the evolution identifiers by analyzing the state record fields in restoration status evolution information. Extract the adaptation information of restoration measures from the ecological restoration data, and lock the adaptation identifier content that represents the correlation between the measures in the adaptation information. The adaptation identifier content corresponds to the matching features between the restoration measures and the remediation behavior. The adaptation identifier content is obtained by interpreting the correlation record field in the adaptation information of the restoration measures. Based on the spatial attributes of regional identifiers and the temporal attributes of evolution identifiers, the application scenario types of land consolidation and ecological restoration data are divided, and different application scenario types correspond to different combinations of regional characteristics and restoration status. Perform combined operations on the core feature fields of each application scenario type to generate a unique scenario identifier. Assign a scenario identifier to the dataset corresponding to each application scenario type. Add a unique identification field to each scenario identifier. This unique identification field is generated by combined operations on the core feature fields of the application scenario type. Based on the usage scenario characteristics of application scenario types, an interaction attribute identifier is defined for the dataset corresponding to each scenario identifier. The interaction attribute identifier includes the interaction trigger type, the interaction response method, and the interaction feedback content. The interaction trigger type corresponds to the user's operation behavior, the interaction response method corresponds to the adjustment method of the visualization presentation, and the interaction feedback content corresponds to the information feedback form after the operation. The datasets with scene identifiers and interaction attribute identifiers are arranged in logical order according to the application scenario type. The datasets with scene identifiers and interaction attribute identifiers are encapsulated into independent scenario-based data units. All independent scenario-based data units are integrated to generate a set of scenario-based data units carrying interaction attribute identifiers. Each independent scenario-based data unit establishes an association entry through scene identifiers and interaction attribute identifiers.

2. The visualization method based on land consolidation and ecological restoration data according to claim 1, characterized in that, The dynamic generation of a visualization carrier adapted to scenario requirements based on the interaction attribute identifiers in the contextualized data unit set carrying interaction attribute identifiers includes: The interaction attribute identifier of each scenario-based data unit in the scenario-based data unit set carrying interaction attribute identifiers is split into fields. The interaction attribute identifiers in the scenario-based data unit set carrying interaction attribute identifiers are parsed, and the interaction trigger type, interaction response method and interaction feedback content in each interaction attribute identifier are extracted to determine the interaction requirements corresponding to different scenario-based data units in the scenario-based data unit set carrying interaction attribute identifiers. The interactive trigger types are categorized according to their operation logic and implementation methods. Visual presentation forms are divided into categories based on the differences in interactive trigger types. Different categories of visual presentation forms correspond to different combinations of interactive trigger types. The categories of visual presentation forms are adapted to the corresponding interactive operation requirements. Based on the implementation requirements and technical specifications of interactive response methods, the core presentation parameters for each type of visualization presentation are determined according to the characteristics of interactive response methods. These core presentation parameters include information display density, spatial layout method, and dynamic response speed. For each type of visualization presentation, the interactive entry style is determined based on the interactive feedback content. The interactive entry style includes the trigger area location, the operation prompt format, and the feedback display location. Establish a correspondence between scenario features of application scenario types and visualization presentation categories. Based on the scenario features of application scenario types, match the corresponding visualization presentation category for each scenario data unit in the set of scenario data units carrying interactive attribute identifiers. The matching is based on scenario identifiers and interactive attribute identifiers. Perform the carrier construction operation according to the input core presentation parameters and interaction entry style requirements. Based on the matching results, call the visualization carrier generation tool, input the core presentation parameters and interaction entry style, and generate an initial visualization carrier with basic interactive response capabilities. The initial visualization carrier is verified by simulating interactive operations to verify the fit of its interactive attribute identifiers. The verification includes the correspondence between the interactive trigger type and the interactive entry style, the adaptability between the interactive response method and the core presentation parameters, and the consistency between the interactive feedback content and the display format. Based on the fit verification results, the core presentation parameters and interactive entry styles of the initial visualization carrier are adjusted so that the adjusted visualization carrier and the interactive attribute identifiers of the contextualized data units are associated accordingly.

3. The visualization method based on land consolidation and ecological restoration data according to claim 2, characterized in that, The implementation requirements and technical specifications of the combined interactive response methods are used to determine the core presentation parameters for each type of visualization presentation based on the characteristics of the interactive response methods, including: Based on the functional characteristics of interactive response methods, interactive response methods are classified into display-type responses involving information display adjustments, layout-type responses involving spatial layout changes, and dynamic-type responses involving dynamic effects presentation. Different types of responses correspond to different implementation requirements. Based on the information processing logic of the display response, a corresponding setting standard is generated. The scope and depth of information adjustment for the display response are analyzed, and the setting standard of information display density is determined. The setting standard of information display density satisfies the implementation of information addition, subtraction and detail expansion operations in the display response. Based on the spatial variation pattern of layout-type responses, corresponding design rules are generated. The dimensions and methods of spatial variation of layout-type responses are analyzed to determine the design rules of spatial layout methods. The design rules of spatial layout methods support the implementation of area switching and position adjustment operations in layout-type responses. Based on the time-varying characteristics of dynamic responses, corresponding configuration parameters are generated. The speed and form of the dynamic effects of dynamic responses are analyzed to determine the configuration parameters for dynamic response speed. The configuration parameters for dynamic response speed are matched to the implementation of animation display and real-time update operations in dynamic responses. The parameters are structurally combined according to their functional relationships, and the setting standards for information display density, the design rules for spatial layout, and the configuration parameters for dynamic response speed are integrated to form a core presentation parameter framework for each type of visualization presentation. The core presentation parameter framework includes parameter type, parameter range, and parameter adjustment rules. Based on the scene characteristics of the application scene type corresponding to the scene identifier, the parameter range in the core presentation parameter framework is adjusted by expanding or narrowing the parameter value range, so that the parameter range can adapt to the information display needs under different application scene types. The core presentation parameters are prioritized based on the degree of impact of the interaction response method on the overall visualization effect, and the parameters corresponding to the key interaction response methods are optimized first. Test environments with different parameter combinations are constructed for testing. The performance of the core presentation parameters under different interactive response methods is verified through parameter simulation. Based on the verification results, the core presentation parameters for each type of visualization are finally determined.

4. The visualization method based on land consolidation and ecological restoration data according to claim 1, characterized in that, The process of embedding the set of contextualized data units carrying interactive attribute identifiers into the visualization carrier according to the presentation rules of the visualization carrier to generate basic visualization data with interactive response capabilities includes: The presentation rules of the visualization carrier are extracted by parsing the configuration file of the visualization carrier. The presentation rules include data embedding format, information association method and interaction trigger mapping relationship. The data embedding format specifies the storage structure of the scenario-based data unit, the information association method specifies the association logic between different scenario-based data units, and the interaction trigger mapping relationship specifies the corresponding rules of interactive operation and data call. According to the field requirements and encoding specifications of the data embedding format, each scenario-based data unit in the scenario-based data unit set is subjected to format conversion processing so that the converted scenario-based data unit can adapt to the storage requirements of the visualization carrier, and the conversion process retains the scenario identifier, interactive attribute identifier and original data content of the scenario-based data unit. According to the logical rules of the information association method, the format-converted scenario-based data units are associated to establish information association links between different scenario-based data units. The information association links are based on scenario identifiers and interaction attribute identifiers. Based on the interaction trigger mapping relationship, the interaction attribute identifier of each format-converted scenario-based data unit is bound to the corresponding visualization carrier interaction entry. The binding includes comparing the interaction trigger type and the entry operation method to make them correspond, and verifying the interaction response method and the entry feedback effect to make them match. The core data content is extracted from the contextualized data units after format conversion. The extraction is achieved by filtering core fields and merging duplicate information, and is processed according to the information display requirements of the visualization carrier to retain the core information features. The processed core data content is integrated with the information association links and interaction entry binding information, and combined according to the storage order of the data embedding format to generate a data embedding package. The data embedding package includes the format-converted scenario-based data units, information association links, and interaction entry binding information. The data embedding package is written into the designated storage area of ​​the visualization carrier through the storage interface of the visualization carrier, and arranged according to the storage order in the presentation rules, so as to maintain the integrity and relevance of the data embedding package. Interactive response tests are conducted on the visualization carrier after embedding data. The tests are implemented by simulating different types of interactive operations to verify the correspondence between interactive operations and data display, check the accuracy of information association, detect the smoothness of dynamic response, and adjust the data embedding method according to the test results to generate the visualization base data with interactive response capabilities.

5. The visualization method based on land consolidation and ecological restoration data according to claim 4, characterized in that, The step of associating the format-converted scenario-based data units according to the logical rules of the information association method to establish information association links between different scenario-based data units includes: By analyzing the information association methods, association dimensions and association rules are determined. The association dimensions include spatial association dimensions, temporal association dimensions, and content association dimensions. The association rules specify the matching conditions and association forms under different association dimensions. Based on the spatial association dimension, the regional identifier content in the scenario-based data unit after format conversion is extracted, and scenario-based data units with the same or adjacent regional identifier content are matched to generate spatial association groups. The spatial association groups reflect the relevant data of the same area under different application scenario types. Based on the time association dimension, the execution identifier content and evolution identifier content are extracted from the contextualized data units after format conversion, and the contextualized data units corresponding to the execution identifier content and evolution identifier content with time sequence or synchronous relationship are matched to generate time association groups, which reflect related data within the same time period. Based on the content association dimension, the adaptation identifier content in the contextualized data unit after format conversion is extracted, and the contextualized data unit corresponding to the adaptation identifier content with corresponding adaptation relationship is matched to generate a content association group. The content association group reflects the data related to the rectification behavior and the remediation measures. According to the priority of the association dimensions, the spatial association group, the temporal association group, and the content association group are hierarchically integrated to form a multi-dimensional association set, which contains a combination of scenario-based data units under different association dimensions. Assign an association identifier to each association group and the multi-dimensional association set. Each association identifier is generated by combining the core features and association dimension information of its corresponding association group and adding a unique identification field. Based on the association identifier, an information association link is constructed between different scenario-based data units after format conversion. The information association link includes an association start point, an association end point, and an association dimension identifier. The association start point and association end point correspond to the scenario-based data units participating in the association, and the association dimension identifier corresponds to the dimension type to which the association belongs.

6. The visualization method based on land consolidation and ecological restoration data according to claim 1, characterized in that, The process of capturing user interaction behaviors from visually responsive basic data and extracting the corresponding set of interaction feature information includes: The interactive behavior capture module of the visualization basic data with interactive response capability is activated. The activation of the interactive behavior capture module combines the interactive response speed and storage capacity of the visualization carrier. The module is configured to operate according to a set capture frequency and capture range. The capture frequency is used to record user interaction operations in real time, and the capture range covers all interactive entry points of the visualization carrier. The interactive behavior capture module records all user operations on the visual carrier to generate an interactive behavior log. The interactive behavior log includes operation time, operation location, operation type, and operation number. The operation time corresponds to the moment when the interactive behavior occurs, the operation location corresponds to the specific location of the interactive entry, the operation type corresponds to the triggered interactive action, and the operation number corresponds to the trigger frequency of the same interactive entry. The operation type is extracted from the interaction behavior log to distinguish different interaction actions, which include click operation, swipe operation, zoom operation and switch operation; The correspondence between the operation location and the interaction entry of the visualization carrier is analyzed. By matching the coordinates of the operation location with the coordinate range of the interaction entry, the interaction entry corresponding to each operation behavior is determined, and then the corresponding contextualized data unit and interaction attribute identifier are associated. The frequency of each operation type in the interaction behavior log is counted to analyze the usage of different operation types; Extract the temporal distribution characteristics of the operation time, divide the operation time into time intervals and count the number of operations in each interval to analyze the temporal patterns of user interaction behavior; Based on the operation type, corresponding interaction entry, usage frequency, and time distribution characteristics, the information is structured and integrated according to the feature dimensions to construct an interaction feature information set. Each piece of information in the interaction feature information set contains the complete features of a single interaction behavior, as well as the statistical features of the overall interaction behavior.

7. The visualization method based on land consolidation and ecological restoration data according to claim 6, characterized in that, The interaction feature information set is constructed by structurally integrating the operation type, corresponding interaction entry point, usage frequency, and time distribution characteristics according to feature dimensions, including: The operation types are classified and coded, and a unique type code is assigned to each operation type according to the logical order of the operation types; The corresponding interactive entry is location-coded, and a unique location code is assigned to each interactive entry based on the spatial coordinate system of the visualization carrier, combined with the spatial coordinates and hierarchical relationship of the interactive entry. The number of uses is segmented and statistically analyzed according to a preset time period to generate periodic usage data, which includes the number of uses and percentage of each operation type within different statistical periods. The time distribution characteristics are segmented, and intervals are divided according to the continuity of time and user usage habits. The number of interaction behaviors and the distribution of operation types in each time interval are counted to generate time interval distribution data. The type code, location code, periodic usage count data, and time interval distribution data are associated and bound with the corresponding interaction behavior log entries. The association relationship entries are established through the log entry identifier to generate a single interaction behavior feature record, wherein each single interaction behavior feature record contains all feature information of a single interaction behavior. The single interactive behavior feature records are summarized and classified and sorted according to the encoding order of operation type and the chronological order of time interval to generate a set of classified and sorted feature records. Statistical features are extracted from the sorted feature record set. By calculating statistical indicators and filtering core data, the statistical features are obtained. The statistical features include the average number of times each operation type is used, the highest number of times it is used, the main time interval distribution, and the core interaction entry distribution. The individual interactive behavior feature records and statistical features are integrated and structured according to the hierarchical relationship of the features to construct the interactive feature information set, which includes individual interactive behavior features and overall interactive behavior statistical features.

8. The visualization method based on land consolidation and ecological restoration data according to claim 1, characterized in that, The process of adjusting the presentation parameters of the visualization carrier based on the set of interactive feature information and the embedding method of the scenario-based data units in the set of scenario-based data units carrying interactive attribute identifiers forms the final result of land consolidation and ecological restoration data visualization for dynamically responding to user interactions, including: Analyze the usage frequency and distribution data in the statistical features of the interaction feature information set, parse the statistical features of the interaction feature information set, identify the operation types and core interaction entry points whose usage frequency meets the preset conditions, and determine the interactive functions and data content that users focus on. Based on the interactive functions that users focus on, the information display density parameter value in the core presentation parameters of the visualization carrier is adjusted, including increasing the information display density parameter value of the contextualized data unit corresponding to the core interactive entry, and adjusting the information display density parameter value of the non-focused area. Based on the location distribution of the core interactive entry points, the coordinate parameters and arrangement rules of the spatial layout of the visualization carrier are adjusted, including adjusting the area where the core interactive entry points are located to a preset easily operable location range, and adjusting the spatial arrangement order of related data; Based on the time interval distribution data, the configuration parameters of the dynamic response speed of the visualization carrier are adjusted, including adjusting the dynamic response speed parameters within the time interval of the user interaction action set, and maintaining the basic dynamic response speed parameters in other time intervals. Based on the periodic usage data, the storage path and priority parameters of the embedding method of the scenario-based data unit are adjusted, including adopting a priority embedding strategy for scenario-based data units whose usage frequency meets the preset conditions, and storing them in the high-speed access area of ​​the visualization carrier. Based on the association relationships of different operation types, optimize the information association links between scenario-based data units in the scenario-based data unit set carrying interactive attribute identifiers, enhance the connectivity entries of the information association links corresponding to the focus operation type, and simplify the number of nodes in the data call path. Adjust the style parameters and display rules of the interactive entry points, including using a preset eye-catching operation prompt for core interactive entry points, and maintaining a unified style for non-core interactive entry points; The effects of the adjusted visualization carrier and data embedding method are tested in a simulated environment. The test includes simulating user interaction behavior to verify the rationality of information display, the convenience of spatial layout, the smoothness of dynamic response, and the efficiency of data retrieval. Fine-tuning is then carried out based on the test results to form the final result of the land consolidation and ecological restoration data visualization.

9. A visualization system based on land consolidation and ecological restoration data, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the visualization method based on land consolidation and ecological restoration data as described in any one of claims 1 to 8 by executing the machine-executable instructions.

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