CN117555978B - Intelligent determining method for geographic model input data space range - Google Patents
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Abstract
The invention provides an intelligent determining method for the space range of input data of a geographic model, which comprises the following steps: determining the characteristics of a geographic model to be constructed and the data type of input data; formulating identifiable knowledge rules based on the characteristics of the geographic model and the data type of the input data; based on the preset identifiable knowledge rule, determining the spatial range of input data of the geographic model to be constructed by combining a heuristic modeling method; and judging whether the data source meets the content and the space range requirements of the input data or not based on the determined space range, if not, iterating the reasoning process until the input data cannot be derived by other models or the data can meet the input conditions, and if so, obtaining the workflow of the geographic model to be calculated, wherein the workflow is configured with accurate space range input. The invention solves the problems that the prior art can cause incorrect spatial range of input data in the geographic modeling process, thereby causing linkage effect and generating incorrect geographic modeling result.
Description
Technical Field
The invention relates to the technical field of geographic model construction, in particular to an intelligent determination method for the spatial range of geographic model input data.
Background
As a key step in the geographic modeling process, the preparation of input data plays a critical role in ensuring successful execution of the modeling and obtaining complete and accurate results. The process includes not only preparing the appropriate content for each input, but also the appropriate spatial extent. Since the spatial extent of the input data depends on the characteristics of the model employed and the type of data that is input accordingly, the appropriate spatial extent of the input data may often be inconsistent with the spatial extent of the output target region of interest to the user. In particular when coupling multiple geographic models into one workflow, the various inputs in the workflow can be more complex and cumbersome when properly prepared. An effective method is urgently required to relieve the burden of input data preparation in the modeling process.
Current input data preparation methods can be divided into two categories, program-oriented methods and object-oriented methods, according to the modeling paradigm employed. In a program-oriented approach, a user prepares input data starting with a search for raw data and builds a custom workflow to derive the data needed for the model, typically manually based on modeling personnel's knowledge of the specific modeling requirements. The target guiding method aims at automatically selecting proper input data and proper derivative data models from modeling targets of users, and expanding model workflow in an iterative mode, so that dependence on modeling knowledge and skills of the users is reduced to the greatest extent, and the defects of the program guiding method are relieved. The basic strategy of the target-oriented approach is to formalize the knowledge required for input data preparation and apply it to advanced geologic means.
However, existing target-oriented input data preparation methods focus on preparing the appropriate data content (semantics and type) for the model, ignoring the appropriate spatial extent of the model input data. The user often decides and sets an appropriate spatial range of input data by himself/herself, and takes the user's study area as the spatial range of input data of the model to be built (or as a model combination of workflow models), and even if it is possible to provide an output covering the study area, the accuracy of the result thereof cannot be ensured. This often results in improper spatial coverage of the input data during the geographic modeling process, thereby inducing chain effects and producing incorrect geographic modeling results.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an intelligent determination method for the input data space range of a geographic model, and solves the problems that in the prior art, the input data space range is improper in the geographic modeling process, so that the linkage effect is caused and an incorrect geographic modeling result is generated.
In order to achieve the above object, the present invention provides the following solutions:
an intelligent determining method for a geographic model input data space range, comprising the following steps:
determining the characteristics of a geographic model to be constructed and the data type of input data;
formulating identifiable knowledge rules based on the characteristics of the geographic model and the data type of the input data;
based on a preset identifiable knowledge rule and combined with a heuristic modeling method, determining the spatial range of input data of a geographic model to be constructed;
and judging whether the data source meets the content and the space range requirements of the input data or not based on the determined space range, if not, iterating the reasoning process until the input data cannot be derived by other models or the data can meet the input conditions, and if so, obtaining the workflow of the geographic model to be calculated, wherein the workflow is configured with accurate space range input.
Preferably, the method further comprises:
and carrying out correction judgment on the spatial range of the input data of the geographic model to be calculated, and if the required spatial range of the current data is inconsistent with the spatial range output by the upstream model, carrying out clipping correction on the spatial range of the current input data based on the spatial range of the current input data to obtain the geographic model to be constructed for configuring the accurate spatial range of the input data.
Preferably, the features of the geographic model include:
a specific spatial range requirement, a connectivity expansion requirement, a buffer distance expansion requirement, and a spatial range requirement to maintain a region of interest.
Preferably, the category of the input data includes:
dot data, line data, face data, and raster data.
Preferably, the determining the preset identifiable knowledge rule based on the characteristics of the geographic model and the category of the input data includes:
classifying and summarizing according to the data types of the input data and the characteristics of the geographic model to be calculated to obtain different classification combinations;
determining the requirements of the data space range appearing under the scenes of different classification combinations according to classification induction;
setting an extraction flow according to different space range requirements;
and formulating identifiable knowledge rules according to the classified and generalized data types, different space range requirements corresponding to the model characteristics of the geographic model to be constructed and the extraction flow thereof.
Preferably, the determining a data extraction process according to the classification combination includes:
determining a first space range flow, performing space search in the integrated data set to determine whether the space range of the existing data set can meet the first space range, and if the space range of the existing data set is missing, judging whether the first space range can be obtained by an integration method;
determining a second space range flow, performing global connectivity search on the data without directivity, and performing connectivity search on the data with directivity according to the direction to determine a second space range;
determining a third space range flow, extracting and tracing an upstream water collecting area according to the water flow direction based on the water flow direction calculated by the DEM to obtain a complete river basin boundary, and determining a third space range by distinguishing the water collecting area based on the upstream of the river channel from the water collecting area based on the upstream of the slope in the extracting process;
determining a fourth spatial range flow, the fourth spatial range being related to the point element, constructing a Thiessen polygon using the point data, the minimum Thiessen polygon intersecting the region of interest being determined as the fourth spatial range flow;
determining a fifth space range flow, and directly expanding outwards to determine a fifth space range through a designated buffer distance;
determining a sixth spatial range procedure, wherein the sixth spatial range is used for terrain analysis on the type of raster data, and the spatial range of the raster size of one pixel which is externally expanded is determined as a sixth spatial range;
and determining a seventh spatial range flow, wherein the seventh spatial range directly maintains the spatial range of the original interest region.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides an intelligent determining method for the space range of input data of a geographic model, which comprises the following steps: determining the characteristics of the geographic model and the data type of the input data; an identifiable knowledge rule formulated based on the characteristics of the geographic model and the data type of the input data; based on a preset identifiable knowledge rule, combining with a heuristic modeling method to determine the spatial range of input data of the geographic model to be constructed; and judging whether the data source meets the content and the space range requirements of the input data or not based on the determined space range, if not, iterating the reasoning process until the input data cannot be derived by other models or the data can meet the input conditions, and if so, obtaining the workflow of the geographic model to be calculated, wherein the workflow is configured with accurate space range input. The invention solves the problems that in the prior art, the incorrect spatial range of input data can occur in the geographic modeling process, so that the linkage effect is caused and an incorrect geographic modeling result is generated.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an intelligent determining method for a geographic model input data space range according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an intelligent determining method for a spatial range of input data of a geographic model according to an embodiment of the present invention;
FIG. 3 is a schematic view of spatial range types provided in an embodiment of the present invention;
fig. 4 is a schematic flow chart of identifiable knowledge rules provided in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide an intelligent determination method for the spatial range of input data of a geographic model, which solves the problems that in the prior art, the spatial range of the input data is improper in the geographic modeling process, so that the linkage effect is caused and an incorrect geographic modeling result is generated.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the present invention provides an intelligent determining method for a spatial range of input data of a geographic model, which includes:
step 100: determining the characteristics of a geographic model to be constructed and the data type of input data;
step 200: formulating identifiable knowledge rules based on the characteristics of the geographic model and the data type of the input data;
step 300: based on a preset identifiable knowledge rule and combined with a heuristic modeling method, determining the spatial range of input data of a geographic model to be constructed;
step 400: and judging whether the data source meets the content and the space range requirements of the input data or not based on the determined space range, if not, iterating the reasoning process until the input data cannot be derived by other models or the data can meet the input conditions, and if so, obtaining the workflow of the geographic model to be calculated, wherein the workflow is configured with accurate space range input.
The invention summarizes most of the geographic models and the characteristics of input data, then forms a set of relatively universal rule base, and reasoning is carried out on the geographic models to be constructed when the rules are actually applied.
Specifically, the invention is based on a set of predefined knowledge rules, and combines with heuristic modeling technology, so that when geographic modeling is performed in a research area with any spatial range, the spatial range of all input data in a model workflow can be automatically determined, as shown in fig. 2.
Specifically, in the process of geographic modeling, the knowledge from which the appropriate spatial range of input data of the geographic model is determined mainly considers two factors: the characteristics of the geographic model and the type of corresponding input data. The knowledge of the influence of these two factors on the spatial extent of the model input data can be systematically generalized according to model categories and data types, and a set of knowledge rules is formed.
The model inputs knowledge classification related to the data. According to the demand characteristics of the geographic model on the space range of the input data, the demand characteristics can be divided into four categories of specific space range demands, connectivity expansion demands, buffer distance expansion demands and space range of a maintenance interest area. The categories of model input data can be classified into point, line, plane and grid data according to the common types, and other special individual categories are classified into one category. The spatial extent of the data in different scenarios can be determined under a combination of the two classifications. The method comprises the following steps:
in a first model classification, a specific spatial range covering the target area is specified, including administrative boundaries, drainage basin boundaries, or custom spatial ranges. The spatial extent of the input data is consistent with this particular spatial extent, whether the input data type corresponds to a point, line, polygon or grid.
The second model defines a spatial range through generalized connectivity. Generalized connectivity includes connectivity of linear elements such as roads and rivers, as well as connectivity of data-derived information, such as flows obtained from Digital Elevation Models (DEMs). Thus, this class is only applicable where the input data type belongs to both linear and grid types, since the other two data classes are themselves consistent with the target region.
The third type of model requires expanding the buffer according to the target area. Subdivision into two subcategories: predefined distances and distances calculated from context (as in conventional buffer analysis). The context-dependent calculation distance is defined as the spatial range required to extend the buffer analysis outwards from the target domain; the predetermined distance may vary depending on whether the input data is dot data or raster data. For point data, the spatial range is defined as the minimum Thiessen polygon range consisting of points that can cover the target domain. For raster data, the neighborhood window size will be expanded based on the region of interest as the spatial extent.
The last four classes of models will preserve the spatial extent of the region of interest. This requires defining the spatial range as the range where the input data intersects the spatial range of the region of interest. When the data type in the module is determined to be beyond the defined classification, the target region scope will remain unchanged except that the model belongs to a specific spatial scope class. And finally determining all the spatial range types of A-E, as shown in figure 3.
Further, the determining a preset identifiable knowledge rule based on the characteristics of the geographic model and the category of the input data includes:
classifying and summarizing according to the data types of the input data and the characteristics of the geographic model to be calculated to obtain different classification combinations;
determining the requirements of the data space range appearing under the scenes of different classification combinations according to classification induction;
setting an extraction flow according to different space range requirements;
and formulating identifiable knowledge rules according to the classified and generalized data types, different space range requirements corresponding to the model characteristics of the geographic model to be constructed and the extraction flow thereof.
Further, the determining a data extraction flow according to the classification combination includes:
the determining a data extraction flow according to the classification combination comprises the following steps:
determining a first space range flow, performing space search in the integrated data set to determine whether the space range of the existing data set can meet the first space range, and if the space range of the existing data set is missing, judging whether the first space range can be obtained by an integration method;
determining a second space range flow, performing global connectivity search on the data without directivity, and performing connectivity search on the data with directivity according to the direction to determine a second space range;
determining a third space range flow, extracting and tracing an upstream water collecting area according to the water flow direction based on the water flow direction calculated by the DEM to obtain a complete river basin boundary, and determining a third space range by distinguishing the water collecting area based on the upstream of the river channel from the water collecting area based on the upstream of the slope in the extracting process;
determining a fourth spatial range flow, the fourth spatial range being related to the point element, constructing a Thiessen polygon using the point data, the minimum Thiessen polygon intersecting the region of interest being determined as the fourth spatial range flow;
determining a fifth space range flow, and directly expanding outwards to determine a fifth space range through a designated buffer distance;
determining a sixth spatial range procedure, wherein the sixth spatial range is used for terrain analysis on the type of raster data, and the spatial range of the raster size of one pixel which is externally expanded is determined as a sixth spatial range;
and determining a seventh spatial range flow, wherein the seventh spatial range directly maintains the spatial range of the original interest region.
Specifically, computer-recognizable knowledge rules are formulated. Knowledge rules are used in the automated construction of geographic models. To simplify the automatic specification and extraction of spatial ranges, we devised an 'if-then' rule. This 'if < condition > then < action >' rule uses the model and the input data class as "if conditions" in the decision module. Subsequently, we define a spatial scope type that customizes the appropriate extraction flow as the corresponding "then action". The method comprises the following steps:
for a class a spatial range, a spatial search is first performed in the integrated dataset to determine if there is existing data that satisfies the particular spatial range. If missing, it is further determined whether the spatial extent can be derived by an integration method (e.g., defining a drainage basin boundary).
The B-type space range performs directivity search for line data having display directivity, and performs global connectivity search for data having no directivity.
The class C spatial range considers the water flow direction calculated by the DEM. The extraction of the river basin boundary distinguishes between a water collection area based on upstream of the river channel and a water collection area based on upstream of the slope.
Class D spatial extent relates to the construction of a tessellation polygon using point data. The Thiessen polygon intersecting the region of interest is determined as a class D spatial range.
Class E directly expands outward by a specified buffer distance to determine the desired spatial extent.
Class F is for raster data types, commonly used for terrain analysis. Such as a slope calculation or the like. In this case, the distance of one pixel is expanded outwards.
Class G directly preserves the spatial extent of the original region of interest.
The 'if < condition > then < action >' rule is shown in FIG. 4.
Specifically, knowledge rules are used in the model building process to determine the spatial extent of each input data. To ensure the practical effect of the developed method, especially in complex modeling workflows involving multiple models and inputs, our method is combined with heuristic modeling, as follows:
after the user has determined the modeled region of interest and the model used, the method will gradually infer the input data it needs. A basic problem unit consisting of modeling interest area, model and data can be constructed, and the required spatial range of the input data is determined under the rule according to the predefined related model category and data type category. After the determination, the designed method can judge whether the source data meets the content and space range requirements of the input data. If the data does not meet the requirements, continuing the reasoning process until the data cannot be derived by other models or the data can meet the input conditions. The spatial extent of the current module will serve as the target area extent for the subsequent modules, thereby ensuring the integrity of each process calculation in the workflow.
A model of a workflow often has multiple branches. When the situation that the model depends on a plurality of inputs is met, the method generates different basic problem units according to different inputs and respectively determines the space range; when multiple models rely on the same input, the method waits for the determination of the spatial extent of the same data and merges before iterative reasoning.
Further, the method further comprises the following steps:
and carrying out correction judgment on the spatial range of the input data of the geographic model to be calculated, and if the required spatial range of the current data is inconsistent with the spatial range output by the upstream model, carrying out clipping correction on the spatial range of the current input data based on the spatial range of the current input data to obtain the geographic model to be constructed for configuring the accurate spatial range of the input data.
Specifically, a model workflow for execution is reconstructed based on the determined spatial extent of the input data. To ensure the integrity of the calculation result, the spatial range of the input data is often larger than the modeled region of interest, resulting in redundancy in calculation, for which purpose the method will determine whether each input needs to be corrected for spatial range before executing the workflow. The judging conditions are as follows: if the spatial range of the current data is changed in a flourishing manner compared with the input region of interest range, which indicates that the range of the calculation result exceeds the spatial range input by the downstream model, the input data is sheared according to the corresponding spatial range in the downstream judging module. Once all model input data in the workflow is traversed, computational redundancy is effectively reduced.
The beneficial effects of the invention are as follows:
(1) Automated determination of all input data spatial ranges for a model (workflow). After the user selects the model, modeling is performed in any selected interest area, and the method can automatically feed back the input data and the proper space range required by the intermediate data required by the model for the user. Thereby ensuring the integrity of the input data, the spatial extent accuracy of which is based on the determined knowledge system and knowledge rules.
(2) And (3) improving the precision of the (middle) result. Compared with the traditional model input data prepared directly based on the space range of the region of interest, the method can ensure that the model obtains complete results by performing geographic model calculation according to the space range of the input data determined by the method. The accuracy of the result is applied to the automatic construction process of the model according to the determined knowledge rule.
(3) And reducing the computational redundancy. In order to ensure the integrity of the calculation result, the spatial range of the input data is often larger than the requirement of the calculation result, and the method cuts the data under the condition of determining that the calculation result is accurate in calculation, so that redundant calculation in the calculation is avoided
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (4)
1. An intelligent determining method for a geographic model input data space range is characterized by comprising the following steps:
determining the characteristics of a geographic model to be constructed and the data type of input data;
formulating identifiable knowledge rules based on the characteristics of the geographic model and the data type of the input data;
based on a preset identifiable knowledge rule and combined with a heuristic modeling method, determining the spatial range of input data of a geographic model to be constructed;
judging whether the data source meets the content and space range requirements of the input data or not based on the determined space range, if not, iterating the reasoning process until the input data cannot be derived by other models or the data can meet the input conditions, and if so, obtaining the workflow of the geographic model to be calculated with the accurate space range input configured;
the determining a preset identifiable knowledge rule based on the characteristics of the geographic model and the category of the input data comprises the following steps:
classifying and summarizing according to the data types of the input data and the characteristics of the geographic model to be calculated to obtain different classification combinations;
determining the requirements of the data space range appearing under the scenes of different classification combinations according to classification induction;
setting an extraction flow according to different space range requirements;
formulating identifiable knowledge rules according to the classified and induced data types, different space range requirements corresponding to the model characteristics of the geographic model to be constructed and the extraction flow thereof;
setting an extraction flow according to different space range requirements, including:
determining a first space range flow, performing space search in the integrated data set to determine whether the space range of the existing data set can meet the first space range, and if the space range of the existing data set is missing, judging whether the first space range can be obtained by an integration method;
determining a second space range flow, performing global connectivity search on the data without directivity, and performing connectivity search on the data with directivity according to the direction to determine a second space range;
determining a third space range flow, extracting and tracing an upstream water collecting area according to the water flow direction based on the water flow direction calculated by the DEM to obtain a complete river basin boundary, and determining a third space range by distinguishing the water collecting area based on the upstream of the river channel from the water collecting area based on the upstream of the slope in the extracting process;
determining a fourth spatial range flow, the fourth spatial range being related to the point element, constructing a Thiessen polygon using the point data, the minimum Thiessen polygon intersecting the region of interest being determined as the fourth spatial range flow;
determining a fifth space range flow, and directly expanding outwards to determine a fifth space range through a designated buffer distance;
determining a sixth spatial range procedure, wherein the sixth spatial range is used for terrain analysis on the type of raster data, and the spatial range of the raster size of one pixel which is externally expanded is determined as a sixth spatial range;
and determining a seventh spatial range flow, wherein the seventh spatial range directly maintains the spatial range of the original interest region.
2. The method for intelligently determining the spatial extent of input data of a geographic model according to claim 1, further comprising:
and carrying out correction judgment on the spatial range of the input data of the geographic model to be calculated, and if the required spatial range of the current data is inconsistent with the spatial range output by the upstream model, carrying out clipping correction on the spatial range of the current input data based on the spatial range of the current input data to obtain the geographic model to be constructed for configuring the accurate spatial range of the input data.
3. The method for intelligently determining the spatial extent of input data of a geographic model according to claim 1, wherein the features of the geographic model comprise:
a specific spatial range requirement, a connectivity expansion requirement, a buffer distance expansion requirement, and a spatial range requirement to maintain a region of interest.
4. The method for intelligently determining the spatial extent of input data of a geographic model according to claim 1, wherein the category of the input data comprises:
dot data, line data, face data, and raster data.
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