Large-size image geographic mapping method based on multi-factor dynamic layering mechanism

Abstract

The invention discloses a large-size image geographic mapping method based on a multi-factor dynamic layering mechanism. The method comprises the following steps: acquiring various mapping elements required by large-size image geographic mapping; carrying out element weight distribution on the obtained various charting elements according to the incidence relation among the charting elements; constructing a mapping element layering mechanism, and performing dynamic layering according to the obtained mapping elements and the weights thereof; based on a dynamic layering result, carrying out spatial domain topology analysis on the drawing elements belonging to each level according to established standards and specifications, and generating a standard drawing layer reflecting weighted contribution; automatically distributing the obtained standard charting layer to the most suitable charting personnel in combination with the behavior data of the charting personnel and a distributed news recommendation algorithm; and after a map making person completes geographic map making, all the standard map layers are integrated according to a map making comprehensive principle, and a large-size image map meeting the standard is formed. According to the method, the drawing quality can be ensured, the drawing threshold is reduced, and the large-size image geographic drawing efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of geographic mapping, and in particular relates to a large-scale image geographic mapping method based on a multi-factor dynamic stratification mechanism. Background Art

[0002] Geographical mapping is a process of analyzing and mapping geographical phenomena using cartographic methods. Its task is to simply, quickly and intuitively display the geographical environment and the relationship between various elements. Traditional geographic mapping work involves a large number of people such as mapping experts, database experts, and industry experts. Professional desktop mapping software is used to complete the basic layers. Symbol configuration, layout and icon decoration are usually completed with the help of mature graphic design software. The mapping process is time-consuming, the workload of low-level repetitive work is huge, and the data quality is difficult to guarantee, making it difficult to meet the personalized and intelligent needs of various industries for large-scale image geographic mapping.

[0003] The significant growth of fields such as big data, cloud computing and artificial intelligence, as well as the industry's new demands for large-scale image geographic mapping expression forms, mapping content, response time and mapping precision, are driving geographic mapping towards intelligent, interactive, dynamic and diversified development. At present, experts and scholars at home and abroad have also carried out many studies on geographic mapping. For example, in 2024, Li Jia'e proposed "a new type of thematic map automatic mapping method", designed and implemented a thematic map mapping framework driven by spatial database and application model, but the framework lacks adaptability and cannot fully express the high-fidelity requirements of map display in a complete and publication-level map under multi-factor scenarios. ZTE's "a collaborative graphics editing method and system based on mobile terminals" is prone to network conflicts due to the large amount of information and frequent interactions when facing large-scale image geographic mapping and frequent editing scenes, so it cannot effectively improve mapping efficiency.

[0004] Faced with the huge and fragmented mapping elements of large-scale image geographic mapping, it is urgent to develop a solution through technological innovation to effectively improve the production efficiency of large-scale image geographic mapping, while taking into account the quality requirements of high fidelity, high precision and high integrity of mapping results. Summary of the invention

[0005] The purpose of the present invention is to provide a large-scale image geographic mapping method based on a multi-factor dynamic stratification mechanism. Starting from the mapping quality and mapping efficiency, a multi-factor dynamic stratification mechanism of mapping elements and a multi-user distributed geographic mapping method are constructed to form a professional, automated and efficient mapping mechanism suitable for large-scale image geographic mapping. It can not only solve the problems of "errors, omissions, deficiencies, poor quality and slowness" in the mapping process, but also ensure the mapping quality and lower the mapping threshold, while achieving the purpose of improving the efficiency of large-scale image geographic mapping, normalizing mapping, standardizing mapping and optimizing mapping vision.

[0006] The technical solution adopted by the present invention is a large-scale image geographic mapping method based on a multi-factor dynamic layering mechanism, comprising the following steps:

[0007] S1, using the existing database to automatically obtain or manually input various cartographic elements required for large-scale image geographic mapping, and determine the number of cartographic elements;

[0008] S2, based on the various cartographic elements obtained in S1, weights of various elements are allocated according to the associations between the cartographic elements. The specific steps include:

[0009] A1, compare the obtained cartographic elements one by one, and determine the relative importance of each cartographic element based on the correlation between them and expert experience and knowledge;

[0010] A2, based on the relative importance of each mapping element, a positive and negative matrix is ​​constructed to assign weights to each mapping element;

[0011] S3, constructing a mapping element stratification mechanism, and dynamically stratifying the mapping elements and their weights obtained in S2;

[0012] S4, based on the dynamic stratification results of S3, the mapping elements belonging to each level are subjected to spatial domain topological analysis according to established standards and specifications to generate standard mapping layers reflecting weighted contributions;

[0013] The spatial domain topological analysis is implemented by any of the following operations: tangency, tangency remainder, domain interconnection, and merging.

[0014] S5, combining the standard mapping layer obtained in S4 with the mapmaker behavior data and the distributed news recommendation algorithm, and automatically assigning it to the most suitable mapmaker. The specific steps include:

[0015] B1, obtain the behavior data of cartographers, including basic information of cartographers, data on cartographers’ working scenarios, data on cartographers’ preferences, and data on cartographers’ skills and specialties;

[0016] B2, after cluster analysis of the map maker behavior data, we obtain user feature record data. Then, we combine the map making project situation, hardware usage and the existing historical record data in the database, use the distributed news recommendation algorithm to assign the best map making layers to different map makers, and record the map making results in real time, where:

[0017] User feature record data means grouping, categorizing and statistically analyzing all acquired behavior data of cartographers to obtain basic attribute data and cartographer capability data;

[0018] The mapping project situation includes the scope of the mapping project, the mapping standards and specifications to be adopted, the source and quality of the mapping data, the mapping schedule, and the allocation of mapping personnel and hardware equipment;

[0019] Hardware usage includes the CPU speed, CPU running time, total memory capacity, memory usage, disk capacity, and disk activity time of the hardware devices used by cartographers when making maps;

[0020] The existing historical record data in the database includes the basic attribute data and mapping capability data of the mapmakers stored in the database;

[0021] S6, after completing geographic mapping, cartographers integrate various standard layers according to the principles of cartographic synthesis, and finally form a large-size image map that meets the standards;

[0022] The principles of cartographic generalization include checking and correcting layer visibility, checking and correcting map attribute topological relationships, checking and correcting map attribute elements, and checking and correcting map aesthetics.

[0023] Furthermore, in step A2:

[0024] The formula for constructing the positive and negative matrices is:

[0025]

[0026] Where M is the positive and negative matrix; A is the normalization process, that is, each column element of the positive and negative matrix is ​​divided by the sum of the elements in the column; a is the number of mapping elements, and a×a represents an a-row×a-column matrix constructed based on the number of mapping elements; n ij Indicates a mapping element i with a j The measurement value for importance comparison is in the range of [0, 10];

[0027] The calculation formula for the weight of each mapping element is:

[0028]

[0029] Among them, X i (i=1, 2, 3, ..., a) represents the weight of the i-th mapping element; M i Represents each element value of the corresponding row of the i-th mapping element in the positive and negative matrix M.

[0030] Furthermore, in S3, the formula for constructing the mapping element hierarchical mechanism is:

[0031]

[0032] Among them, Layer irepresents the maximum weight of the mapping elements contained in the i-th layer; f(X1, X2, X3, ... X a ) represents the minimum value among all the cartographic element weights; g(X1, X2, X3, ... X a ) represents the maximum value of all cartographic element weights; Layer represents the number of layers; a is the number of cartographic elements.

[0033] Furthermore, in S4, the mapping elements belonging to each level are subjected to spatial domain topological analysis according to established standards and specifications. The spatial domain topological analysis is implemented by any operation of tangency, tangency remainder, domain interconnection, and merging, wherein:

[0034] Tangent: The overlapping parts of the input cartographic features and the reference cartographic features are written to the output cartographic feature class;

[0035] Tangent remainder: The non-overlapping parts of the input cartographic features and the reference cartographic features are written to the output cartographic feature class;

[0036] Domain interconnection: The input cartographic features and the reference cartographic features are connected according to their relative positional relationship, and the results are written into the output cartographic feature class;

[0037] Merge: The input cartographic features and the reference cartographic features are directly merged, the cartographic feature attributes are aggregated, and all cartographic features and their attributes are written to the output cartographic feature class.

[0038] Furthermore, in S6, the cartographic generalization principle includes checking and correcting layer visibility, checking and correcting map attribute topological relationships, checking and correcting map attribute elements, and checking and correcting map aesthetics, wherein:

[0039] Layer visibility check and correction: check and correct the layer transparency, visible range, and layer lock information;

[0040] Check and correct the topological relationship of map attributes: check and correct whether the map elements are closed, whether there are hanging arcs, and whether there are false nodes;

[0041] Check and correct map attribute elements: Check and correct the attribute fields and attribute matching of map elements;

[0042] Check and correct the map's aesthetics: Check and correct the map's comprehensive color representation, overall typesetting and layout.

[0043] The beneficial effects of the present invention are:

[0044] (1) Improving the accuracy and practicality of mapping: The present invention comprehensively considers the multiple and diverse mapping elements required for large-scale image geographic mapping, combines the purpose of map use, user needs, etc., dynamically adjusts the mapping level and its display content, and designs a more integrated, more accurate, and more practical map mapping method, effectively improving the accuracy and practicality of mapping;

[0045] (2) Improving the timeliness and quality of mapping: Based on the behavioral data of cartographers and a distributed news recommendation algorithm, the present invention can achieve optimal configuration between various mapping elements and cartographers when mapping large-scale images, thereby effectively improving the timeliness and quality of mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The figure is an overall flow chart of the method of the present invention.

[0047] Figure 2 It is a schematic diagram of the effect of spatial domain topological analysis of the method of the present invention.

[0048] Figure 3 The figure is a flow chart of the optimal allocation method of standard mapping layers in the method of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer and more understandable, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings.

[0050] A large-scale image geographic mapping method based on a multi-factor dynamic layering mechanism, such as Figure 1 As shown, the following steps are included:

[0051] S1, use the existing database to automatically obtain or manually input various types of mapping elements required for large-scale image geographic mapping, and determine the number of mapping elements.

[0052] The mapping elements stored in the database of this embodiment include but are not limited to the following, as shown in Table 1:

[0053] Table 1: Database storage cartographic feature table

[0054]

[0055] The following content of this embodiment takes 12 types of mapping elements (i.e., the number of mapping elements confirmed by the user) required for geographic mapping, such as compass, scale, map projection, mapping area, map name, publishing unit, publishing time, water system, farmland, administrative division boundary, explanatory notes and eagle eye map as examples to introduce in detail the technical solution of the present invention.

[0056] S2, based on the various cartographic elements obtained in S1, weights of various elements are allocated according to the associations between the cartographic elements. The specific steps include:

[0057] A1, compare the obtained cartographic elements one by one, and determine the relative importance of each cartographic element based on the correlation between them and expert experience and knowledge;

[0058] A2, based on the relative importance of each cartographic element, a positive and negative matrix is ​​constructed to assign weights to each cartographic element.

[0059] The formula for constructing the positive and negative matrices is:

[0060]

[0061] Where M is the positive and negative matrix; A is the normalization process, that is, each column element of the positive and negative matrix is ​​divided by the sum of the elements in the column; a is the number of mapping elements, and a×a represents an a-row×a-column matrix constructed based on the number of mapping elements; n ij Indicates a mapping element i with a j The measurement value for importance comparison is in the range of [0, 10].

[0062] The calculation formula for the weight of each mapping element is:

[0063]

[0064] Among them, X i (i=1, 2, 3, ..., a) represents the weight of the i-th mapping element; M i Represents each element value of the corresponding row of the i-th mapping element in the positive and negative matrix M.

[0065] Based on the above steps, this embodiment assigns weights to 12 types of mapping elements, and the results are shown in Table 2:

[0066] Table 2: Weight distribution table of 12 types of mapping elements

[0067] Serial number Cartographic elements Weight distribution 1 compass 0.028 2 scale 0.058 3 Map projections 0.136 4 Mapping area 0.092 5 Map Name 0.132 6 Publishing Unit 0.064 7 Publication date 0.044 8 Water system 0.132 9 farmland 0.172 10 Administrative boundaries 0.082 11 Notes 0.039 12 Hawkeye view 0.021

[0068] S3, constructs a mapping element stratification mechanism and performs dynamic stratification based on the mapping elements and their weights obtained in S2.

[0069] The formula for constructing the mapping element layering mechanism is:

[0070]

[0071] Among them, Layer irepresents the maximum weight of the mapping elements contained in the i-th layer (i = 1, 2, ... Layer); f(X1, X2, X3, ... X a ) represents the minimum value among all the cartographic element weights; g(X1, X2, X3, ... X a ) represents the maximum value of all cartographic element weights; Layer represents the number of layers; a is the number of cartographic elements.

[0072] Based on the above steps, this embodiment dynamically layers 12 types of mapping elements. For example, when the mapping layer is 3, the dynamic layering results are shown in Table 3:

[0073] Table 3: Dynamic layered table with 12 types of cartographic features and 3 cartographic layers

[0074] Cartographic hierarchy Cartographic elements 1 Compass, scale, publishing unit, publishing time, instructions, eagle eye map 2 Mapping area, administrative boundaries 3 Map projection, map name, water system, farmland

[0075] A mapping element stratification mechanism is constructed, and dynamic stratification processing is implemented based on the mapping elements and their corresponding weights obtained by S2 to ensure that in different application scenarios, for large-scale image geographic mapping, the mapping elements can be flexibly adjusted and optimized after in-depth analysis and weight evaluation, ensuring that the mapping results in complex environments are both scientific and practical, thereby improving the accuracy and applicability of mapping.

[0076] S4, based on the dynamic stratification results of S3, conducts spatial domain topological analysis on the cartographic elements belonging to each level according to established standards and specifications, and generates standard cartographic layers reflecting weighted contributions.

[0077] The spatial domain topology analysis is used to ensure that all mapping elements are analyzed in accordance with the established standards and specifications. The spatial domain topology analysis is implemented by any of the following operations: tangency, tangency remainder, domain interconnection, and merging.

[0078] Tangent: The overlapping parts of the input cartographic features and the reference cartographic features are written to the output cartographic feature class;

[0079] Tangent remainder: The non-overlapping parts of the input cartographic features and the reference cartographic features are written to the output cartographic feature class;

[0080] Domain interconnection: The input cartographic features and the reference cartographic features are connected according to their relative position relationship (including position and attribute connection), and the results are written into the output cartographic feature class;

[0081] Merge: The input cartographic features and the reference cartographic features are directly merged, the cartographic feature attributes are aggregated, and all cartographic features and their attributes are written to the output cartographic feature class.

[0082] The schematic diagram of the implementation effect of spatial domain topology analysis is as follows: Figure 2 shown.

[0083] S5, combines the standard mapping layer obtained in S4 with the mapper behavior data and the distributed news recommendation algorithm, and automatically assigns it to the most suitable mapper, e.g. Figure 3 As shown, the specific steps include:

[0084] B1, obtain the behavior data of cartographers, including basic information of cartographers (name, gender, age, education level, major, etc.), work scenario data of cartographers (online time of cartographers, work efficiency of cartographers, work quality of cartographers, work frequency of cartographers, etc.), cartographers’ preference data, cartographers’ skills and specialties data, etc.;

[0085] B2, after cluster analysis of the map maker behavior data, we obtain user feature record data. Then, we combine the map making project situation, hardware usage, and existing historical record data in the database, and use the distributed news recommendation algorithm to assign the best map making layers to different map makers, and record the map making results in real time, where:

[0086] User feature record data means grouping, categorizing and statistically analyzing all acquired behavior data of cartographers to obtain basic attribute data and cartographer capability data;

[0087] The mapping project situation includes the scope of the mapping project, the mapping standards and specifications to be adopted, the source and quality of mapping data, the mapping schedule, the allocation of mapping personnel and hardware equipment, etc.;

[0088] Hardware usage includes the CPU speed, CPU running time, total memory capacity, memory usage, disk capacity, disk activity time, etc. of the hardware devices used by cartographers when making maps;

[0089] The existing historical record data in the database includes the basic attribute data and mapping capability data of the mapmakers stored in the database.

[0090] Based on the above steps, when this embodiment allocates three standard mapping layers to two mappers, the allocation results are shown in Table 4:

[0091] Table 4: Result table of 3 standard mapping layers assigned to 2 mappers

[0092] Cartographic hierarchy Cartographer 1、3 Account: test1@163.com 2 Account: test2@gmail.com

[0093] S6. After completing geographic mapping, cartographers integrate various standard layers according to the principles of cartographic synthesis, and finally form a large-size image map that meets the standards.

[0094] The principles of cartographic generalization include checking and correcting layer visibility, checking and correcting map attribute topological relationships, checking and correcting map attribute elements, and checking and correcting map aesthetics, among which:

[0095] Layer visibility check and correction: check and correct information such as layer transparency, visible range, layer lock, etc.;

[0096] Check and correct the topological relationship of map attributes: check and correct whether the map elements are closed, whether there are hanging arcs, whether there are false nodes, etc.;

[0097] Check and correct map attribute elements: Check and correct the attribute fields and attribute matching of map elements;

[0098] Check and correct the map's aesthetics: Check and correct the map's comprehensive color representation, overall typesetting and layout, etc.

[0099] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A large-scale image geographic mapping method based on a multi-factor dynamic layering mechanism, characterized in that: The following steps are involved: S1, using the existing database to automatically obtain or manually input various cartographic elements required for large-scale image geographic mapping, and determine the number of cartographic elements; S2, based on the various cartographic elements obtained in S1, weights of various elements are allocated according to the associations between the cartographic elements. The specific steps include: A1, compare the obtained cartographic elements one by one, and determine the relative importance of each cartographic element based on the correlation between them and expert experience and knowledge; A2, based on the relative importance of each mapping element, a positive and negative matrix is ​​constructed to assign weights to each mapping element; S3, constructing a mapping element stratification mechanism, and dynamically stratifying the mapping elements and their weights obtained in S2; S4, based on the dynamic stratification results of S3, the mapping elements belonging to each level are subjected to spatial domain topological analysis according to established standards and specifications to generate standard mapping layers reflecting weighted contributions; The spatial domain topological analysis is implemented by any of the following operations: tangency, tangency remainder, domain interconnection, and merging. S5, combining the standard mapping layer obtained in S4 with the mapmaker behavior data and the distributed news recommendation algorithm, and automatically assigning it to the most suitable mapmaker. The specific steps include: B1, obtain the behavior data of cartographers, including basic information of cartographers, data on cartographers’ working scenarios, data on cartographers’ preferences, and data on cartographers’ skills and specialties; B2, after cluster analysis of the map maker behavior data, we obtain user feature record data. Then, we combine the map making project situation, hardware usage and the existing historical record data in the database, use the distributed news recommendation algorithm to assign the best map making layers to different map makers, and record the map making results in real time, where: User feature record data means grouping, categorizing and statistically analyzing all acquired behavior data of cartographers to obtain basic attribute data and cartographer capability data; The mapping project situation includes the scope of the mapping project, the mapping standards and specifications to be adopted, the source and quality of the mapping data, the mapping schedule, and the allocation of mapping personnel and hardware equipment; Hardware usage includes the CPU speed, CPU running time, total memory capacity, memory usage, disk capacity, and disk activity time of the hardware devices used by cartographers when making maps; The existing historical record data in the database includes the basic attribute data and mapping capability data of the mapmakers stored in the database; S6, after completing geographic mapping, cartographers integrate various standard layers according to the principles of cartographic synthesis, and finally form a large-size image map that meets the standards; The principles of cartographic generalization include checking and correcting layer visibility, checking and correcting map attribute topological relationships, checking and correcting map attribute elements, and checking and correcting map aesthetics.

2. The large-scale image geographic mapping method based on a multi-factor dynamic layering mechanism according to claim 1 is characterized in that: In step A2: The formula for constructing the positive and negative matrices is: Where M is the positive and negative matrix; A is the normalization process, that is, each column element of the positive and negative matrix is ​​divided by the sum of the elements in the column; a is the number of mapping elements, and a×a represents an a-row×a-column matrix constructed based on the number of mapping elements; n ij Indicates a mapping element i with a j The measurement value for importance comparison is in the range of [0, 10]; The calculation formula for the weight of each mapping element is: Among them, X i (i=1, 2, 3, ..., a) represents the weight of the i-th mapping element; M i Represents each element value of the corresponding row of the i-th mapping element in the positive and negative matrix M.

3. The large-scale image geographic mapping method based on a multi-factor dynamic layering mechanism according to claim 1 is characterized in that: In S3, the formula for constructing the mapping element hierarchical mechanism is: Among them, Layer i represents the maximum weight of the mapping elements contained in the i-th layer; f(X1, X2, X3, ... X a ) represents the minimum value among all the cartographic element weights; g(X1, X2, X3, ... X a ) represents the maximum value of all cartographic element weights; Layer represents the number of layers; a is the number of cartographic elements.

4. The large-scale image geographic mapping method based on a multi-factor dynamic layering mechanism according to claim 1 is characterized in that: In S4, the mapping elements belonging to each level are subjected to spatial domain topological analysis according to established standards and specifications. The spatial domain topological analysis is implemented by any operation of tangency, tangency remainder, domain interconnection, and merging, wherein: Tangent: The overlapping parts of the input cartographic features and the reference cartographic features are written to the output cartographic feature class; Tangent remainder: The non-overlapping parts of the input cartographic features and the reference cartographic features are written to the output cartographic feature class; Domain interconnection: The input cartographic features and the reference cartographic features are connected according to their relative positional relationship, and the results are written into the output cartographic feature class; Merge: The input cartographic features and the reference cartographic features are directly merged, the cartographic feature attributes are aggregated, and all cartographic features and their attributes are written to the output cartographic feature class.

5. The large-scale image geographic mapping method based on a multi-factor dynamic layering mechanism according to claim 1 is characterized in that: In S6, the cartographic generalization principle includes layer visibility check and correction, map attribute topological relationship check and correction, map attribute element check and correction, and map aesthetics check and correction, wherein: Layer visibility check and correction: check and correct the layer transparency, visible range, and layer lock information; Check and correct the topological relationship of map attributes: check and correct whether the map elements are closed, whether there are hanging arcs, and whether there are false nodes; Check and correct map attribute elements: check and correct the attribute fields and attribute matching of map elements; check and correct map aesthetics: check and correct the comprehensive color representation, overall typesetting and layout of the map.

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