阅读说明：本技术 一种基于梯度模糊集距离测度的网格化生成方法及装置 (Grid generation method and device based on distance measure of gradient fuzzy set ) 是由 吴丽贤 钱正浩 林钰杰 宋才华 关兆雄 庞伟林 杨峰 于 2021-09-08 设计创作，主要内容包括：本发明公开了一种基于梯度模糊集距离测度的网格化生成方法及装置,方法包括：获取目标区域的所有特征及其特征数据；对所有所述特征数据进行梯度模糊化处理,得到对应的模糊特征数据；所述模糊特征数据包括：将所述模糊特征数据输入预先构建的特征权重模型中,得到所述特征数据对应的特征权重；基于所述特征数据,通过梯度模糊距离测度方法,依次计算所述特征间的特征距离；基于所述特征权重和所有所述特征距离,生成目标网格化信息。解决现有的网格化生成方法存在的由于工作量大且步骤复杂,生成的过程容易出现主观性强以及效率低的问题。(The invention discloses a gridding generation method and a gridding generation device based on distance measurement of a gradient fuzzy set, wherein the method comprises the following steps: acquiring all characteristics and characteristic data of a target area; performing gradient fuzzification processing on all the characteristic data to obtain corresponding fuzzy characteristic data; the fuzzy feature data comprises: inputting the fuzzy feature data into a pre-constructed feature weight model to obtain feature weights corresponding to the feature data; based on the characteristic data, sequentially calculating characteristic distances among the characteristics by a gradient fuzzy distance measurement method; and generating target gridding information based on the characteristic weight and all the characteristic distances. The method solves the problems that the existing gridding generation method is high in workload and complex in steps, the generation process is easy to have strong subjectivity and low in efficiency.)

1. A grid generation method based on distance measure of gradient fuzzy sets is characterized by comprising the following steps:

acquiring all characteristics and characteristic data of a target area;

performing gradient fuzzification processing on all the characteristic data to obtain corresponding fuzzy characteristic data;

inputting the fuzzy feature data into a pre-constructed feature weight model to obtain feature weights corresponding to the feature data;

based on the characteristic data, sequentially calculating characteristic distances among the characteristics by a gradient fuzzy distance measurement method;

and generating target gridding information based on the characteristic weight and all the characteristic distances.

2. The method for generating gridding based on distance measure of gradient fuzzy sets according to claim 1, wherein after inputting the fuzzy feature data into a pre-constructed feature weight model and obtaining the feature weight corresponding to the feature data, the method further comprises:

determining a feature central point from feature points corresponding to the features based on the numerical relationship of the feature weights;

the step of sequentially calculating the characteristic distances among the characteristics by a gradient fuzzy distance measurement method based on the characteristic data specifically comprises the following steps:

and sequentially calculating the distances between the characteristic points and the characteristic central points by the gradient fuzzy distance measurement method to obtain the characteristic distances.

3. The method of claim 2, wherein generating target gridding information based on the feature weight and all the feature distances comprises:

according to the magnitude relation of the feature weight, sorting the feature points except the feature central point around the feature central point;

and adjusting the distance between the corresponding feature point and the feature central point according to the feature distance, and sequentially connecting features except the feature central point to obtain the target gridding information.

4. The grid generation method based on the distance measure of the gradient fuzzy set according to any one of claims 1 to 3, wherein the feature weight model is specifically:

wherein M is_JM is the total number of the features of the non-target area, n is the total number of the features of the target area, i is the ith feature, i is less than or equal to m, j is the jth feature, j is less than or equal to n, w_jIs a feature weight, and w_j∈[0,1]， Is r_ijAndthe square of the distance between, r_ijIs a measure of the blurring distance between the ith and jth features, and r_ij∈(0,1)，Is a function ofCalculated value of l in the formula_jTo calculateThe number of features contained within the middle region.

5. A gridding generation device based on distance measure of gradient fuzzy sets is characterized by comprising:

the acquisition module is used for acquiring all characteristics and characteristic data of the target area;

the fuzzification processing module is used for carrying out gradient fuzzification processing on all the characteristic data to obtain corresponding fuzzy characteristic data;

the input module is used for inputting the fuzzy feature data into a pre-constructed feature weight model to obtain the feature weight corresponding to the feature data;

the characteristic distance calculation module is used for sequentially calculating the characteristic distances among the characteristics through a gradient fuzzy distance measurement method based on the characteristic data;

and the target gridding information generating module is used for generating target gridding information based on the characteristic weight and all the characteristic distances.

6. The apparatus according to claim 5, further comprising:

the characteristic center point characteristic module is used for determining a characteristic center point from the characteristic points corresponding to the characteristics based on the numerical relationship of the characteristic weight;

the feature distance calculation module is specifically configured to calculate distances between the feature points and the feature center points in sequence by the gradient fuzzy distance measurement method, so as to obtain the feature distances.

7. The apparatus according to claim 6, wherein the target gridding information generating module comprises:

the characteristic arrangement submodule is used for sequencing the characteristic points except the characteristic central point around the characteristic central point according to the size relation of the characteristic weight;

and the adjusting submodule is used for adjusting the distance between the corresponding characteristic point and the characteristic central point according to the characteristic distance, and sequentially connecting the characteristics except the characteristic central point to obtain the target gridding information.

8. The apparatus for generating gridding based on distance measure of gradient blur set according to any of claims 5-7, wherein the feature weight model is specifically:

wherein M is_JM is the total number of the features of the non-target area, n is the total number of the features of the target area, i is the ith feature, i is less than or equal to m, j is the jth feature, j is less than or equal to n, w_jIs a feature weight, and w_j∈[0,1]， Is r_ijAndthe square of the distance between, r_ijIs a measure of the blurring distance between the ith and jth features, and r_ij∈(0,1)，Is a function ofCalculated value of l in the formula_jTo calculateThe number of features contained within the middle region.

9. An electronic device comprising a processor and a memory, the memory storing analysis machine readable instructions that, when executed by the processor, perform the method of any of claims 1-4.

10. A storage medium having stored thereon a parser program, characterized in that the parser program, when executed by a processor, performs the method according to any of claims 1-4.

Technical Field

The invention relates to the technical field of gridding generation, in particular to a gridding generation method and a gridding generation device based on gradient fuzzy set distance measurement.

Background

The Grid (Grid) is defined by dividing a certain area into a plurality of Grid-shaped units according to a certain rule, so that the area can be managed more finely. Gridding generation may be understood as decomposing a complex area into several metagrids on demand and connecting the metagrids on demand. The simple element grids are orderly and closely connected with each other to form a grid with a reasonable structure.

At present, most of gridding generation is performed by rough drawing of grids on a relevant plane map in a manual mode by using a mechanical working mode by workers based on experience accumulated by self work and proficiency level of business. The generation method has the problems of strong subjectivity and low efficiency in the generation process due to large workload and complex steps.

Disclosure of Invention

The invention provides a grid generation method and device based on gradient fuzzy set distance measurement, which are used for solving the problems that the traditional grid generation method is high in workload and complex in steps, the generation process is easy to have strong subjectivity and low in efficiency.

In a first aspect, a grid generation method based on a distance measure of a gradient fuzzy set provided in an embodiment of the present invention includes:

acquiring all characteristics and characteristic data of a target area;

performing gradient fuzzification processing on all the characteristic data to obtain corresponding fuzzy characteristic data;

inputting the fuzzy feature data into a pre-constructed feature weight model to obtain feature weights corresponding to the feature data;

based on the characteristic data, sequentially calculating characteristic distances among the characteristics by a gradient fuzzy distance measurement method;

and generating target gridding information based on the characteristic weight and all the characteristic distances.

Optionally, after inputting the fuzzy feature data into a pre-constructed feature weight model and obtaining a feature weight corresponding to the feature data, the method further includes:

determining a feature central point from feature points corresponding to the features based on the numerical relationship of the feature weights;

the step of sequentially calculating the characteristic distances among the characteristics by a gradient fuzzy distance measurement method based on the characteristic data specifically comprises the following steps:

and sequentially calculating the distances between the characteristic points and the characteristic central points by the gradient fuzzy distance measurement method to obtain the characteristic distances.

Optionally, generating target gridding information based on the feature weight and all the feature distances includes:

according to the magnitude relation of the feature weight, sorting the feature points except the feature central point around the feature central point;

and adjusting the distance between the corresponding feature point and the feature central point according to the feature distance, and sequentially connecting features except the feature central point to obtain the target gridding information.

Optionally, the feature weight model is specifically:

wherein M is_JIs a feature weight model, m isThe total number of features of the non-target area, n is the total number of features of the target area, i is the ith feature, i is less than or equal to m, j is the jth feature, j is less than or equal to n, w_jIs a feature weight, and w_j∈[0,1]， Is r_ijAndthe square of the distance between, r_ijIs a measure of the blurring distance between the ith and jth features, and r_ij∈(0,1)，Is a function ofCalculated value of l in the formula_jTo calculateThe number of features contained within the middle region.

In a second aspect, an embodiment of the present invention provides a grid generation apparatus based on a distance measure of a gradient fuzzy set, including:

the acquisition module is used for acquiring all characteristics and characteristic data of the target area;

the fuzzification processing module is used for carrying out gradient fuzzification processing on all the characteristic data to obtain corresponding fuzzy characteristic data;

the input module is used for inputting the fuzzy feature data into a pre-constructed feature weight model to obtain the feature weight corresponding to the feature data;

the characteristic distance calculation module is used for sequentially calculating the characteristic distances among the characteristics through a gradient fuzzy distance measurement method based on the characteristic data;

and the target gridding information generating module is used for generating target gridding information based on the characteristic weight and all the characteristic distances.

Optionally, the method further comprises:

the characteristic center point characteristic module is used for determining a characteristic center point from the characteristic points corresponding to the characteristics based on the numerical relationship of the characteristic weight;

the feature distance calculation module is specifically configured to calculate distances between the feature points and the feature center points in sequence by the gradient fuzzy distance measurement method, so as to obtain the feature distances.

Optionally, the target gridding information generating module includes:

the characteristic arrangement submodule is used for sequencing the characteristic points except the characteristic central point around the characteristic central point according to the size relation of the characteristic weight;

and the adjusting submodule is used for adjusting the distance between the corresponding characteristic point and the characteristic central point according to the characteristic distance, and sequentially connecting the characteristics except the characteristic central point to obtain the target gridding information.

Optionally, the feature weight model is specifically:

wherein M is_JM is the total number of the features of the non-target area, n is the total number of the features of the target area, i is the ith feature, i is less than or equal to m, j is the jth feature, j is less than or equal to n, w_jIs a feature weight, and w_j∈[0,1]， Is r_ijAndthe square of the distance between, r_ijIs a measure of the blurring distance between the ith and jth features, and r_ij∈(0,1)，Is a function ofCalculated value of l in the formula_jTo calculateThe number of features contained within the middle region.

In a third aspect, the present invention provides an electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, which when executed by the processor, implement the steps of the method according to the first aspect.

In a fourth aspect, the present invention provides a readable storage medium on which is stored a program or instructions which, when executed by a processor, performs the steps of the method according to the first aspect.

According to the technical scheme, the invention has the following advantages:

the method comprises the steps of acquiring all characteristics and characteristic data of a target area; performing gradient fuzzification processing on all the characteristic data to obtain corresponding fuzzy characteristic data; the fuzzy feature data comprises: inputting the fuzzy feature data into a pre-constructed feature weight model to obtain feature weights corresponding to the feature data; based on the characteristic data, sequentially calculating characteristic distances among the characteristics by a gradient fuzzy distance measurement method; and generating target gridding information based on the characteristic weight and all the characteristic distances. The method solves the problems that the existing gridding generation method is high in workload and complex in steps, the generation process is easy to have strong subjectivity and low in efficiency.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts;

FIG. 1 is a flowchart illustrating steps of an embodiment of a grid generation method based on distance measurement of a gradient fuzzy set according to the present invention;

FIG. 2 is a schematic diagram of generating target gridding information according to the present invention;

fig. 3 is a structural block diagram of an embodiment of a grid generation device based on distance measurement of a gradient fuzzy set according to the present invention.

Detailed Description

The embodiment of the invention provides a grid generation method and device based on distance measurement of a gradient fuzzy set, which are used for solving the problems that the traditional grid generation method is high in workload and complex in steps, the generation process is easy to have strong subjectivity and low in efficiency.

In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

At present, gridding generation is mostly carried out by workers based on experience accumulated by the workers and proficiency of business, and the grids are roughly drawn by using a mechanical working mode on a related plane map in a manual mode, and due to possible errors in the drawing process and differences in the mode that different workers see problems or deal with the problems, even if the grids in the same area are drawn, the overall layout difference is large, namely the problems of strong subjectivity and low efficiency easily occur in the process of generating grid information, and the grids are very complicated if the grids need to be modified or updated in the later period.

To solve the above problems, an embodiment of the present invention provides a grid generation method based on distance measure of a gradient blur set, please refer to fig. 1, and fig. 1 is a flowchart of steps of an embodiment of the grid generation method based on distance measure of the gradient blur set according to the present invention, including:

s1, acquiring all characteristics and characteristic data of the target area;

it should be noted that, the definition of the Grid (Grid) is to divide a certain area into a plurality of Grid-shaped units according to a certain rule, so as to facilitate more fine management of the area.

The gridding generation method is determined based on the distance relation among all feature data of the region, and the distance among different features is the basis of gridding generation. It is therefore desirable to obtain feature data over the relevant area to generate a determination of the spatial relationship between the grids for ease of management and control. In the subsequent gridding management, gridding generation (adding or deleting the characteristics of the region) needs to be carried out again, the operation is very convenient, and the dependence degree on the experience of workers is greatly reduced.

In addition, gridding can be understood as decomposing a complex area into several metagrids on demand and connecting the metagrids on demand. The simple element grids are orderly and closely connected with each other to form a grid with a reasonable structure.

S2, performing gradient fuzzification processing on all the characteristic data to obtain corresponding fuzzy characteristic data;

in the embodiment of the present invention, gradient blurring processing is performed on all feature data, that is, equivalent blurring processing is performed on features (human, ground, object, organization, etc.) in the target region, so that the features in the target region are regarded as one point in space, for example, the features are regarded as D_i＝(x_i,y_i...z_i) Is expressed in terms of the form. After the features in the target area are subjected to gradient blurring processing, as long as the distance between different features is determined, the size and the shape of the target gridding information are basically determined. By carrying out fuzzification processing on the features in the distance measurement environment, the complexity in grid generation is greatly reduced, and the workload of grid generation is reduced.

S3, inputting the fuzzy feature data into a pre-constructed feature weight model to obtain the feature weight corresponding to the feature data; the characteristic weight model specifically comprises:

wherein M is_JM is the total number of the features of the non-target area, n is the total number of the features of the target area, i is the ith feature, i is less than or equal to m, j is the jth feature, j is less than or equal to n, w_jIs a feature weight, and w_j∈[0,1]， Is r_ijAndthe square of the distance between, r_ijIs as follows

J (w, λ), where λ is a real number and J (w, λ) is:

the partial derivatives are calculated for J (w, λ) to obtain:

further obtaining:

will w_jSubstituting into the partial derivative function of J (w, lambda) yields:

substituting the algebraic expression of lambda into the partial derivative function of J (w, lambda) to obtain w_jThe algebraic expression of (A):

thus, passing through w_jThe characteristic weight corresponding to all the characteristic data in the weight model can be determined by the algebraic expression.

In an optional embodiment, after inputting the fuzzy feature data into a pre-constructed feature weight model and obtaining a feature weight corresponding to the feature data, the method further includes:

and determining a feature central point from the feature points corresponding to the features based on the numerical relationship of the feature weights.

In the embodiment of the invention, the feature point corresponding to the feature with the largest feature weight value is selected as the feature central point.

S4, sequentially calculating the characteristic distances among the characteristics by a gradient fuzzy distance measurement method based on the characteristic data;

in an optional embodiment, the step of sequentially calculating the feature distances between the features by a gradient fuzzy distance measurement method based on the feature data specifically includes:

and sequentially calculating the distances between the characteristic points and the characteristic central points by the gradient fuzzy distance measurement method to obtain the characteristic distances.

The gradient fuzzification distance measurement method can represent uncertainty understanding of workers on objective things in a quantitative mode, can also describe importance difference of evaluation opinions, and therefore has important theoretical value and wide application. In particular, the gradient blur set distance measure may be used to calculate the true distance between two points in n-dimensional space, or to vector the (natural length) distance of that point to the origin. Points in both two-dimensional and three-dimensional space can be computed using a gradient blur set distance measure computation method.

In the embodiment of the invention, by a gradient fuzzification distance measurement method, the data relation among the features after the feature weight is calculated is mined, and the distance among the features is calculated.

In a specific implementation, region feature D is assumed_i＝(x_i,y_i) The corresponding weight is w_j(j ═ 1,2,3 … n), and w_j∈[0,1]，And region feature D₁Has the largest weight value, D₂The value of (A) is counted next time.

Suppose two points D in two-dimensional space₁＝(x₁,y₁) And D₂＝(x₂,y₂) The distance between the two is calculated by the following formula:

suppose two points D in three-dimensional space₁＝(x₁,y₁,z₁) And D₂＝(x₂,y₂,z₂) The distance between the two is calculated by the following formula:

the same applies to the distance calculation between two points in the four-dimensional space and the five-dimensional space … … n-dimensional space.

In addition, D is₁And D₂Three common conditions under the distance measurement of the gradient fuzzy set are met:

(1) nonnegativity: d (D)₁,D₂)≥0；

(2) The exchange property: d (D)₁,D₂)＝d(D₂,D₁)；

(3) The reflexibility is as follows:

and S5, generating target gridding information based on the characteristic weight and all the characteristic distances.

In an optional embodiment, generating the target gridding information based on the feature weights and all the feature distances comprises:

according to the magnitude relation of the feature weight, sorting the feature points except the feature central point around the feature central point;

and adjusting the distance between the corresponding feature point and the feature central point according to the feature distance, and sequentially connecting features except the feature central point to obtain the target gridding information.

In the embodiment of the invention, after gradient fuzzy word processing and gradient fuzzy distance measurement calculation, feature central points are arranged according to the magnitude relation of feature weights, and then, according to feature distances between the feature central points and other features, the features are sequentially connected to generate a target gridding information generation schematic diagram as shown in fig. 2. The sequence of the features is sorted according to the weight, so that the problem that the region features are drawn for multiple times during gridding generation can be solved, and the problem that the region features are omitted and lost during the gridding generation process can be prevented.

In the embodiment of the invention, all characteristics of the target area and characteristic data thereof are acquired; performing gradient fuzzification processing on all the characteristic data to obtain corresponding fuzzy characteristic data; the fuzzy feature data comprises: inputting the fuzzy feature data into a pre-constructed feature weight model to obtain feature weights corresponding to the feature data; based on the characteristic data, sequentially calculating characteristic distances among the characteristics by a gradient fuzzy distance measurement method; and generating target gridding information based on the characteristic weight and all the characteristic distances. The method solves the problems that the existing gridding generation method is high in workload and complex in steps, the generation process is easy to have strong subjectivity and low in efficiency.

Referring to fig. 3, a block diagram of a grid generation apparatus based on distance measure of gradient fuzzy sets according to an embodiment of the present invention is shown, where the apparatus includes:

an obtaining module 401, configured to obtain all features of the target area and feature data thereof;

the fuzzification processing module 402 is configured to perform gradient fuzzification processing on all the feature data to obtain corresponding fuzzy feature data;

an input module 403, configured to input the fuzzy feature data into a pre-constructed feature weight model, so as to obtain a feature weight corresponding to the feature data;

a feature distance calculation module 404, configured to sequentially calculate feature distances between the features by a gradient fuzzy distance measurement method based on the feature data;

a target gridding information generating module 405, configured to generate target gridding information based on the feature weight and all the feature distances.

In an optional embodiment, further comprising:

the characteristic center point characteristic module is used for determining a characteristic center point from the characteristic points corresponding to the characteristics based on the numerical relationship of the characteristic weight;

the feature distance calculating module 404 is specifically configured to sequentially calculate distances between the feature points and the feature center points by using the gradient fuzzy distance measuring method, so as to obtain the feature distances.

In an optional embodiment, the target gridding information generating module 405 includes:

the characteristic arrangement submodule is used for uniformly arranging the characteristic points except the characteristic central point around the characteristic central point according to the size relation of the characteristic weight;

and the adjusting submodule is used for adjusting the distance between the corresponding characteristic point and the characteristic central point according to the characteristic distance, and sequentially connecting the characteristics except the characteristic central point to obtain the target gridding information.

In an optional embodiment, the feature weight model is specifically:

wherein M is_JM is the total number of the features of the non-target area, n is the total number of the features of the target area, i is the ith feature, i is less than or equal to m, j is the jth feature, j is less than or equal to n, w_jIs a feature weight, and w_j∈[0,1]， Is r_ijAndthe square of the distance between, r_ijIs a measure of the blurring distance between the ith and jth features, and r_ij∈(0,1)，Is a function ofCalculated value of l in the formula_jTo calculateThe number of features contained within the middle region.

An embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory stores an analyzer program, and when the analyzer program is executed by the processor, the processor executes the steps of the grid generation method based on gradient fuzzy set distance measure according to any of the above embodiments.

The embodiment of the present invention further provides an analyzer readable storage medium, on which an analyzer program is stored, and when the analyzer program is executed by the processor, the method for controlling the grid generation based on the distance measure of the gradient fuzzy set according to any of the above embodiments is implemented.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments provided in the present application, it should be understood that the method, apparatus, electronic device and storage medium disclosed in the present application may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium readable by an analyzer. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a readable storage medium and includes several instructions for enabling an analyzer (which may be a personal analyzer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.