An expression method, similarity description method and device for power grid engineering objects

Abstract

The method for expressing power grid engineering objects of the present invention is based on CityGML data and includes the following steps: Step 1) Parse the CityGML data; Step 2) Based on the dimensions of the CityGML data including spatial location, geometric form, element relationship, attribute characteristics, and semantic description, form an expression primitive structure of power grid engineering objects to store or real-time call power grid engineering object data. Beneficial effects: It can improve the cross-link data penetration ability between different departments and improve the management and application level of power grid engineering data with CityGML data as the basic format.

Description

Technical Field

[0001] The present invention belongs to the field of multi - energy complementary balance of combined energy systems, and particularly relates to an expression method for power grid engineering objects, a similarity description method, and an apparatus therefor. Background Art

[0002] CityGML data is a data format commonly used in power grid engineering and can be used to express the facility construction and results of power grid engineering.

[0003] In the application of power grid engineering data organization and management, multiple links such as design, construction, and acceptance need to be taken into account, resulting in diverse characteristics of data management departments and data application scenarios. According to the application requirements of different parts, it is necessary to formally describe and perform similarity calculations on CityGML data from multiple dimensions. Currently, the cross - link data penetration ability between different departments is insufficient, and the management and application level of power grid engineering data based on CityGML data is relatively low, affecting the overall operation efficiency of data. Summary of the Invention

[0004] In order to solve the defects and deficiencies existing in the prior art, the present invention provides an expression method for power grid engineering objects, a similarity description method, and a device therefor, so as to improve the cross - link data penetration ability between different departments and the management and application level of power grid engineering data based on CityGML data. The specific technical solutions are as follows:

[0005] The expression method for the power grid engineering object includes:

[0006] Parsing the CityGML data to obtain dimension information including: spatial position, geometric form, element relationship, attribute characteristics, and semantic description;

[0007] Forming an expression primitive structure of the power grid engineering object based on the dimension information to store or real - time call power grid engineering object data.

[0008] A further design of the expression method for the power grid engineering object is that the parsing of the CityGML data in step 1) includes:

[0009] Spatial position parsing: Traversing the coordinate point data in the CityGML data in sequence, and recording the maximum and minimum values x max 、x min of the x - axis and the maximum and minimum values y max 、y min of the y - axis of the current power grid facility object, and obtaining the spatial coordinates of the center point of the power grid facility object as Geometric form parsing: Separately parsing out the point set {p1, p2,..., p n}, a set of lines {l1, l2, …, l m}, a set of surfaces {pg1, pg2, …, pg s}, and a set of volumes {v1, v2, …, v t} where an element p in the point set i = (x i , y i ), an element l in the line set i = {p i1 , p i2 , …, p in}, an element pg in the surface set i = {l i1 , l i2 , …, l im}, and an element v in the volume set i = {pg i1 , pg i2 , …, pg is};

[0010] Element relationship analysis: For a given object, traverse other point, line, surface, or volume objects in sequence to find all objects that intersect with the given object, and obtain the associated data set {p1, p2, … l1, l2, …, pg1, pg2, …, v1, v2, …} of the given object;

[0011] Attribute feature analysis: Extract the inherent attributes of the objects in the CityGML data in the form of strings, and associate the inherent attributes with the parsed geometric forms;

[0012] Semantic description analysis: Extract the semantic features of the objects in the CityGML data in the form of strings, and associate the semantic features with the parsed geometric forms.

[0013] The further design of the expression method of the power grid engineering object lies in that the power grid engineering object o is based on the primitive structure expression method of CityGML data as: o = (a1E1, a2E2, a3E3, a4E4, a5E5), where E1 represents the spatial position dimension, E2 represents the geometric form dimension, E3 represents the element relationship dimension, E4 represents the attribute feature dimension, E5 represents the semantic description dimension, and a i , i = 1, …, 5 represent the coefficients of each dimension.

[0014] The present invention also provides a method for describing the similarity of power grid engineering objects using the expression method of power grid engineering objects, including the following steps: Step a) Construct a similarity calculation subspace of CityGML data according to the expression primitive structure through a geometric algebra subspace generation mechanism and similarity calculation constraint rules;

[0015] Step b) Convert the data of each dimension to the geometric algebra space and combine them into geometric algebra multivectors to achieve formal expression;

[0016] Step c) Construct similarity measure operators for each dimension of the geometric algebra space, design similarity calculation methods for each single dimension and mixed dimensions of spatial position, geometric shape, element relationship, attribute feature, and semantic description, and construct a comprehensive similarity index according to specific power grid application requirements to achieve similarity calculation of CityGML data for specific dimensions.

[0017] The further design of the similarity description method for power grid engineering objects lies in that the similarity calculation of the set dimensions of power grid engineering objects in step a) is carried out on a specific dimension space. 32 combined dimension subspaces can be constructed on a five-dimensional space. The dimension space contains at least 2 dimension vectors. Then the 26 multi-dimensional subspaces generated are set as:

[0018]

[0019] E1 represents the spatial position dimension, E2 represents the geometric shape dimension, E3 represents the element relationship dimension, E4 represents the attribute feature dimension, E5 represents the semantic description dimension, and E 1...5 represents the subspace formed by the combination of each dimension.

[0020] The further design of the similarity description method for power grid engineering objects lies in that in step b), the dimension information is expressed as the coefficient a i , i = 1, …, 5, and map the dimension information extracted from CityGML data to the geometric algebra space;

[0021] When i = 1, a1 represents the position information. For the given coordinate loc = (x, y), the geometric algebra expression of this coordinate is a1(loc) = xe1 + ye2, where e1 and e2 represent the abscissa and ordinate dimensions respectively;

[0022] When i = 2, a2 represents the geometric shape, and it is expressed as a dimension hierarchical nested structure composed of polygon sets, surface sets, line sets, and point sets;

[0023] When i = 3, a3 represents the topological information between objects. For the given power grid engineering object o, its expression is a3(o) = {id1, id2, …}, where id i represents the ID of other objects that are topologically associated with the object o;

[0024] When i = 4, a4 represents the attribute features of the object. The geometric algebra expression of the attribute features is: a4(o) = atr1 * g1 + atr2 * g2 + …, where atr1, atr2, … represent attribute strings, and g1, g2, … represent the corresponding attribute dimensions;

[0025] When i = 5, a5 represents the semantic description of the object, and the geometric algebra expression of the semantic description is: a5(o) = sem1*f1 + sem2*f2 + …, where sem1, sem2, … represent semantic strings, and f1, f2, … represent the corresponding semantic dimensions;

[0026] The formal expression of the composite dimension object is: GA(o) = a1E1 + a2E2 + a3E3 + a4E4 + a5E5.

[0027] The further design of the similarity description method for the power grid engineering object lies in that in step c), for the given power grid engineering objects o and o′, their formal expressions are respectively: GA(o) = a1E1 + a2E2 + a3E3 + a4E4 + a5E5 and GA(o′) = a1′E1 + a2′E2 + a3′E3 + a4′E4 + a5′E5, and the similarity calculation is respectively carried out in 5 single - dimension and 26 composite - dimension sub - spaces.

[0028] For the given 5 single - dimension sub - spaces E i , its similarity calculation formula is When i = 1, d(a i , a i ′) represents the calculation of the spatial position similarity, which is solved by the Euclidean distance: max d is the maximum value of the distance between objects in the data;

[0029] When i = 2, d(a i , a i ′) represents the similarity of the geometric shape. Since geometric objects have multiple categories such as points, lines, planes, and solids, when calculating, first use the dimension calculation function grade(a i ) to judge whether the dimensions of the two objects are equal. When grade(a i ) ≠ grade(a i ′), d(a i , a i ′) = 0; when grade(a i ) = drade(a′ i ), it is necessary to consider cases. When the dimension is 1, the distance of points is used to calculate the similarity; when the dimension is 2 or 3, the similarity of line and plane objects is calculated; when the dimension is 4, the similarity of solid objects needs to be calculated. When i = 3, d(a i , a i ′) represents the similarity of the element relationship. Since at this time a i , a iThe sequence of objects associated with it is stored in '', the object is converted into characters, and the similarity is solved through the character distance. The calculation formula is: d(a i , a i ′) = N(a i ∩ a i ′) / max(N(a i ), N(a i ′)), where a i ∩ a i ′ represents the common part in a i and a i ′, and N() is used to calculate the number of characters in the parentheses;

[0030] When i = 4, 5, d(a i , a i ′) represents the character distance, and its calculation formula is: d(a i , a i ′) = N(a i ∩ a i ′) / max(N(a i ), N(a i ′)), where a i ∩ a i ′ represents the common part in a i and a i ′, and N() is used to calculate the number of characters in the parentheses;

[0031] For a given two-dimensional or higher-dimensional subspace E i…j , i ≠ j and 1 ≤ i, j ≤ 5, the calculation formula for the mixed-dimensional similarity is d(a i , a′ i ) and d(a j , a′ j ) represent the calculation of the distance in each dimension, and n is the number of dimensions of the subspace E i…j .

[0032] The present invention also provides a similarity calculation device for grid engineering objects, including:

[0033] An analysis module for analyzing CityGML data to obtain the analyzed CityGML data;

[0034] An execution module for forming an expression primitive structure of grid engineering objects based on the analyzed CityGML data to store or real-time call grid engineering object data; a similarity calculation module for quantitatively expressing single or plural dimensional information between grid engineering objects based on the expression primitive structure.

[0035] The further design of the similarity calculation device for the power grid engineering object lies in that the parsing module is specifically used for

[0036] Spatial position parsing: Traverse the coordinate point data in the CityGML data in sequence, and record the maximum and minimum values x max 、x min and the maximum and minimum values y max 、y min of the x-axis and y-axis of the current power grid facility object, and obtain the spatial coordinates of the center point of the power grid facility object as Geometric shape parsing: Parse out the point set {p1, p2,..., p n}, line set {l1, l2,..., l m}, surface set {pg1, pg2,..., pg s} and volume set {v1, v2,..., v t} in the CityGML data respectively. Among them, the element p i in the point set = (x i , y i ), the element l i in the line set = {p i1 , p i2 ,..., p in}, the element pg i in the surface set = {l i1 , l i2 ,..., l im}, and the element v i in the volume set = {pg i1 , pg i2 ,..., pg is};

[0037] Element relationship parsing: For a given object, traverse other point, line, surface or volume objects in sequence, find all objects that intersect with the given object, and obtain the associated data set {p1, p2,..., l1, l2,..., pg1, pg2,..., v1, v2,...} of the given object;

[0038] Attribute feature parsing: Extract the inherent attributes of the objects in the CityGML data in the form of strings, and associate the inherent attributes with the parsed geometric shapes;

[0039] Semantic description parsing: Extract the semantic features of the objects in the CityGML data in the form of strings, and associate the semantic features with the parsed geometric shapes.

[0040] The further design of the similarity calculation device for power grid engineering objects lies in that the quantization description process of the similarity calculation module is specifically as follows: First, according to the expression primitive structure, through the geometric algebra subspace generation mechanism and the similarity calculation constraint rules, a multi-dimensional subspace for similarity calculation of CityGML data is constructed; then, the data of each dimension is converted into the geometric algebra space and combined into a geometric algebra multi-vector to achieve formalized expression; finally, the similarity measure operators of each dimension in the geometric algebra space are constructed, the similarity calculation methods of the single dimension and the mixed dimension of the spatial position, geometric form, element relationship, attribute feature, and semantic description are designed, and according to the specific power grid application requirements, a comprehensive similarity index is constructed to achieve the similarity calculation of CityGML data in specific dimensions.

[0041] The beneficial effects of the present invention are as follows:

[0042] The expression method of the power grid engineering object of the present invention proposes a dimension description method of CityGML data based on geometric algebra. The five dimensions respectively describe the spatial position, geometric form, element relationship, attribute feature, and semantic description of CityGML data. The description content is more abundant and structured, can more effectively reflect the characteristics of power grid engineering data, can improve the cross-link data penetration ability between different departments, and improve the management and application level of power grid engineering data with CityGML data as the basic format.

[0043] The similarity description method of the power grid engineering object of the present invention constructs 26 similarity calculation subspace dimensions through the geometric algebra subspace generation mechanism and the power grid engineering data similarity calculation constraint rules, and constructs the similarity calculation method of CityGML data according to the needs from 26 similarity calculation dimensions, which can more effectively meet the needs of power grid engineering data organization and management. Description of the Drawings

[0044] Figure 1 It is a schematic flow chart of the expression method and similarity description method of power grid engineering objects.

[0045] Figure 2 It is a schematic diagram of an example of power grid engineering CityGML data. Detailed Embodiments

[0046] The present invention will be described in detail below with reference to the drawings and specific embodiments.

[0047] As Figure 1 , the expression method of the power grid engineering object in this embodiment, based on CityGML data, includes the following steps:

[0048] Step 1) Parse the CityGML data;

[0049] Step 2) Based on the dimensions of CityGML data including spatial location, geometric form, element relationship, attribute features, and semantic description, form the expression primitive structure of power grid engineering objects to store or real-time retrieve power grid engineering object data.

[0050] The parsing of CityGML data in Step 1) includes:

[0051] Spatial location parsing: Traverse the coordinate point data in the CityGML data in sequence, record the maximum and minimum values x max 、x min and the maximum and minimum values y max 、y min of the current power grid facility object, and obtain the spatial coordinates of the center point of the power grid facility object as Geometric form parsing: Parse out the point set {p1, p2, …, p n}, line set {l1, l2, …, l m}, surface set {pg1, pg2, …, pg s}, and volume set {v1, v2, …, v t} in the CityGML data respectively. Among them, the element p i in the point set = (x i , y i ), the element l i in the line set = {p i1 , p i2 , …, p in}, the element pg i in the surface set = {l i1 , l i2 , …, l im}, and the element v i in the volume set = {pg i1 , pg i2 , …, pg is};

[0052] Element relationship parsing: For a given object, traverse other point, line, surface, or volume objects in sequence, find all objects that intersect with it, and obtain the associated data set {p1, p2, …, l1, l2, …, pg1, pg2, …, v1, v2, …} of the given object;

[0053] Attribute feature parsing: Extract the inherent attributes of the object in the CityGML data in the form of a string, and associate the inherent attributes with the parsed geometric form;

[0054] Semantic description parsing: Extract the semantic features of the object in the CityGML data in the form of a string, and associate the semantic features with the parsed geometric form.

[0055] In step 2), the primitive structure expression of the power grid engineering object o based on CityGML data is: o = (a1E1, a2E2, a3E3, a4E4, a5E5), where E1 represents the spatial position dimension, E2 represents the geometric form dimension, E3 represents the feature relationship dimension, E4 represents the attribute feature dimension, E5 represents the semantic description dimension, and a i , i = 1, …, 5 represent the coefficients of each dimension.

[0056] The similarity description method of the power grid engineering object adopting the expression method of the power grid engineering object includes the following steps:

[0057] Step a): Construct a similarity calculation subspace of CityGML data through a geometric algebra subspace generation mechanism and similarity calculation constraint rules according to the expression primitive structure.

[0058] Step b): Convert the data of each dimension to the geometric algebra space and combine them into a geometric algebra multivector to achieve formal expression.

[0059] Step c): Construct similarity measure operators for each dimension of the geometric algebra space, design similarity calculation methods for each single dimension and mixed dimensions of spatial position, geometric form, feature relationship, attribute feature, and semantic description, and construct a comprehensive similarity index according to specific power grid application requirements to achieve similarity calculation of CityGML data in specific dimensions.

[0060] The similarity calculation of the set dimension of the power grid engineering object in step a) is carried out on a specific dimension space. 32 combined dimension subspaces can be constructed in the five-dimensional space. The dimension space contains at least 2 dimension vectors. Then the 26 multi-dimensional subspaces generated are:

[0061]

[0062] In step b), by expressing the dimension information as the coefficient a of the dimension i , i = 1, …, 5, map the dimension information extracted from CityGML data to the geometric algebra space;

[0063] When i = 1, a1 represents the position information. For the given coordinate loc = (x, y), the geometric algebra expression of this coordinate is a1(loc) = xe1 + ye2, where e1 and e2 represent the abscissa and ordinate dimensions respectively;

[0064] When i = 2, a2 represents the geometric form and is expressed as a dimension hierarchical nested structure composed of polygon sets, surface sets, line sets, and point sets;

[0065] When i = 3, a3 represents the topological information between objects. For a given object o, it is expressed as a3(o) = {id1, id2, …}, where id i represents the IDs of other objects that are topologically associated with object o;

[0066] When i = 4, a4 represents the attribute characteristics of an object. The geometric algebra expression of the attribute characteristics is: a4(o) = atr1*g1 + atr2*g2 + …, where atr1, atr2, … represent attribute strings, and g1, g2, … represent the corresponding attribute dimensions;

[0067] When i = 5, a5 represents the semantic description of an object. The geometric algebra expression of the semantic description is: a5(o) = sem1*f1 + sem2*f2 + …, where sem1, sem2, … represent semantic strings, and f1, f2, … represent the corresponding semantic dimensions;

[0068] The formal expression of a composite - dimension object is: GA(o) = a1E1 + a2E2 + a3E3 + a4E4 + a5E5. In step c), for the given power - grid engineering data o and o′, their formal expressions are: GA(o) = a1E1 + a2E2 + a3E3 + a4E4 + a5E5 and GA(o′) = a1′E1 + a2′E2 + a3′E3 + a4′E4 + a5′E5 respectively. The similarity calculation is carried out in 5 single - dimension and 26 composite - dimension sub - spaces.

[0069] For the given 5 single - dimension sub - spaces E i , its similarity calculation formula is When i = 1, d(a i , a i ′) represents the calculation of spatial - position similarity, which is solved by the Euclidean distance: max d is the maximum value of the distance between objects in the data;

[0070] When i = 2, d(a i , a i ′) represents the similarity of geometric shapes. Since geometric objects have various categories such as points, lines, planes, and solids, when calculating, first use the dimension - calculation function grade(a i ) to judge whether the dimensions of the two objects are equal. When grade(a i ) ≠ grade(a i ′), d(a i , a i ′) = 0; when grade(a i ) = grade(a′ i) When considering different cases, when the dimension is 1, the similarity is calculated using the distance between points; when the dimension is 2 or 3, the similarity of line and plane objects needs to be calculated. Here, the overlapping area ratio of the circumscribed rectangles is used for calculation, that is, the circumscribed rectangles PG(a i ,a i ′) of a and a i ), PG(a i ′) are obtained respectively, and the similarity calculation formula is where s(PG(a i )) and s(PG(a′ i )) represent the areas of PG(a i ) and PG(a i ′) respectively, and Δs represents the intersecting area of the two. It can be seen that the calculation of morphological similarity here pays more attention to the spatial range information of geometric shapes. If more attention is paid to geometric shapes, the Hu moment method can also be used for similarity calculation. When the dimension is 4, the similarity of volume objects needs to be calculated. Here, a similar method is used, and the overlapping volume ratio of the circumscribed hexahedrons is used for calculation. In practical applications, information such as the volume and centroid of volume objects can also be considered during the similarity calculation process to obtain more stable results. This calculation process is a common technical means for those skilled in the art and will not be elaborated here.

[0071] When i = 3, d(a i ,a i ′) represents the similarity of the element relationship. Since the sequences of associated objects are stored in a i ,a i ′ at this time, the objects are converted into characters, and the similarity is solved through the character distance. The calculation formula is: d(a i ,a i ′) = N(a i ∩a i ′) / max(N(a i ), N(a i ′)), where a i ∩a i ′ represents the common part in a i and a i ′, and N() is used to calculate the number of characters in the parentheses;

[0072] When i = 4, 5, d(a i ,a i ′ represents the character distance, and the calculation formula is: d(a i ,a i ′) = N(a i ∩a i ′) / max(N(a i ), N(a i ′)), where ai ∩a i ′ represents a i and a i ′ is the common part, and N() is used to calculate the number of characters in the parentheses;

[0073] For a given subspace E in two or more dimensions i…j , i≠j and 1≤i,j≤5, the calculation formula for the mixed-dimensional similarity is d(a i , a i ′) and d(a j , a j ′) represent the calculation of the distance in each dimension, and n is the number of dimensions of the subspace E i…j .

[0074] The following combines Figure 2 to give an example of CityGML data for a power grid project. The specific implementation process is as follows:

[0075] To clearly describe the implementation process of the technical solution, a simplified CityGML data for the power grid project is used as Figure 2 shown. Since CityGML data has a standard encoding format, the description of its encoding content is omitted here. Using the standard reading API of CityGML, it is easy to obtain that the data objects in the data are composed of 3 tower objects, 2 substation objects, 2 road objects, and 5 power transmission line objects. They are numbered in sequence and expressed as {o1, o2, o3, o4, o5, o6, o7, o8, o9, o 10 , o 11 , o 12}, where o1, o2, o3 represent towers, o4, o5 represent substations, o6, o7 represent roads, and o8, o9, o 10 , o 11 , o 12 represent 5 power transmission line sets.

[0076] Interpret and describe the geometric and attribute information of the 12 data objects obtained by the standard reading API of CityGML in five dimensions: spatial position, geometric shape, element relationship, attribute characteristics, and semantic description. It can be obtained that:

[0077] In the formula, the second item of the five-tuple is the geometric shape of the object. Due to space limitations, only the first-level composition element IDs are listed here. The subsequent nested levels can be obtained from the polygon-edge association table and edge-node association table obtained by parsing.

[0078] Mapping each dimension to the geometric algebra space, its geometric algebra expression can be obtained. As an example, here, only the geometric algebra expressions of objects o1, o4, o6, and o8 are listed by category:

[0079]

[0080] Further calculate the similarity of each data object. For example, for the iron tower and power distribution room objects o1 and o4, the one-dimensional similarity calculation results are respectively:

[0081]

[0082] The results show that the distance similarity between the two objects is 0.594, and the distance is relatively close; the geometric shape similarity is 0, and they do not intersect in space; the element relationship similarity is 0.5, indicating a certain degree of correlation; due to having the same responsible person, their attribute similarity is relatively high, but the semantic similarity is relatively low.

[0083] The multi-dimensional similarity needs to be constructed according to application requirements. For example, for the power grid equipment inspection business that focuses on spatial location and association relationship, a mixed dimension similarity can be constructed Calculate the mixed dimension similarities of objects o1, o4 and o1, o3 respectively, and the results are:

[0084]

[0085] From the calculation results, it can be seen that although objects o1 and o4 belong to different categories, due to their proximity in space and topological association, the obtained mixed dimension similarity is larger, while although objects o1 and o3 belong to the iron tower objects, the obtained similarity is smaller, indicating that objects o1 and o4 have relatively stronger collaboration during the inspection operation.

[0086] This embodiment also provides a similarity calculation device for power grid engineering objects. The device mainly consists of: a parsing module, an execution module, and a similarity calculation module. Among them, the parsing module is used to parse the CityGML data to obtain the parsed CityGML data; the execution module is used to form the expression primitive structure of the power grid engineering object based on the parsed CityGML data to store or real-time call the power grid engineering object data; the similarity calculation module quantifies the single or plural dimension information between power grid engineering objects based on the expression primitive structure.

[0087] The parsing module is specifically used for:

[0088] Spatial position parsing: Traverse the coordinate point data in the CityGML data in sequence, and record the maximum and minimum values x of the x-axis of the current power grid facility object max 、x minand the maximum and minimum values of y on the y-axis max , y min , and the spatial coordinates of the center point of the power grid facility object are obtained as Geometric shape analysis: Analyze the point set {p1, p2,..., p n}, line set {l1, l2,..., l m}, surface set {pg1, pg2,..., pg s}, and volume set {v1, v2,..., v t} in the CityGML data respectively. Among them, the element p i in the point set = (x i , y i ), the element l i in the line set = {p i1 , p i2 ,..., p in}, the element pg i in the surface set = {l i1 , l i2 ,..., l im}, and the element v i in the volume set = {pg i1 , pg i2 ,..., pg is};

[0089] Element relationship analysis: For a given object, traverse other point, line, surface, or volume objects in sequence, find all objects that intersect with the given object, and obtain the associated data set {p1, p2,..., l1, l2,..., pg1, pg2,..., v1, v2,...} of the given object;

[0090] Attribute feature analysis: Extract the inherent attributes of the objects in the CityGML data in the form of strings, and associate the inherent attributes with the analyzed geometric shapes;

[0091] Semantic description analysis: Extract the semantic features of the objects in the CityGML data in the form of strings, and associate the semantic features with the analyzed geometric shapes.

[0092] Furthermore, the quantization description process of the similarity calculation module is specifically as follows: First, according to the expression primitive structure, through the geometric algebra subspace generation mechanism and the similarity calculation constraint rules, a multi-dimensional subspace for similarity calculation of CityGML data is constructed; then, the data of each dimension is transformed into the geometric algebra space and combined into a geometric algebra multivector to achieve formalized expression; finally, similarity measure operators for each dimension of the geometric algebra space are constructed, similarity calculation methods for each single dimension and mixed dimension of spatial position, geometric shape, feature relationship, attribute feature, and semantic description are designed, and according to the specific power grid application requirements, a comprehensive similarity index is constructed to achieve the similarity calculation of CityGML data for specific dimensions.

[0093] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0094] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0095] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for realizing the functions specified in one process or a plurality of processes and / or blocks. Figure 1 One process or a plurality of processes and / or blocks Figure 1 Steps for realizing the functions specified in one block or a plurality of blocks.

[0097] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0098] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for expressing power grid engineering objects, characterized in that, Including: Parsing the CityGML data to obtain the parsed CityGML data; Forming an expression primitive structure of the power grid engineering object based on the parsed CityGML data to store or real-time call the power grid engineering object data; The parsing of the CityGML data includes: spatial position parsing, geometric shape parsing, element relationship parsing, attribute feature parsing, semantic description parsing; Spatial position analysis: Traverse the coordinate point data in the CityGML data in sequence, and record the maximum and minimum values of the x-axis of the current power grid facility object as x max , x min and the maximum and minimum values of the y-axis as y max , y min . The spatial coordinates of the center point of the power grid facility object are obtained as Geometric Morphology Analysis: Analyze the point set {p1, p2, …, p n}, line set {l1, l2, …, l m}, surface set {pg1, pg2, …, pg s}, and volume set {v1, v2, …, v t} in the CityGML data respectively. Among them, the element p i in the point set = (x i , y i ), the element l i in the line set = {p i1 , p i2 , …, p in}, the element pg i in the surface set = {l i1 , l i2 , …, l im}, and the element v i in the volume set = {pg i1 , pg i2 , …, pg is}; Element relationship parsing: For a given object, sequentially traverse other point, line, surface or volume objects, find all objects that intersect with the given object, and obtain the associated data set {p1, p2, …, l1, l2, …, pg1, pg2, …, v1, v2, …} of the given object; Attribute feature parsing: Extract the inherent attributes of the object in the CityGML data in the form of a string, and associate the inherent attributes with the parsed geometric shape; Semantic description parsing: Extract the semantic features of the object in the CityGML data in the form of a string, and associate the semantic features with the parsed geometric shape; The primitive structure expression of the power grid engineering object o based on CityGML data is: o = (a1E1, a2E2, a3E3, a4E4, a5E5), where E1 represents the spatial position dimension, E2 represents the geometric form dimension, E3 represents the feature relationship dimension, E4 represents the attribute feature dimension, E5 represents the semantic description dimension, and a i , i = 1, …, 5 represent the coefficients of each dimension.

2. A method for describing the similarity of power grid engineering objects using the expression method of power grid engineering objects as described in claim 1, characterized in that, Including the following steps: Step a) Construct a multi-dimensional subspace for similarity calculation of CityGML data according to the expression primitive structure through a geometric algebra subspace generation mechanism and similarity calculation constraint rules; Step b) Convert the data of each dimension to the geometric algebra space and combine them into a geometric algebra multivector to achieve formal expression; Step c) Construct similarity measure operators for each dimension of the geometric algebra space, design similarity calculation methods for each single dimension and mixed dimension of spatial position, geometric shape, element relationship, attribute feature, and semantic description, and construct a comprehensive similarity index according to specific power grid application requirements to achieve similarity calculation of CityGML data in a specific dimension.

3. The method for describing the similarity of power grid engineering objects according to claim 2, characterized in that In step a), the similarity calculation of the set dimension of the power grid engineering object is set to be performed in a specific dimension space. 32 combined dimension subspaces are constructed in a five-dimensional space. The dimension space contains at least 2 dimension vectors. Then the 26 multi-dimensional subspaces generated are set as follows: E1 represents the spatial position dimension, E2 represents the geometric form dimension, E3 represents the element relationship dimension, E4 represents the attribute feature dimension, E5 represents the semantic description dimension, and E 1...5 represents the subspace formed by the combination of each dimension.

4. The method for describing the similarity of power grid engineering objects according to claim 3, wherein Step b) maps the respective dimensional information extracted from the CityGML data to the geometric algebra space by expressing the dimensional information as coefficients a of the dimensions i , i = 1, …, 5 When i = 1, a1 represents the position information. For a given coordinate loc = (x, y), the geometric algebra expression of this coordinate is a1(loc) = xe1 + ye2, where e1 and e2 respectively represent the abscissa and ordinate dimensions; When i = 2, a2 represents the geometric shape, and it is expressed as a dimension-level nested structure composed of polygon sets, surface sets, line sets and point sets; When i = 3, a3 represents the topological information between objects. For a given power grid engineering object o, it is expressed as a3(o) = {id1, id2, …}, where id i represents the IDs of other objects that are topologically associated with object o; When i = 4, a4 represents the attribute features of the object. The geometric algebra expression of the attribute features is: a4(o) = atr1*g1 + atr2*g2 + …, where atr1, atr2, … represent attribute strings, and g1, g2, … represent the corresponding attribute dimensions; When i = 5, a5 represents the semantic description of the object. The geometric algebra expression of the semantic description is: a5(o) = sem1*f1 + sem2*f2 + …, where sem1, sem2, … represent semantic strings, and f1, f2, … represent the corresponding semantic dimensions; The formal expression of the composite dimensional object is: GA(o) = a1E1 + a2E2 + a3E3 + a4E4 + a5E5.

5. The method for describing the similarity of power grid engineering objects according to claim 4, characterized in that In step c), for the given power grid project objects o and o′, their formal expressions are respectively: GA(o) = a1E1 + a2E2 + a3E3 + a4E4 + a5E5 and GA(o′) = a1′E1 + a2′E2 + a3′E3 + a4′E4 + a5′E5. The similarity calculation is carried out in 5 single-dimensional and 26 composite-dimensional subspaces respectively. For a given five single-dimensional subspaces E i , its similarity calculation formula is When i = 1, d(a i , a i ′) represents the calculation of spatial position similarity, which is solved by Euclidean distance: max d is the maximum value of the distances between objects in the data; When i = 2, d(a i , a i ′) represents the similarity of geometric forms. Since geometric objects include various categories such as points, lines, planes, and solids, when calculating, first use the dimension calculation function grade(a i ) to determine whether the dimensions of the two objects are equal. When grade(a i ) ≠ grade(a i ′), d(a i , a i ′) = 0; when grade(a i ) = grade(a i ′), it is necessary to consider different cases. When the dimension is 1, calculate the similarity using the distance between points; when the dimension is 2 or 3, calculate the similarity of line and plane objects; When the dimension is 4, the similarity of the volume object needs to be calculated. When i = 3, d(a i , a i ′) represents the similarity of the element relationship. Since at this time, the object sequence associated with it is stored in a i , a i ′, the object is converted into characters, and the similarity is solved through the character distance. The calculation formula is: d(a i , a i ′) = N(a i ∩ a i ′) / max(N(a i ), N(a i ′)), where a i ∩ a i ′ represents the common part in a i and a i ′, and N() is used to calculate the number of characters in the parentheses; When i = 4 or 5, d(a i , a i ′) represents the character distance, and its calculation formula is: d(a i , a i ′) = N(a i ∩ a i ′) / max(N(a i ), N(a i ′)), where a i ∩ a i ′ represents the common part in a i and a i ′, and N() is used to calculate the number of characters in the parentheses; For a given subspace E in two or more dimensions i…j , where i ≠ j and 1 ≤ i, j ≤ 5, the calculation formula for the mixed - dimensional similarity is d(a i , a i ′) and d(a j , a j ′) represent the calculation of distances in each dimension, and n is the number of dimensions of the subspace E i…j .

6. A similarity calculation device for power grid engineering objects, characterized in that, Including: A parsing module for parsing CityGML data to obtain the parsed CityGML data. An execution module for forming an expression primitive structure of the power grid project object based on the parsed CityGML data to store or real-time call the power grid project object data; a similarity calculation module for quantitatively describing the single or plural dimensional information between power grid project objects based on the expression primitive structure. The parsing module is specifically used for Spatial position analysis: Traverse the coordinate point data in the CityGML data in sequence, and record the maximum and minimum values of the x-axis of the current power grid facility object as x max and x min and the maximum and minimum values of the y-axis as y max and y min . The spatial coordinates of the center point of the power grid facility object are obtained as Geometric form analysis: Analyze the point set {p1, p2, …, p n}, line set {l1, l2, …, l m}, surface set {pg1, pg2, …, pg s}, and volume set {v1, v2, …, v t} in the CityGML data respectively. Among them, the element p i in the point set = (x i , y i ), the element l i in the line set = {p i1 , p i2 , …, p in}, the element pg i in the surface set = {l i1 , l i2 , …, l im}, and the element v i in the volume set = {pg i1 , pg i2 , …, pg is}; Element relationship parsing: For a given object, traverse other point, line, surface or volume objects in sequence to find all objects that intersect with the given object, and obtain the associated data set {p1, p2,..., l1, l2,..., pg1, pg2,..., v1, v2,...} of the given object. Attribute feature parsing: Extract the inherent attributes of the object in the CityGML data in the form of a string, and associate the inherent attributes with the parsed geometric form. Semantic description parsing: Extract the semantic features of the object in the CityGML data in the form of a string, and associate the semantic features with the parsed geometric form. The quantization description process of the similarity calculation module is specifically as follows: First, according to the expression primitive structure, construct a multi-dimensional subspace for similarity calculation of CityGML data through a geometric algebra subspace generation mechanism and similarity calculation constraint rules; then convert the data of each dimension to the geometric algebra space and combine them into a geometric algebra multivector to achieve formal expression; finally, construct similarity measure operators for each dimension of the geometric algebra space, design similarity calculation methods for each single dimension and mixed dimension of spatial position, geometric form, element relationship, attribute feature, and semantic description, and construct a comprehensive similarity index according to specific power grid application requirements to achieve similarity calculation of CityGML data for specific dimensions.

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