Image data mapping processing method, electronic device, storage medium and program

Abstract

The embodiment of the invention discloses an image data mapping processing method, electronic equipment, a storage medium and a program, and the method comprises the steps: obtaining target vector drawing data and an association mapping data set of the target vector drawing data; wherein the target vector drawing data comprises primitives; carrying out self-adaptive matching on the primitives in the target vector drawing data and the association mapping data set to obtain a self-adaptive matching result; and establishing a bidirectional mapping relationship between the primitives in the target vector drawing data and the association mapping data set according to the self-adaptive matching result. According to the technical scheme provided by the embodiment of the invention, the bidirectional mapping relationship between the image and the data can be established, so that the operation and maintenance management efficiency and accuracy of a service system are improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of data processing, and in particular to an image data mapping processing method, an electronic device, a storage medium and a program. BACKGROUND

[0002] At present, digital technology has penetrated into key application scenarios such as industrial production, intelligent terminals, medical diagnosis and security monitoring. Image data has become the core carrier of data acquisition, transmission and analysis due to its natural advantage of ultra-high information density. However, the existing technical solutions are generally limited to one-way data extraction mode from image to database, or one-way data conversion path from image to information system, and have not realized the two-way tracing capability from database to corresponding image elements in image. This technical shortcoming directly restricts the overall efficiency improvement of operation and maintenance work. SUMMARY

[0003] Embodiments of the present application provide an image data mapping processing method, device, electronic device, storage medium and program, which can establish a two-way mapping relationship between image and data, thereby improving the efficiency and accuracy of business system operation and maintenance.

[0004] According to an aspect of the present application, an image data mapping processing method is provided, comprising:

[0005] obtaining target vector drawing data and an associated mapping data set of the target vector drawing data; wherein the target vector drawing data comprises image elements;

[0006] adaptively matching the image elements in the target vector drawing data and the associated mapping data set to obtain an adaptive matching result;

[0007] establishing a two-way mapping relationship between the image elements in the target vector drawing data and the associated mapping data set according to the adaptive matching result.

[0008] According to another aspect of the present application, an image data mapping processing device is provided, comprising:

[0009] a data acquisition module configured to obtain target vector drawing data and an associated mapping data set of the target vector drawing data; wherein the target vector drawing data comprises image elements;

[0010] an adaptive matching module configured to adaptively match the image elements in the target vector drawing data and the associated mapping data set to obtain an adaptive matching result;

[0011] The bidirectional mapping relationship establishing module is configured to establish a bidirectional mapping relationship between the graphic elements in the target vector drawing data and the associated mapping data set according to the adaptive matching result.

[0012] According to another aspect of the present application, there is provided an electronic device comprising:

[0013] at least one processor; and

[0014] a memory connected to the at least one processor in communication; wherein

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the mapping processing method of image data according to any one of the embodiments of the present application.

[0016] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to implement the mapping processing method of image data according to any one of the embodiments of the present application when executed by the processor.

[0017] According to another aspect of the present application, there is also provided a computer program product comprising a computer program for implementing the mapping processing method of image data according to any one of the embodiments of the present application when executed by a processor.

[0018] The embodiments of the present application obtain the target vector drawing data comprising graphic elements and the associated mapping data set of the target vector drawing data, and perform adaptive matching on the graphic elements in the target vector drawing data and the associated mapping data set to obtain an adaptive matching result. After obtaining the adaptive matching result, a bidirectional mapping relationship between the graphic elements in the target vector drawing data and the associated mapping data set is established according to the adaptive matching result. The above-mentioned solution solves the problem of lack of bidirectional association between images and data in the prior art, and can establish a bidirectional mapping relationship between images and data, thereby improving the efficiency and accuracy of business system operation and maintenance management.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to make the technical solutions in the embodiments of the present application clearer, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description only show some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0021] Figure 1 is a flow chart of an image data mapping processing method provided by an embodiment of the present application;

[0022] Figure 2 is a flow chart of an image data mapping processing method provided by an embodiment of the present application;

[0023] Figure 3 is a flow chart of a specific image-based data matching method provided by an embodiment of the present application;

[0024] Figure 4 is an architecture diagram of a railway catenary digital twin system provided by an embodiment of the present application;

[0025] Figure 5 is a schematic diagram of an image data mapping processing device provided by an embodiment of the present application;

[0026] Figure 6 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the technical solutions in the embodiments of the present application clearer, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description only show some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0028] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to include only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment one

[0030] Figure 1 is a flowchart of an image data mapping processing method provided by Embodiment one of the present application. The present embodiment can be applied to the case of establishing a bidirectional mapping relationship between an image and data. The method can be executed by an image data mapping processing device. The device can be implemented in software and / or hardware, and can be integrated in an electronic device, which can be a terminal device or a server device, as long as it can execute the image data mapping processing method. The present application does not limit the specific type of the electronic device. Correspondingly, as shown in Figure 1 the method includes the following operations:

[0031] S110, obtaining target vector drawing data and an associated mapping data set of the target vector drawing data, wherein the target vector drawing data includes a graphic primitive.

[0032] The target vector drawing data can be vector format graphic data used to represent and identify a specific target. For example, the target vector drawing data can include at least one graphic primitive. The graphic primitive can be the most basic indivisible image unit in the target vector drawing data. The target vector drawing data can include, but is not limited to, image data of a railway catenary, image data of a city rail transit catenary, image data of a trolleybus catenary, and image data of a device composition structure, etc. The present application does not limit the specific type of the target vector drawing data. It should be noted that the target vector drawing data can be in a CAD (Computer-Aided Design) or other vector format, and the present application does not limit the specific format type of the target vector drawing data. The associated mapping data set can be a data set associated with the target vector drawing data in a business system.

[0033] In the present embodiment, the vector drawing data to be bidirectionally associated and mapped can be taken as the target vector drawing data, and the data set to be associated with the target vector drawing data can be taken as the associated mapping data set of the target vector drawing data. To establish a bidirectional mapping relationship between an image and data, the target vector drawing data and the associated mapping data set of the target vector drawing data can be obtained first.

[0034] S120, adaptively matching the graphic primitive in the target vector drawing data and the associated mapping data set to obtain an adaptive matching result.

[0035] The adaptive matching result can be the result of adaptively matching the graphic primitive in the target vector drawing data and the associated mapping data set.

[0036] Accordingly, after obtaining the target vector drawing data and the associated mapping dataset of the target vector drawing data, the primitives in the target vector drawing data can be adaptively matched with each data object in the associated mapping dataset to determine the adaptive matching result of each primitive and each data object.

[0037] S130. Based on the adaptive matching result, establish a bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset.

[0038] Among them, the bidirectional mapping relationship can be a bidirectional reversible data association mechanism between primitives in the target vector drawing data and data objects in the associated mapping dataset.

[0039] Correspondingly, after adaptively matching the primitives and associated mapping datasets in the target vector drawing data and obtaining the adaptive matching results, a bidirectional mapping relationship between the primitives and associated mapping datasets in the target vector drawing data can be established based on the adaptive matching results between the primitives and data objects.

[0040] Therefore, the image data mapping processing method provided in this embodiment of the invention establishes a bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset through the adaptive matching results of the primitives and the data objects in the associated mapping dataset. This enables the closed-loop tracing capability of accurately retrieving business data through primitives and locating primitives in reverse through business data, thereby improving the execution efficiency of data query and fault location functions in the operation and maintenance management of the business system and enhancing the intelligent operation and maintenance level of the business system.

[0041] This invention provides an embodiment that acquires target vector drawing data including primitives and an associated mapping dataset of the target vector drawing data. It then performs adaptive matching on the primitives in the target vector drawing data and the associated mapping dataset to obtain an adaptive matching result. After obtaining the adaptive matching result, a bidirectional mapping relationship is established between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching result. This solution solves the problem of the lack of bidirectional association between images and data in existing technologies, enabling the establishment of a bidirectional mapping relationship between images and data, thereby improving the efficiency and accuracy of business system operation and maintenance management.

[0042] Example 2

[0043] Figure 2This is a flowchart of an image data mapping processing method provided in Embodiment 2 of the present invention. This embodiment is based on the above embodiment and is further specified. In this embodiment, specific optional implementation methods are given for establishing a bidirectional mapping relationship between primitives in the target vector drawing data and the associated mapping dataset based on adaptive matching results. Various specific optional implementation operations are also given after establishing the bidirectional mapping relationship between primitives in the target vector drawing data and the associated mapping dataset based on adaptive matching results. Correspondingly, as... Figure 2 As shown, the method in this embodiment may include:

[0044] S210. Obtain the target vector graphic data and the associated mapping dataset of the target vector graphic data.

[0045] S220. Perform adaptive matching between the primitives in the target vector drawing data and the associated mapping dataset to obtain an adaptive matching result.

[0046] In an optional embodiment of the present invention, the adaptive matching of the primitives in the target vector drawing data and the associated mapping dataset to obtain an adaptive matching result may include: extracting features from the primitives in the target vector drawing data to obtain primitive feature association data; extracting features from the associated mapping dataset to obtain device identifier location association data; and adaptively matching the primitive feature association data and the device identifier location association data to obtain an adaptive matching result between each primitive and the target data in the associated mapping dataset.

[0047] Specifically, the primitive feature association data can be a collection of primitive feature data extracted from the target vector drawing data. The device identification location association data can be a collection of association data obtained by extracting features from the association mapping dataset.

[0048] In this embodiment of the invention, when adaptively matching the primitives in the target vector drawing data and the associated mapping dataset to obtain the adaptive matching result, features can first be extracted from the primitives in the target vector drawing data to obtain primitive feature association data. Simultaneously, features can be extracted from the associated mapping dataset to obtain device identifier location association data. Furthermore, adaptive matching can be performed on the primitive feature association data and the device identifier location association data to obtain the adaptive matching result between each primitive and the target data in the associated mapping dataset.

[0049] S230. If the matching result of the first target graph element is successful, extract the first target business primary key that matches the first target graph element from the associated mapping dataset.

[0050] The first target graph element can be a graph element that successfully matches the target data in the associated mapping dataset. The first target business primary key can be a business primary key that matches the first target graph element.

[0051] Specifically, after adaptively matching the primitives in the target vector drawing data with the associated mapping dataset and obtaining the adaptive matching results, the primitives with successful matching results can be used as the first target primitives. At the same time, the first target business primary key that matches the first target primitive can be extracted from the associated mapping dataset.

[0052] S240. Determine the extended attribute method of the first target graphic element according to the device type of the first target graphic element.

[0053] One of the extended attribute methods is to add custom attribute data to graphic elements.

[0054] Specifically, the extended attribute method for the first target element can be determined based on its device type. In a concrete example, block references can preferentially use block attributes ATTDEF / ATTREF, all elements can use XData, complex data structures can use XRecord, and extended dictionaries can be used when multiple business primary keys need to be associated.

[0055] S250. Write the first target business primary key into the element extension attribute of the first target element according to the extended attribute method of the first target element.

[0056] Accordingly, after determining the extended attribute method of the first target graphic element, the first target business primary key can be written into the graphic extended attribute of the first target graphic element.

[0057] S260. Based on the extended attribute of the first target graphic element, establish a bidirectional association mapping relationship between the first target graphic element in the target vector drawing data and the first target mapping data corresponding to the first target business primary key.

[0058] Among them, the first target mapping data can be the data corresponding to the first target business primary key.

[0059] Correspondingly, after writing the first target business primary key into the element extension attribute of the first target element, a bidirectional association mapping relationship can be established between the first target element in the target vector drawing data and the first target mapping data corresponding to the first target business primary key, based on the element extension attribute of the first target element.

[0060] In an optional embodiment of the present invention, after establishing a bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching result, the method may further include: determining a second target primitive whose matching result is a failed match, and obtaining a primitive association data group for establishing the bidirectional mapping relationship; extracting spatial logical constraint information between each associated primitive in the primitive association data group; determining first spatial topology information of the first associated surrounding primitives of the second target primitive based on the target vector drawing data; determining second target mapping data corresponding to the second target primitive based on the first spatial topology information and the spatial logical constraint information; and establishing a bidirectional mapping relationship between the second target primitive and the second target mapping data.

[0061] The second target primitive can be a primitive that has not successfully matched the target data in the associated mapping dataset. The primitive association data group can be primitives and target mapping data with established bidirectional mapping relationships. Spatial logical constraint information can be the positional relationships, layout rules, geometric associations, and spatial behavior restrictions of primitives in space. The first associated surrounding primitives can be primitives surrounding the second target primitive with established bidirectional mapping relationships. The first spatial topology information can be the spatial location and topological relationship association information of the first associated surrounding primitives. The second target mapping data can be the target mapping data that matches the second target primitive.

[0062] In this embodiment of the invention, after establishing a bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching results, a second target primitive that failed to match can be identified, and the primitive associated data group with the established bidirectional mapping relationship can be extracted. Based on this, the spatial logical constraint information between each associated primitive in the primitive associated data group is extracted; simultaneously, based on the target vector drawing data, the position and topological relationship (i.e., the first spatial topological relationship) between the second target primitive and its surrounding first associated peripheral primitives is determined. Subsequently, combining the aforementioned first spatial topological relationship and spatial logical constraint information, second target mapping data that matches the second target primitive is selected, and the establishment of a bidirectional mapping relationship between the second target primitive and the second target mapping data is completed.

[0063] In an optional embodiment of the present invention, after establishing the bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching result, the method may further include: determining the target bidirectional mapping missing data in the associated mapping dataset; determining the second associated surrounding primitives in the target vector drawing data based on the coordinate information in the target bidirectional mapping missing data; extracting the second spatial topology information of the second associated surrounding primitives; determining the third target primitive corresponding to the target bidirectional mapping missing data based on the spatial topology information of the second associated surrounding primitives; and establishing the bidirectional mapping relationship between the target bidirectional mapping missing data and the third target primitive.

[0064] Specifically, the missing data in the target bidirectional mapping can be data in the associated mapping dataset for which no bidirectional mapping relationship has been established. The second associated surrounding primitives can be primitives in the coordinate location attachments of the missing target bidirectional mapping data that have established bidirectional mapping relationships. The second spatial topology information can be the spatial location and topological relationship association information of the second associated surrounding primitives. The third target primitive can be a primitive that matches the missing target bidirectional mapping data.

[0065] In this embodiment of the invention, after establishing a bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching result, unmatched target bidirectional mapping missing data in the associated mapping dataset can be identified. Subsequently, based on the coordinate information contained in the target bidirectional mapping missing data, second associated surrounding primitives near the target vector drawing data are retrieved. Further, second spatial topological information of the second associated surrounding primitives can be extracted, and a third target primitive corresponding to the target bidirectional mapping missing data can be determined based on the second spatial topological information. After identifying the third target primitive corresponding to the target bidirectional mapping missing data, a bidirectional mapping relationship between the two can be established.

[0066] In an optional embodiment of the present invention, after establishing a bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching result, the method may further include: generating a mapping relationship table based on the bidirectional mapping relationship between the target vector drawing data and the associated mapping dataset; querying the mapping relationship table based on the second target business primary key in the associated mapping dataset to determine the corresponding fourth target primitive in the target vector drawing data, and highlighting the fourth target primitive; or determining a third target business primary key corresponding to the fifth target primitive based on the primitive extended attribute of the fifth target primitive; querying the associated mapping dataset based on the third target business primary key to determine the third target mapping data corresponding to the fifth target primitive in the associated mapping dataset.

[0067] The mapping table can be a table used to record the bidirectional mapping relationship between the target vector drawing data and the associated mapping dataset. The second target business primary key can be a business primary key entered by the user. The fourth target graphic element can be a graphic element that matches the second target business primary key. The fifth target graphic element can be a graphic element selected by the user. The third target business primary key can be a business primary key that matches the fifth target graphic element. The third target mapping data can be the data in the associated mapping dataset corresponding to the third target business primary key.

[0068] In this embodiment of the invention, after establishing a bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching results, a mapping relationship table can be created based on the established bidirectional mapping relationship between the target vector drawing data and the associated mapping dataset. Further, when a user inputs a second target business primary key, the business system will query the aforementioned mapping relationship table based on the second target business primary key to accurately locate the corresponding fourth target primitive in the target vector drawing data, and perform a highlighting operation on the fourth target primitive in the target vector drawing data. In addition, the user can directly select a fifth target primitive in the target vector drawing data. The business system will determine its corresponding third target business primary key by reading the primitive extended attributes of the fifth target primitive; furthermore, using the third target business primary key as the retrieval basis, a query is performed in the associated mapping dataset to determine the third target mapping data corresponding to the fifth target primitive in the associated mapping dataset.

[0069] In an optional embodiment of the present invention, after establishing a bidirectional mapping relationship between the graphic elements in the target vector drawing data and the associated mapping dataset based on the adaptive matching result, the method may further include: obtaining real-time operational data of the physical equipment in the target vector drawing data; determining the target progress status of each graphic element in the target vector drawing data based on the mapping relationship table of the bidirectional mapping relationship and the real-time operational data of the physical equipment; and determining the target progress status of the associated graphic elements of the graphic elements based on the extended attributes of the graphic elements, the mapping relationship table, and the real-time operational data of the physical equipment.

[0070] Among them, the real-time operation data of physical equipment can be the real-time operation data of the physical equipment corresponding to each element in the target vector drawing data. The target progress status can be a quantitative description of the completion status and progress pace of a certain task. Associated elements can be elements that are related to this element.

[0071] In this embodiment of the invention, after establishing a bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching result, real-time operational data of the physical equipment in the target vector drawing data can be obtained. Subsequently, based on the mapping relationship table corresponding to the bidirectional mapping relationship and combined with the aforementioned real-time operational data of the physical equipment, the target progress status of the task corresponding to each primitive in the target vector drawing data can be accurately determined. Furthermore, the extended attributes of the primitives can be used in conjunction with the constructed mapping relationship table to locate and determine the associated primitives of the primitive. For example, associated primitives may include types such as superior primitives and subordinate primitives that have a hierarchical relationship with the primitive. Based on this, combined with the determined target progress status of the primitive and the real-time operational data of the physical equipment, the target progress status corresponding to the associated primitive can be deduced.

[0072] This invention provides an embodiment that acquires target vector drawing data including graphic elements and an associated mapping dataset of the target vector drawing data. It then performs adaptive matching on the graphic elements in the target vector drawing data and the associated mapping dataset to obtain an adaptive matching result. If the matching result for the first target graphic element is successful, a first target business primary key matching the first target graphic element is extracted from the associated mapping dataset. Simultaneously, the extended attribute method of the first target graphic element is determined based on its device type. Further, the first target business primary key is written into the graphic element extended attribute of the first target graphic element according to its extended attribute method. This establishes a bidirectional associated mapping relationship between the first target graphic element in the target vector drawing data and the first target mapping data corresponding to the first target business primary key. This solution addresses the problem of lack of bidirectional association between images and data in existing technologies, enabling the establishment of a bidirectional mapping relationship between images and data, thereby improving the efficiency and accuracy of business system operation and maintenance management.

[0073] Example 3

[0074] Figure 3 This is a flowchart of a specific image-based data matching method provided in Embodiment 3 of the present invention. To more clearly illustrate the technical solution provided by the embodiments of the present invention, the present invention provides a specific image-based data matching method, such as... Figure 3 As shown, the method includes the following operations:

[0075] S310. Identify the basic type of each graphic element in the target image data.

[0076] The target image data can be image data to be matched. For example, the target image data can include, but is not limited to, image data of railway catenary, urban rail transit catenary, trolleybus catenary, and image data of equipment composition structures. This embodiment of the invention does not limit the specific type of the target image data. It should be noted that the target image data can be in CAD format, SVG (Scalable Vector Graphics) format, EPS (Encapsulated PostScript) format, or JPG (Joint Photographic Experts Group) format. This embodiment of the invention does not limit the specific format type of the target image data. A primitive can be the smallest indivisible basic unit constituting the target image data. The primitive base type can be the type upon which the primitive is based. For example, the primitive base type can include, but is not limited to, point primitives, line primitives, region primitives, or component primitives. This embodiment of the invention does not limit the specific content of the primitive base type.

[0077] In this embodiment of the invention, the image data to be matched can be used as the target image data. To achieve image-based data matching, the basic primitive type of each primitive in the target image data can first be identified.

[0078] In a specific example, a standard library of primitive legends can be pre-built to provide a reference benchmark for the accurate identification of primitive basic types. When identifying primitive basic types, geometric features such as coordinate position, geometric shape, and size information of the target image data can first be extracted. Furthermore, the extracted geometric features can be compared with the pre-built standard library of primitive legends to calculate geometric similarity, thereby determining the primitive basic type of each primitive in the target image data based on the geometric similarity.

[0079] S320. Determine the data matching strategy for each graphic element based on the basic type of the graphic element.

[0080] Among them, the data matching strategy can be a matching method between the target image data and the data objects in the target dataset.

[0081] Correspondingly, after identifying the basic type of each graphic element in the target image data, the data matching strategy for each graphic element can be determined based on the basic type of the graphic element.

[0082] In an optional embodiment of the present invention, determining the data matching strategy for each of the graphic elements based on the graphic element basic type may include: when the graphic element basic type is determined to be a point graphic element, generating a data matching strategy for the point graphic element based on the point association attribute information of the point graphic element; wherein, the point association attribute information includes at least one of coordinates, layer name, block name, and attribute value; when the graphic element basic type is determined to be a line graphic element, generating a data matching strategy for the line graphic element based on the line association attribute information of the line graphic element; wherein, the line association attribute information includes... The data matching strategy for a region-like element is generated based on its region-like association attribute information, where the region-like association attribute information includes at least one of region boundary and topological inclusion relationship. When the basic type of the element is determined to be a component element, a data matching strategy for the component element is generated based on its component association attribute information, where the component association attribute information includes at least one of parent element topological relationship and spatial location.

[0083] In this invention, point-like primitives can be primitives in the target data image whose core shape is a discrete point. For example, point-like primitives can include, but are not limited to, pillars, etc. This embodiment of the invention does not limit the specific type of point-like primitives. Point-like associated attribute information can be the associated information of point-like primitives, such as, but not limited to, coordinates, layer names, block names, and attribute values. Linear primitives can be primitives with length attributes formed by a series of discrete points arranged according to a specific trajectory, such as, but not limited to, contact lines or catenary cables, etc. This embodiment of the invention does not limit the specific type of linear primitives. Linear associated attribute information can be the associated information of linear primitives, such as, but not limited to, endpoint coordinates, layer names, and primitive length, etc. Region-like primitives can be continuous regions with definite area attributes enclosed by one or more closed linear primitives, such as, but not limited to, anchor segments or insulation areas, etc. This embodiment of the invention does not limit the specific type of region-like primitives. Region-like associated attribute information can be the associated information of region-like primitives, such as, but not limited to, region boundaries and topological inclusion relationships, etc. Component elements can be composite elements used to characterize components or assemblies with specific functions, such as including but not limited to wrist arms, positioners, and insulators. This embodiment of the invention does not limit the specific type of component elements. Component association attribute information can be association information of component elements, such as including but not limited to parent element topology relationships and spatial locations.

[0084] In this embodiment of the invention, when determining the data matching strategy for each graphic element based on its basic graphic element type, if the basic graphic element type is a point graphic element, the data matching strategy for the point graphic element can be generated based on at least one of the coordinates, layer name, block name, and attribute value of the point graphic element; if the basic graphic element type is a line graphic element, the data matching strategy for the line graphic element can be generated based on at least one of the endpoint coordinates, layer name, and graphic element length of the line graphic element; if the basic graphic element type is a region graphic element, the data matching strategy for the region graphic element can be generated based on at least one of the region boundary and topological inclusion relationship of the region graphic element; if the basic graphic element type is a component graphic element, the data matching strategy for the component graphic element can be generated based on at least one of the parent graphic element topological relationship and spatial location of the component graphic element.

[0085] S330. Match each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result.

[0086] The target dataset can be a dataset to be matched with the target image data. The image data association matching result is obtained by matching the primitives in the target image data with the data objects in the target dataset using a data matching strategy.

[0087] Specifically, after determining the data matching strategy for each graphic element based on its basic type, the data matching strategy for each graphic element can be used to match each graphic element in the target image data with the data objects in the target dataset, determine the association relationship between each graphic element and the target data object in the target dataset, and use it as the association matching result of each image data.

[0088] In an optional embodiment of the present invention, if the basic type of the graphic element is a point graphic element, then the step of matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result may include: calculating the graphic element distance matching degree between the coordinates of the point graphic element and the coordinates of the point graphic element metadata object in the target dataset; calculating the matching degree between the layer name of the point graphic element and the first layer name of the device type of the point graphic element metadata object in the target dataset; calculating the matching degree between the block name of the point graphic element and the block name of the specification information of the point graphic element metadata object in the target dataset; calculating the matching degree between the attribute value of the point graphic element and the attribute value of the business primary key of the point graphic element metadata object in the target dataset; performing a weighted summation of the graphic element distance matching degree, the first layer name matching degree, the block name matching degree, and the attribute value matching degree, and determining the image data association matching result of the point graphic element based on the weighted summation result.

[0089] Specifically, the element distance matching degree can be the degree of matching between the coordinates of a point element and the coordinates of a point image metadata object in the target dataset. The first layer name matching degree can be the degree of matching between the layer name of a point element and the device type of a point image metadata object in the target dataset. The block name matching degree can be the degree of matching between the block name of a point element and the specification information of a point image metadata object in the target dataset. The attribute value matching degree can be the degree of matching between the attribute values ​​of a point element and the business primary key of a point image metadata object in the target dataset.

[0090] In this embodiment of the invention, if the basic type of the graphic element is a point graphic element, then when matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result, it is permissible to use a graphic element whose basic type is a point graphic element and the graphic element... Specific types and data objects When the device types are the same, the primitive distance matching degree between the coordinates of the point primitives and the coordinates of the point primitive data objects in the target dataset is calculated based on the following formula:

[0091] ;

[0092] The matching degree between the layer name of the point primitive and the first layer name of the device type of the point primitive metadata object in the target dataset can be calculated based on the following formula:

[0093] ;

[0094] The matching degree between the block name of the point primitive and the block name of the specification information of the point primitive metadata object in the target dataset can be calculated based on the following formula:

[0095] ;

[0096] The matching degree between the attribute values ​​of point primitives and the attribute values ​​of the business primary key of the point primitive metadata object in the target dataset can be calculated based on the following formula:

[0097] ;

[0098] Furthermore, the matching degree of primitive distance, the matching degree of first layer name, the matching degree of block name, and the matching degree of attribute value can be weighted and summed based on the following formula:

[0099] ;

[0100] in, For the i-th primitive, For the j-th data object in the target dataset, For point primitives, The distance matching degree of primitives. The Euclidean distance between the coordinates of point primitives and the coordinates of point primitive metadata objects in the target dataset. For example, the distance threshold for point primitives. It can be 5 meters, but this embodiment of the invention is not applicable. The specific values ​​to be taken are limited. For the first layer name matching degree, The layer name for the graphic element. For the device type of the point graph metadata object in the target dataset, For block name matching degree, The block name for point-like primitives. For the device model of the data object, For the device type of the data object, For attribute value matching degree, For the attribute values ​​of point primitives, The business primary key for the point graph metadata object. This is the weighted summation result of point primitives. Let be the matching degree of the k-th point primitive. This is the weight of the matching degree for the k-th point primitive. For example, The embodiments of the present invention do not The specific values ​​to be taken are limited.

[0101] In an optional embodiment of the present invention, if the basic type of the graphic element is a linear graphic element, then the step of matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result may include: calculating the endpoint coordinate distance matching degree between the endpoint coordinates of the linear graphic element and the endpoint coordinates of the linear graphic element data objects in the target dataset; calculating the length matching degree between the graphic element length of the linear graphic element and the graphic element length of the linear graphic element data objects in the target dataset; calculating the second layer name matching degree between the layer name of the linear graphic element and the device type of the linear graphic element data objects in the target dataset; performing a weighted summation of the endpoint coordinate distance matching degree, the length matching degree, and the second layer name matching degree, and determining the image data association matching result of the linear graphic element based on the weighted summation result.

[0102] Specifically, the endpoint coordinate distance matching degree can be the matching degree between the endpoint coordinates of the line primitive and the endpoint coordinates of the line primitive metadata object in the target dataset. The length matching degree can be the matching degree between the primitive length of the line primitive and the primitive length of the line primitive metadata object in the target dataset. The second layer name matching degree can be the matching degree between the layer name of the line primitive and the device type of the line primitive metadata object in the target dataset.

[0103] In this embodiment of the invention, if the basic type of the graphic element is a linear graphic element, then when matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result, it is permissible to use a graphic element whose basic type is a linear graphic element and whose graphic element type is a linear graphic element. Specific types and data objects When the device types are the same, the endpoint coordinate distance matching degree between the endpoint coordinates of the line primitives and the endpoint coordinates of the line primitive data objects in the target dataset is calculated based on the following formula:

[0104] ;

[0105] ;

[0106] ;

[0107] The length matching degree between the primitive length of a linear primitive and the primitive length of the linear primitive data object in the target dataset can be calculated based on the following formula:

[0108] ;

[0109] The matching degree between the layer name of the line primitive and the second layer name of the device type of the line primitive metadata object in the target dataset can be calculated based on the following formula:

[0110] ;

[0111] Furthermore, the endpoint coordinate distance matching degree, length matching degree, and second layer name matching degree can be weighted and summed based on the following formula:

[0112] ;

[0113] in, Linear primitives For endpoint coordinate distance matching degree, The distance matching degree between the starting coordinates of the line primitive and the starting coordinates of the line primitive metadata object in the target dataset. The distance matching degree between the endpoint coordinates of the line primitive and the endpoint coordinates of the line primitive metadata object in the target dataset. Let be the Euclidean distance between the starting coordinates of the i-th linear primitive and the starting coordinates of the j-th linear primitive data object in the target dataset. Let be the Euclidean distance between the endpoint coordinates of the i-th linear primitive and the endpoint coordinates of the j-th linear primitive data object in the target dataset. This refers to the distance threshold between the coordinates of each endpoint of a line primitive and the coordinates of each endpoint of the line primitive metadata object in the target dataset. For example, It can be 5 meters. For length matching degree, Let be the primitive length of the i-th linear primitive. Let the primitive length of the j-th line graph metadata object in the target dataset be . The threshold for the difference in element length, for example, It can be 10 meters. For the second layer name matching degree, Let i be the layer name of the i-th linear primitive. For the device type of the j-th line graph metadata object in the target dataset, This is the weighted summation result for the linear primitives. Let be the feature matching degree between the i-th linear primitive and the j-th linear primitive data object in the target dataset. for The weight. For example, The embodiments of the present invention do not The specific values ​​to be taken are limited.

[0114] In an optional embodiment of the present invention, if the basic type of the primitive is a region-shaped primitive, then the step of matching each primitive in the target image data and the data object in the target dataset according to the data matching strategy of each primitive to obtain the image data association matching result may include: calculating the region boundary overlap between the region boundary of the region-shaped primitive and the region boundary of the region-shaped primitive data object in the target dataset; calculating the topological inclusion relationship overlap between the topological inclusion relationship of the region-shaped primitive and the topological inclusion relationship of the region-shaped primitive data object in the target dataset; performing a weighted summation of the region boundary overlap and the topological inclusion relationship overlap, and determining the image data association matching result of the region-shaped primitive based on the weighted summation result.

[0115] Among them, the region boundary overlap can be the degree of overlap between the region boundary of a region-like primitive and the region boundary of the region-like primitive metadata object in the target dataset. The topological containment relationship overlap can be the degree of overlap between the topological containment relationship of a region-like primitive and the topological containment relationship of the region-like primitive metadata object in the target dataset.

[0116] In this embodiment of the invention, if the basic type of the graphic element is a region-shaped graphic element, then when matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result, it is permissible for the basic type of the graphic element to be a region-shaped graphic element, and the graphic element... Specific types and data objects When the device types are the same, the overlap between the region boundaries of the region primitives and the region boundaries of the region primitive data objects in the target dataset is calculated based on the following formula:

[0117] ;

[0118] The topological overlap between the topological containment relationships of region map elements and the topological containment relationships of region map metadata objects in the target dataset can be calculated based on the following formula:

[0119] ;

[0120] Furthermore, the overlap between region boundaries and the overlap of topological inclusion relationships can be weighted and summed based on the following formula:

[0121] ;

[0122] in, For region-shaped primitives, Region boundary overlap for The weight, for example, It can be 0.6, but this embodiment of the invention does not specify. The specific values ​​to be taken are limited. For the region boundary of the region-like primitive, For the region boundaries of the region map metadata objects in the target dataset, This is the area calculation function. This represents the overlap degree of the topological containment relationship. for The weight, for example, It can be 0.4, but this embodiment of the invention is not applicable. The specific values ​​to be taken are limited. Let i be the set of associated primitives included in the i-th region primitive. This is the weighted summation result of the region-like primitives.

[0123] In an optional embodiment of the present invention, if the basic type of the graphic element is a component graphic element, then the step of matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result may include: calculating the topological relationship matching degree between the parent graphic element topological relationship of the component graphic element and the parent graphic element topological relationship of the component graphic element data object in the target dataset; calculating the spatial position matching degree between the spatial position of the component graphic element and the spatial position of the component graphic element data object in the target dataset; performing a weighted summation of the topological relationship matching degree and the spatial position matching degree, and determining the image data association matching result of the component graphic element based on the weighted summation result.

[0124] The topological relationship matching degree can be the degree of matching between the topological relationship of the parent element of the component graphic element and the topological relationship of the parent element of the component graphic element data object in the target dataset. The spatial location matching degree can be the degree of matching between the spatial location of the component graphic element and the spatial location of the component graphic element data object in the target dataset.

[0125] In this embodiment of the invention, if the basic type of the graphic element is a component graphic element, then when matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result, it is permissible for the basic type of the graphic element to be a component graphic element, and the graphic element... Specific types and data objects When layer names are the same, the topological matching degree between the parent element topology of the component element and the parent element topology of the component element data object in the target dataset is calculated based on the following formula:

[0126] ;

[0127] The spatial location matching degree between the spatial location of a component element and the spatial location of the component element data object in the target dataset can be calculated based on the following formula:

[0128] ;

[0129] Furthermore, the topological relationship matching degree and spatial location matching degree can be weighted and summed based on the following formula:

[0130] ;

[0131] in, For component elements, For topological relationship matching degree, for The weight, for example, It can be 0.5, but this embodiment of the invention does not specify. The specific values ​​to be taken are limited. It is the parent element of the i-th component element. For the j-th component metadata object in the target dataset, This represents the connection relationship between the i-th component primitive and its parent primitive. For spatial location matching degree, for The weight, for example, It can be 0.5, but this embodiment of the invention does not specify. The specific values ​​to be taken are limited. It can be the spatial location of the i-th component element. This can be the spatial location of the j-th component metadata object in the target dataset. It can be a threshold for the spatial location difference. This is the weighted sum of the component elements.

[0132] Optional, if primitive Specific types and data objects If the layer names are different, no matching is required.

[0133] In an optional embodiment of the present invention, before matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result, the method may further include: adjusting the weight of each matching feature according to the information completeness of each graphic element in the target image data.

[0134] Among them, the information completeness of a graphic element can be the completeness of the various information included in the graphic element.

[0135] In this embodiment of the invention, before matching each element in the target image data with the data objects in the target dataset according to the data matching strategy of each element to obtain the image data association matching result, the weight of each matching feature in the matching process can be adjusted according to the information completeness of each element in the target image data.

[0136] In a specific example, the weights of each matching feature can be adjusted based on the following formula:

[0137] ;

[0138] in, The weights are adjusted for each matching feature. The basic weights for each matching feature, Let be the weight adjustment amount in the t-th iteration. The weight adjustment rule is:

[0139] ;

[0140] in, Let k be the weight of the matching feature k in the t-th iteration. To adjust the coefficients, and to meet the constraints. For example, when layer information is complete, layer information matching can be prioritized, increasing the weight of layer name matching; when annotation information is missing, the weight of coordinate matching and topological relationship matching can be automatically increased; when fuzzy matching exists, the best matching result can be automatically selected through multiple candidate matching and matching degree sorting.

[0141] In an optional embodiment of the present invention, after matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result, the method may further include: if it is determined that there are multiple data objects in the target dataset that match the graphic elements, filtering the target data objects as the image data association matching result according to the weighted summation result; and adjusting the association matching threshold according to the accuracy of the image data association matching result.

[0142] The target data object can be a specific data object. For example, the target data object can be the data object with the largest weighted sum, i.e., the data object with the highest matching degree. The association matching threshold can be a threshold used in the process of calculating each matching feature during the matching process.

[0143] In this embodiment of the invention, after matching each graphic element in the target image data with the data objects in the target dataset according to the data matching strategy of each graphic element to obtain the image data association matching result, if there are multiple data objects in the target dataset that match the graphic elements, the data objects can be filtered according to the weighted summation result of each data object, and the data object with the highest weighted summation result can be used as the target data object. Then, the target data object can be used as the image data association matching result of the graphic elements.

[0144] Furthermore, the thresholds used in calculating each matching feature during the matching process can be adjusted based on the accuracy of the image data association matching results.

[0145] In a specific example, the association matching threshold can be automatically adjusted based on the quality distribution of the matching results using the following formula:

[0146] ;

[0147] ;

[0148] in, The adjusted association matching threshold, Based on the basic association matching threshold, Let be the average matching degree of the (t-1)th iteration. Let be the set of matching results for the (t-1)th iteration. This is for adjusting the coefficient. For example, when the matching quality is generally high, the threshold can be appropriately increased to reduce false matches; when the matching quality is low, the threshold can be appropriately decreased to increase the matching coverage.

[0149] Therefore, the image-based data matching method provided in this embodiment of the invention determines the data matching strategy for each primitive according to the basic type of primitive, so that each data matching strategy can be accurately adapted to the core features of the corresponding primitive. It can flexibly cope with the primitive matching requirements of different complexity or different morphological features in different scenarios, thereby achieving accurate matching between images and target data and improving the matching efficiency between images and target data.

[0150] This invention identifies the basic type of each graphic element in the target image data and determines a data matching strategy for each graphic element based on its basic type. Furthermore, it matches each graphic element in the target image data with data objects in the target dataset according to the data matching strategy, obtaining an image data association matching result. This solution addresses the shortcomings of existing image data matching methods in terms of processing efficiency and matching accuracy, achieving accurate matching between images and target data while improving the matching efficiency.

[0151] Example 4

[0152] Figure 4 This is an architecture diagram of a railway catenary digital twin system provided in Embodiment 4 of the present invention. To more clearly illustrate the technical solution provided by the embodiments of the present invention, the present invention provides a railway catenary digital twin system, such as... Figure 4 As shown, the system includes the following modules: data storage module, feature extraction module, intelligent matching module, association establishment module, bidirectional query module, and digital twin visualization module. Among them:

[0153] (1) The data storage module can be used to store and manage various types of data. Specifically, the data storage module includes a CAD drawing storage unit, a ledger database unit, an operations database unit, a relational database unit, and an equipment legend library unit. Among them:

[0154] The CAD drawing storage unit can be used to store DWG (Drawing) format CAD drawing files, i.e., target vector drawing data, containing graphic elements and their extended attributes, and supporting functions such as reading, writing, and version management of CAD drawings. The data content in the CAD drawing storage unit may include, but is not limited to, CAD graphic elements, graphic element extended attributes, graphic element handles, and graphic element coordinate information. This embodiment of the invention does not limit the specific data content included in the CAD drawing storage unit.

[0155] The ledger database unit can be used to store ledger data for railway catenary equipment and components, i.e., a relational mapping dataset of target vector drawing data, whose core includes business primary keys and related attribute information. The ledger database unit can be constructed using relational databases such as MySQL or SQL Server. The table structure covered in the relational database can include, but is not limited to, equipment tables, component tables, section tables, and line tables. The data content of the ledger database unit can include, but is not limited to, business primary keys, equipment types, equipment models, location descriptions, and equipment installation dates. This embodiment of the invention does not limit the specific data content included in the ledger database unit.

[0156] The operational database unit can be used to store equipment operational data, such as maintenance progress data. The operational database unit can be constructed using a relational database, which may include, but is not limited to, mapping relationship tables. The data content of the operational database unit may include, but is not limited to, CAD element handles, business primary keys, element types, mapping types, creation times, update times, and matching degrees. This embodiment of the invention does not limit the specific data content included in the operational database unit.

[0157] The equipment legend library unit can be used to store a standard legend library for railway catenary equipment, covering core information such as geometric feature templates, size specifications, and shape patterns of various types of equipment. The equipment legend library unit can consist of a database or file system, and includes equipment legend tables and legend feature tables. Furthermore, the data content of the equipment legend library unit may include, but is not limited to, specific equipment types, legend geometric features, size specification ranges, and shape pattern encodings. This embodiment of the invention does not limit the specific data content included in the equipment legend library unit.

[0158] (2) The feature extraction module can be used to extract feature information from CAD elements and ledger records. Specifically, the feature extraction module includes a CAD element feature extraction unit and a ledger record feature extraction unit. Among them:

[0159] The CAD element feature extraction unit can automatically extract the geometric features, attribute features, and spatial topological relationships of CAD elements. This unit can include geometric feature extraction subunits, attribute feature extraction subunits, and position feature extraction subunits. The geometric feature extraction subunit can read standard legends for railway catenary equipment from the equipment legend library unit of the data storage module, or extract legend information from the legend descriptions in the drawings. Referring to the equipment legend library, it extracts the coordinate position, geometric shape, and dimension information of the elements. Furthermore, by comparing the geometric features of the elements with the legend features in the standard equipment legend library, geometric similarity is calculated to identify the specific type of equipment, improving the accuracy of equipment type identification.

[0160] In a specific example, the geometric similarity of each primitive can be calculated based on the following formula:

[0161] ;

[0162] ;

[0163] ;

[0164] ;

[0165] Furthermore, it can be related to primitives Legend template with the highest geometric similarity The corresponding primitive type is used as a primitive. primitive basic types .

[0166] Furthermore, the specific types of graphic elements can also be identified. Assuming the target image data is a CAD image of a railway catenary, then the specific types of graphic elements... This may include, but is not limited to, supports, contact wires, catenary wires, cantilever arms, positioners, and insulators, etc. This embodiment of the invention does not limit the specific type of the graphic element. The specific type identification function for the graphic element is:

[0167] ;

[0168] in, For the i-th primitive in the target image data, This is the k-th legend template in the standard legend library of graphic elements. Let be the geometric similarity between the i-th primitive and the k-th legend template. The shape similarity between the i-th primitive and the k-th legend template. for The weight, The size similarity between the i-th primitive and the k-th legend template. for The weight, Let be the pattern similarity between the i-th primitive and the k-th legend template, that is, the matching degree between the i-th primitive and the k-th legend template based on shape pattern encoding. for The weights. This is understandable. . Indicates the length or area of ​​a graphic element or legend template. The specific type identification result of the primitive. It is a collection of all legend templates in the standard legend library.

[0169] The attribute feature extraction subunit can be used to extract attribute features such as layer name, line type, color, block name, and existing attribute values ​​for each graphic element. The location feature extraction subunit can be used to extract the spatial positional relationship of graphic elements in the drawing, such as the distance to adjacent graphic elements and whether it is located in a specific area.

[0170] Furthermore, the features extracted by the CAD primitive feature extraction unit can be encoded into feature vectors and output to the intelligent matching module. For example, the feature vectors in this module may include, but are not limited to, coordinate vectors, layer codes, block name codes, attribute value codes, etc.

[0171] The ledger record feature extraction unit can be used to read ledger records from the ledger data unit of the data storage module and extract the features of the ledger records. Specifically, the ledger record feature extraction unit can include a business primary key extraction subunit, a location information extraction subunit, an attribute information extraction subunit, and a location coordinate parsing subunit. Specifically, the business primary key extraction subunit can be used to extract business primary keys from the ledger database, such as equipment codes, component codes, and unique asset numbers. The location information extraction subunit can be used to extract location description information from the ledger records, such as routes, sections, mileage, and coordinates. The attribute information extraction subunit can be used to extract attribute information such as equipment type, model, and installation date. The location coordinate parsing subunit can be used to parse the coordinate information in the ledger and convert it into a CAD coordinate system. If the ledger data only contains location descriptions, such as "Route 001, Section A, K100+200", it can be converted into a CAD coordinate system using a location parsing algorithm.

[0172] Furthermore, the features extracted by the ledger record feature extraction unit can be encoded into feature vectors and output to the intelligent matching module. These feature vectors can include, but are not limited to, business primary keys, coordinate vectors, device type codes, and attribute codes.

[0173] (3) The intelligent matching module can be used to automatically match CAD elements with ledger records. Specifically, the intelligent matching module includes a multi-feature fusion matching unit, a heuristic derivation unit, and an adaptive matching strategy unit. Among them:

[0174] The multi-feature fusion matching unit can be used to perform matching calculations based on multi-dimensional features. The heuristic derivation unit can be used to intelligently deduce possible matching information for unassociated devices by utilizing the topological relationships between successfully associated devices. The adaptive matching strategy unit can be used to automatically adjust the feature weights and matching thresholds of the matching algorithm according to the actual features of the CAD drawings.

[0175] Specifically, the heuristic derivation unit includes a topology relationship extraction subunit, an unassociated element derivation subunit, an unassociated ledger data derivation subunit, a topology consistency verification subunit, and a confidence assessment subunit. The topology relationship extraction subunit can extract topology relationships between devices based on successfully associated element-record pairs, such as spatial adjacency, connectivity, and topology pattern recognition. It can also automatically extract topology rules from associated devices, such as device spacing patterns, device type combination patterns, and line segment characteristics. The unassociated element derivation subunit can be used to find associated devices around unassociated CAD elements, calculate the distance and topology relationship between the unassociated element and its associated devices, analyze the ledger information of associated devices, extract business primary key rules, and derive possible business primary keys for unassociated elements based on topology relationships and business rules.

[0176] The unassociated ledger record derivation subunit can find associated graphic elements near the coordinates of unassociated ledger records, locate the corresponding area in the CAD drawing based on the location information of the ledger record, find associated graphic elements within that area, analyze their topological structure and spatial distribution, and derive candidate graphic elements corresponding to the unassociated records based on topological relationships and spatial locations. The topology consistency verification subunit can be used to verify whether the derived associations conform to the topology rules of the railway catenary. The confidence assessment subunit can be used to calculate the comprehensive confidence of each matching result according to the following formula:

[0177] ;

[0178] Among these, the topological confidence of the matching results can be calculated based on the number of associated devices and the strength of the topological relationship:

[0179] ;

[0180] ;

[0181] The rule matching degree can be calculated based on the primary key rule matching degree:

[0182] ;

[0183] The degree of matching can be calculated based on the degree of spatial location matching:

[0184] ;

[0185] in, The overall confidence level of the matching results. For topological confidence, for The weight, for example, It can be 0.4. For rule matching degree, for The rights, exemplarily, It can be 0.3. For location matching degree, for The weight, for example, It can be 0.3. For adjacency relation functions, For primitives and primitives The Euclidean distance between them This is a distance threshold between adjacent primitives, for example. It can be 100 meters. Let the distance between the i-th primitive and the j-th target data in the associated mapping dataset be Euclidean distance. This is the distance threshold.

[0186] If the overall confidence level Then the push result can be passed to the association establishment module; if If so, it can be marked as a "high-confidence candidate"; if If so, it can be marked as "awaiting manual confirmation".

[0187] The aforementioned modules execute a heuristic derivation algorithm based on topological relationships, leveraging the topological connections between successfully associated devices to intelligently deduce potential matching information for unassociated devices. This algorithm generates accurate candidate matching suggestions for unassociated elements or ledger records by deeply analyzing the spatial distribution patterns, topological connection modes, and association rules between device type and location of associated devices. Subsequently, through two key steps—topological consistency verification and confidence assessment—the association relationships between unassociated objects are automatically established, thereby significantly improving overall association coverage and matching accuracy.

[0188] (4) The association establishment module can be used to establish and maintain the association between CAD elements and ledger data. Specifically, the association establishment module includes an extended attribute writing unit and a mapping relationship management unit. Among them:

[0189] The extended attribute writing unit can include an extended attribute selection subunit, a business primary key encoding subunit, and an extended attribute read / write subunit. The extended attribute selection subunit can be used to select the most suitable extended attribute method based on the element type.

[0190] The business primary key coding subunit can extract business primary keys from the railway catenary equipment and component ledger database to generate standardized unique identifiers, ensuring the uniqueness, stability, and resolvability of the business primary keys. For example, business primary keys may include, but are not limited to, line-section-support number / code, equipment code, component code, unique asset number, etc.

[0191] The extended attribute read / write subunit can use the AutoCAD.NET API or ObjectARX to read and write extended attributes. Extended attributes can be read using the GetXData() or GetAttribute() method and written using the SetXData() or SetAttribute() method, and batch operations are supported.

[0192] The mapping relationship management unit can be used to establish and maintain the mapping relationship between unique identifiers of CAD elements and business primary keys in the ledger database. The mapping relationship management unit includes a mapping relationship table management sub-unit, a batch association establishment sub-unit, an incremental association establishment sub-unit, and an association verification and correction sub-unit. Among them:

[0193] The mapping relationship table management subunit can establish a mapping relationship table in the relational database, including fields such as CAD element handle, business primary key, element type, mapping type, creation time, and update time. For example, the mapping type can include, but is not limited to, one-to-one, one-to-many, many-to-one, and many-to-many.

[0194] Batch association to create sub-units can call an automated matching algorithm to perform batch matching of all graphic elements in CAD drawings and all records in the ledger database. For successfully matched graphic element-record pairs, the business primary key is automatically written into the extended attribute of the CAD graphic element, and the mapping record is automatically inserted into the associated database.

[0195] Incremental association for establishing sub-units can automatically extract features and match them with ledger records when new elements are added to CAD drawings to establish associations; it can also automatically extract features and match them with CAD elements when new records are added to the ledger database to establish associations; and it can automatically detect changes in CAD drawings and the ledger database to trigger incremental matching.

[0196] The association verification and correction subunit can automatically detect whether there are unassociated elements or records, automatically detect whether there are conflicting associations, provide correction suggestions for detected problems, and support automatic correction or manual confirmation.

[0197] The aforementioned module uses extended attributes of CAD elements to carry business primary keys, effectively avoiding the association failure problem caused by relying solely on volatile factors such as coordinates or layer names. Specifically, even if the target vector drawing undergoes version updates or coordinate changes, as long as the business primary key carried in the extended attributes of the elements remains unchanged, the association relationship between the elements and associated data can remain stable.

[0198] (5) The bidirectional query module can be used to implement bidirectional query functions between the database and drawings. Specifically, the bidirectional query module includes a database-to-drawing query unit, a drawing-to-database query unit, and a batch operation unit. Among them:

[0199] The database-to-drawing query unit can locate the corresponding graphic element in the CAD drawing based on the business primary key of the ledger database. Specifically, the business primary key query sub-unit in the database-to-drawing query can take the business primary key from the ledger database, query the mapping relationship table, and obtain the corresponding CAD graphic element handle; the graphic element location sub-unit can locate the graphic element in the CAD drawing based on the handle and highlight it; the batch query sub-unit can support batch queries and highlight multiple graphic elements at the same time.

[0200] The drawing-to-database query unit can read the business primary key from the CAD element extended attributes and query the ledger database to obtain equipment / component information. Specifically, the extended attribute reading sub-unit in the drawing-to-database query unit can read the business primary key from the CAD element extended attributes; the ledger data query sub-unit can query the railway catenary equipment and component ledger database according to the business primary key to obtain the corresponding equipment / component information; the data card display sub-unit can display the query results in data cards. For example, the query result can be "Section Name: LINE_001-SECTION_A, Total Equipment: 100, Completed: 80 (80%), In Progress: 15 (15%), Not Started: 5 (5%)".

[0201] The batch operation unit can be used to support batch selection of CAD elements to generate ledger comparison tables, or batch querying from the ledger and highlighting the corresponding elements in the CAD drawings. Specifically, the batch selection to generate comparison table subunit in the batch operation unit can batch select multiple elements in the CAD drawing, automatically read the business primary key from the element's extended attributes, query the ledger database based on the business primary key, and generate a ledger comparison table; the batch query to highlight subunit can batch query the business primary key from the ledger database, query the mapping relationship table based on the business primary key, obtain the corresponding CAD element handles, and batch highlight the corresponding elements in the CAD drawing.

[0202] The aforementioned modules support two core batch operation modes: first, batch selection of CAD elements and generation of a ledger comparison table; second, batch location of corresponding elements in CAD drawings based on ledger records. This design effectively reduces repetitive operations and significantly improves business processing efficiency. Simultaneously, the solution can automatically establish a mapping relationship table between elements and ledger records and continuously monitor and maintain the established relationships. Furthermore, this solution has incremental matching capabilities; when CAD drawings or ledger data are updated, the business system can automatically detect data changes and perform a re-matching operation to ensure the real-time nature and accuracy of the relationships. Through this fully automated association mechanism, the workload of manually maintaining relationships is significantly reduced, thereby effectively controlling overall maintenance costs.

[0203] (6) The digital twin visualization module can be used to realize multi-level visualization of real-time operational data of physical equipment. Specifically, the digital twin visualization module includes a multi-level progress display unit, a color mapping unit, and a dynamic update unit. Among them:

[0204] The multi-level progress display unit can be used to achieve multi-level visualization of maintenance progress at the line level (section maintenance progress), station level (single equipment maintenance progress), and equipment level (component maintenance progress). Specifically, the line-level display sub-unit can display the overall maintenance progress of the entire line or section, using the color of section elements to indicate overall completion. The station-level display sub-unit can display the maintenance status of each component of a single piece of equipment (such as a support pillar), using the color of equipment elements to indicate the equipment-level maintenance status. The equipment-level display sub-unit can display the maintenance progress status of each component within an equipment, using the color of component elements to indicate the specific status of each component. The hierarchical drill-down sub-unit allows clicking on a section at the line level to drill down to the station level, displaying the maintenance progress of all equipment within that section; clicking on an equipment at the station level drills down to the equipment level, displaying the maintenance progress of each component of that equipment. The hierarchical summary sub-unit allows summarizing the overall maintenance progress of the station area to which the equipment belongs at the equipment level; and summarizing the overall maintenance progress of the line area to which the station area belongs at the station level. The data card linkage sub-unit can be used to display detailed information about the selected level and its sub-levels when a certain level of graphic element is selected; it also supports clicking on a sub-level item in the data card to locate the corresponding graphic element.

[0205] Color mapping units can be used to classify and render maintenance progress status based on CAD primitives, layers, colors, line types, and dynamic block parameters, and to apply threshold coloring. The line-level color mapping subunit can query maintenance progress data for all equipment within a section and calculate the overall maintenance completion rate. For example, completion rate = number of completed devices / total number of devices; it can also determine colors based on completion rate, such as green for 100%, yellow for 0% < completion rate < 100%, gray for 0% completion rate, and red for overdue tasks. The station-level color mapping subunit can query the maintenance status of each component of equipment and determine the equipment color based on the status of major components or a weighted average. The equipment-level color mapping subunit can directly determine the color based on the maintenance progress status of each component, such as green for completed, yellow for in progress, gray for not started, and red for overdue. The color update subunit can be used to update primitive colors using API (Application Programming Interface), supporting three methods: layer color (batch color update), primitive color (single primitive color update), and dynamic block parameter color (dynamic color update).

[0206] In a specific example, line-level completion can be calculated based on the following formula:

[0207] ;

[0208] Furthermore, the line-level color mapping function can be:

[0209] ;

[0210] In a specific example, the color weight of station-level equipment can be determined based on the status of major components within the station area or a weighted average, using the following formula:

[0211] ;

[0212] Device-level color mapping functions can be:

[0213] ;

[0214] in, For line-level completion, To complete, In progress Not started. Overdue For the collection of equipment within the section, A collection of components within the equipment. For equipment or components, This refers to the maintenance status of the equipment or component. , For color mapping functions, A Boolean function for whether the segment contains the expected task. Let c be the weight of component c, satisfying , The color value for component c is, for example, Green for completed, Yellow for in progress, Gray for not started, and Red for overdue.

[0215] The dynamic update unit supports real-time data updates based on the operational database, enabling dynamic expression of maintenance progress status and timeline playback. The data acquisition subunit retrieves equipment maintenance progress data from the operational database, including line-level data (overall maintenance completion rate of all equipment within a section), station-level data (maintenance status of individual components within a single piece of equipment), and equipment-level data (maintenance progress status of individual components within the equipment). The real-time update subunit periodically retrieves the latest maintenance progress data from the operational database and dynamically updates the color attributes of CAD elements based on data changes. For section-level components, multiple element colors may need to be updated to form a regional color representation. It supports scheduled updates (periodically retrieving the latest data from the operational database and updating colors), event-driven updates (triggering color update events when operational data changes), and manual refresh (providing a manual refresh button for users to actively trigger color updates). The timeline playback subunit supports timeline playback, displaying the historical evolution of maintenance progress status. Users can select any point in time to view the maintenance progress status at that point, and it supports animation playback to dynamically display changes in maintenance progress over time.

[0216] The aforementioned system constructs a multi-level visualization system at the line, station, equipment, and component levels, enabling comprehensive visualization of line-level section maintenance progress, station-level single-equipment maintenance progress, and equipment-level component maintenance progress. This system possesses refined progress management capabilities from macro-level overview to micro-level details, accurately adapting to the differentiated needs of different management levels and helping maintenance personnel quickly and comprehensively grasp the real-time progress status of equipment maintenance.

[0217] At the visualization implementation level, the aforementioned system utilizes a standardized color mapping mechanism based on the layer attributes, color parameters, line type features, and dynamic block parameters of CAD elements. This enables precise classification and rendering of maintenance progress status, along with threshold-triggered coloring, making the equipment maintenance progress status intuitive, clear, and easy to understand. Simultaneously, the solution supports real-time data synchronization updates based on the operational database. It not only dynamically presents the maintenance progress status but also features a timeline playback function for the maintenance process, ensuring that all data displayed on the visualization interface remains up-to-date and accurate.

[0218] Furthermore, the aforementioned system deeply integrates CAD drawings, equipment ledger databases, and operational business data to construct a unified digital twin management platform. This system significantly reduces the cumbersome process of switching between multiple systems for maintenance personnel, substantially improving maintenance efficiency. Simultaneously, through a built-in two-way association mechanism, it enables rapid location and accurate querying of equipment information, effectively reducing the error rate during manual searches and saving substantial time.

[0219] In terms of automation performance, the system is equipped with intelligent matching algorithms and heuristic derivation algorithms, achieving a high degree of automation in establishing device information relationships. Practical application verification shows that the success rate of relationship establishment can reach over 95%, significantly reducing manual intervention and substantially improving the automation and intelligence level of operation and maintenance work.

[0220] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information in this technical solution comply with relevant laws and regulations and do not violate public order and good morals.

[0221] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant regions.

[0222] It should be noted that any arrangement or combination of the technical features in the above embodiments also falls within the protection scope of this invention.

[0223] Example 5

[0224] Figure 5 This is a schematic diagram of an image data mapping and processing device provided in Embodiment 5 of the present invention, as shown below. Figure 5 As shown, the device includes: a data acquisition module 510, a data acquisition module 520, and a test case generation module 530, wherein:

[0225] The data acquisition module 510 is used to acquire target vector graphic data and the associated mapping dataset of the target vector graphic data; wherein, the target vector graphic data includes primitives.

[0226] The adaptive matching module 520 is used to adaptively match the primitives in the target vector drawing data with the associated mapping dataset to obtain an adaptive matching result.

[0227] The bidirectional mapping relationship establishment module 530 is used to establish a bidirectional mapping relationship between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching result.

[0228] This invention provides an embodiment that acquires target vector drawing data including primitives and an associated mapping dataset of the target vector drawing data. It then performs adaptive matching on the primitives in the target vector drawing data and the associated mapping dataset to obtain an adaptive matching result. After obtaining the adaptive matching result, a bidirectional mapping relationship is established between the primitives in the target vector drawing data and the associated mapping dataset based on the adaptive matching result. This solution solves the problem of the lack of bidirectional association between images and data in existing technologies, enabling the establishment of a bidirectional mapping relationship between images and data, thereby improving the efficiency and accuracy of business system operation and maintenance management.

[0229] Optionally, the adaptive matching module 520 is specifically used for: extracting features from the primitives in the target vector drawing data to obtain primitive feature association data; extracting features from the association mapping dataset to obtain device identifier location association data; and adaptively matching the primitive feature association data and the device identifier location association data to obtain an adaptive matching result between each primitive and the target data in the association mapping dataset.

[0230] Optionally, the bidirectional mapping relationship establishment module 530 is specifically used for: when the matching result of the first target graphic element is determined to be successful, extracting the first target business primary key that matches the first target graphic element from the associated mapping dataset; determining the extended attribute method of the first target graphic element according to the device type of the first target graphic element; writing the first target business primary key into the graphic element extended attribute of the first target graphic element according to the extended attribute method of the first target graphic element; and establishing a bidirectional associated mapping relationship between the first target graphic element in the target vector drawing data and the first target mapping data corresponding to the first target business primary key according to the graphic element extended attribute of the first target graphic element.

[0231] Optionally, the above-mentioned device may further include an unassociated primitive derivation module, used to determine a second target primitive whose matching result is a failed match, and to obtain a primitive association data group for establishing the bidirectional mapping relationship; extract spatial logical constraint information between each associated primitive in the primitive association data group; determine the first spatial topology information of the first associated surrounding primitives of the second target primitive based on the target vector drawing data; determine the second target mapping data corresponding to the second target primitive based on the first spatial topology information and the spatial logical constraint information; and establish a bidirectional mapping relationship between the second target primitive and the second target mapping data.

[0232] Optionally, the above apparatus may further include an unassociated data derivation module, used to determine the target bidirectional mapping missing data in the associated mapping dataset; determine the second associated surrounding primitive in the target vector drawing data based on the coordinate information in the target bidirectional mapping missing data; extract the second spatial topology information of the second associated surrounding primitive; determine the third target primitive corresponding to the target bidirectional mapping missing data based on the spatial topology information of the second associated surrounding primitive; and establish a bidirectional mapping relationship between the target bidirectional mapping missing data and the third target primitive.

[0233] Optionally, the above apparatus may further include a bidirectional query module, used to generate a mapping relationship table based on the bidirectional mapping relationship between the target vector drawing data and the associated mapping dataset; query the mapping relationship table based on the second target business primary key in the associated mapping dataset to determine the corresponding fourth target graphic element in the target vector drawing data, and highlight the fourth target graphic element; or determine the third target business primary key corresponding to the fifth target graphic element based on the graphic element extension attribute of the fifth target graphic element; query the associated mapping dataset based on the third target business primary key to determine the third target mapping data corresponding to the fifth target graphic element in the associated mapping dataset.

[0234] Optionally, the above-mentioned device may further include a graphic element progress status query module, used to obtain real-time operating data of the physical equipment of the target vector drawing data; determine the target progress status of each graphic element in the target vector drawing data according to the mapping relationship table of the bidirectional mapping relationship and the real-time operating data of the physical equipment; and determine the target progress status of the associated graphic elements of the graphic element according to the extended attributes of the graphic element, the mapping relationship table and the real-time operating data of the physical equipment.

[0235] The image data mapping and processing apparatus described above can execute the image data mapping and processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the image data mapping and processing method provided in any embodiment of the present invention.

[0236] Since the image data mapping processing apparatus described above is an apparatus capable of executing the image data mapping processing method in the embodiments of the present invention, those skilled in the art can understand the specific implementation and various variations of the image data mapping processing apparatus in this embodiment based on the image data mapping processing method described in the embodiments of the present invention. Therefore, how the image data mapping processing apparatus implements the image data mapping processing method in the embodiments of the present invention will not be described in detail here. Any apparatus used by those skilled in the art to implement the image data mapping processing method in the embodiments of the present invention falls within the scope of protection of this application.

[0237] Example 6

[0238] Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0239] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0240] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0241] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as image data mapping processing methods.

[0242] In some embodiments, the image data mapping processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the image data mapping processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the image data mapping processing method by any other suitable means (e.g., by means of firmware).

[0243] Optionally, the image data mapping processing method may include: acquiring target vector graphic data and an associated mapping dataset of the target vector graphic data; wherein the target vector graphic data includes primitives; performing adaptive matching on the primitives in the target vector graphic data and the associated mapping dataset to obtain an adaptive matching result; and establishing a bidirectional mapping relationship between the primitives in the target vector graphic data and the associated mapping dataset based on the adaptive matching result.

[0244] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0245] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0246] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, inspection, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0247] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0248] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0249] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0250] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0251] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A mapping processing method of image data, characterized by, The method comprises the following steps: obtaining target vector drawing data and an associated mapping data set of the target vector drawing data; wherein the target vector drawing data comprises graphic elements; performing adaptive matching on the graphic elements in the target vector drawing data and the associated mapping data set to obtain an adaptive matching result; establishing a bidirectional mapping relationship between the graphic elements in the target vector drawing data and the associated mapping data set according to the adaptive matching result.

2. The method of claim 1, wherein, The adaptive matching on the graphic elements in the target vector drawing data and the associated mapping data set to obtain an adaptive matching result comprises the following steps: performing feature extraction on the graphic elements in the target vector drawing data to obtain graphic element feature association data; performing feature extraction on the associated mapping data set to obtain device identifier position association data; performing adaptive matching on the graphic element feature association data and the device identifier position association data to obtain an adaptive matching result between each of the graphic elements and target data in the associated mapping data set.

3. The method of claim 2, wherein, The establishment of a bidirectional mapping relationship between the graphic elements in the target vector drawing data and the associated mapping data set according to the adaptive matching result comprises the following steps: in a case where it is determined that the matching result of a first target graphic element is successful, extracting a first target business primary key matched with the first target graphic element from the associated mapping data set; determining an extension attribute mode of the first target graphic element according to the device type of the first target graphic element; writing the first target business primary key into the graphic element extension attribute of the first target graphic element according to the extension attribute mode of the first target graphic element; establishing a bidirectional association mapping relationship between the first target graphic element in the target vector drawing data and first target mapping data corresponding to the first target business primary key according to the graphic element extension attribute of the first target graphic element.

4. The method of claim 3, wherein, After the bidirectional mapping relationship between the graphic elements in the target vector drawing data and the associated mapping data set is established according to the adaptive matching result, the method further comprises the following steps: determining a second target graphic element with a matching failure and obtaining a graphic element association data group for establishing the bidirectional mapping relationship; extracting spatial logical constraint information between associated graphic elements in the graphic element association data group; determining first spatial topology information of a first associated peripheral graphic element of the second target graphic element according to the target vector drawing data; determining second target mapping data corresponding to the second target graphic element according to the first spatial topology information and the spatial logical constraint information; establishing a bidirectional mapping relationship between the second target graphic element and the second target mapping data.

5. The method of claim 3, wherein, After the bidirectional mapping relationship between the graphic elements in the target vector drawing data and the associated mapping data set is established according to the adaptive matching result, the method further comprises the following steps: determining target bidirectional mapping missing data in the associated mapping data set; determining a second associated peripheral graphic element in the target vector drawing data according to coordinate information in the target bidirectional mapping missing data; extracting second spatial topology information of the second associated peripheral graphic element; According to the spatial topological information of the second associated peripheral graph element, a third target graph element corresponding to the target bidirectional mapping missing data is determined; A bidirectional mapping relationship between the target bidirectional mapping missing data and the third target graph element is established.

6. The method of claim 1, wherein, After the bidirectional mapping relationship between the graph elements in the target vector drawing data and the associated mapping data set is established according to the adaptive matching result, the method further includes: A mapping relationship table is generated according to the bidirectional mapping relationship between the target vector drawing data and the associated mapping data set; According to the second target business primary key in the associated mapping data set, a fourth target graph element in the target vector drawing data corresponding to the fourth target graph element is determined, and the fourth target graph element is highlighted; or According to the graph element extension attribute of the fifth target graph element, a third target business primary key corresponding to the fifth target graph element is determined; According to the third target business primary key, the associated mapping data set is queried to determine a third target mapping data in the associated mapping data set corresponding to the fifth target graph element.

7. The method of claim 1, wherein, After the bidirectional mapping relationship between the graph elements in the target vector drawing data and the associated mapping data set is established according to the adaptive matching result, the method further includes: Real-time operation data of an entity device of the target vector drawing data is obtained; According to the mapping relationship table of the bidirectional mapping relationship and the real-time operation data of the entity device, a target progress state of each graph element in the target vector drawing data is determined; According to the extension attribute of the graph element, the mapping relationship table and the real-time operation data of the entity device, a target progress state of an associated graph element of the graph element is determined.

8. An electronic device, comprising: The electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the image data mapping processing method of any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the image data mapping processing method of any one of claims 1-7.

10. A computer program product, characterised in that, The computer program / instructions, when executed by the processor, implement the image data mapping processing method of any one of claims 1-7.

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