A road design index checking method and system

Abstract

The application discloses a road design index checking method and system, and relates to the technical field of engineering design, which comprises the following steps: obtaining new version design data and old version design data; analyzing the new version design data to generate the unique identification of a design object; according to the unique identification, performing aggregation processing on the numerical value change information between the new version design data and the old version design data to obtain aggregation change information; according to the aggregation change information, classifying the design object to obtain a target object, wherein the target object comprises a complex geometric object or a common numerical value object; if the target object is a complex geometric object, generating a change result of the feature parameters of an upper layer design geometric object according to the new version design data and the old version design data; and performing parameter comparison processing according to the change result and a lower layer design file to obtain a consistency comparison result. The application realizes road design index checking and improves the accuracy.

Description

Technical Field

[0001] This invention relates to the field of engineering design technology, and in particular to a method and system for checking road design indicators. Background Technology

[0002] Modern engineering design, especially large-scale infrastructure projects, widely relies on cloud-based collaborative work platforms. To efficiently manage multiple versions of design data and save storage space, these platforms often employ an incremental recording method for design data storage. When designers modify design documents and submit a new version, the platform only records the data segments that have changed compared to the previous version, rather than saving the entire file copy. When it is necessary to view or retrieve historical versions, the platform reconstructs the data in the background based on the base version and the incremental changes to present a complete and accessible dataset. However, this incremental recording mode may lead to the loss of shape information when dealing with complex design elements with inherent shape constraints.

[0003] Existing multi-version difference checking tools check for differences by directly comparing data values. However, when faced with implicit design differences that result from information loss or interpretation bias during data transfer and reconstruction, leading to inconsistencies in the final actual form, these tools can only report numerical changes in control point locations and cannot reveal implicit changes in the geometric properties of the complex geometric objects that form the basis of the calculations, resulting in low accuracy.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0005] The main objective of this invention is to propose a method and system for checking road design indicators, which can combine complex geometric object recognition to check road design indicators and improve accuracy.

[0006] On one hand, embodiments of the present invention provide a method for checking road design indicators, including the following steps:

[0007] Obtain design data for both the new and old versions;

[0008] The new version of the design data is parsed to generate a unique identifier for the design object;

[0009] Based on the unique identifier, the numerical change information between the new version design data and the old version design data is aggregated to obtain aggregated change information.

[0010] Based on the aggregated change information, the design objects are classified to obtain target objects, which include complex geometric objects or ordinary numerical objects.

[0011] If the target object is a complex geometric object, then based on the new version design data and the old version design data, the change result of the feature parameters of the upper-level design geometric object is generated;

[0012] Based on the changes and the lower-level design documents, parameter comparison processing is performed to obtain consistency comparison results.

[0013] In some embodiments, parsing the new version design data to generate a unique identifier for the design object includes:

[0014] The first design parameter of the new version design data is obtained, and the first design parameter includes the design tool type and data format;

[0015] Based on the first design parameters, the parser is used to extract the original geometric entities and associated attributes from the new version design data;

[0016] Based on the preset engineering object definition strategy, the original geometric entities and associated attributes are mapped to obtain a standardized set of engineering feature parameters.

[0017] Identify the design object based on the standardized set of engineering characteristic parameters;

[0018] Perform a geometric topology consistency check on the standardized engineering feature parameter set to obtain the geometric topology consistency check result;

[0019] Based on the geometric topology consistency verification result and the standardized engineering feature parameter set, a unique identifier for the design object is generated.

[0020] In some embodiments, the step of mapping the original geometric entities and associated attributes according to a preset engineering object definition strategy to obtain a standardized set of engineering feature parameters includes:

[0021] Select a target template from the preset engineering object definition strategy. The target template includes the geometric composition pattern of the design object, key attribute fields, logical constraints between attributes, and parameter mapping relationships.

[0022] Based on the target template, the original geometric entities and associated attributes are subjected to structured parsing and pattern matching to obtain a combination of geometric entities;

[0023] Based on the parameter mapping relationship in the target template, the original attribute values ​​in the geometric entity combination are converted into the standardized engineering feature parameter set.

[0024] In some embodiments, the step of aggregating the numerical change information between the new version design data and the old version design data based on the unique identifier to obtain aggregated change information includes:

[0025] Based on the unique identifier, the numerical change information is classified to obtain multiple design attribute categories;

[0026] Based on the priority of the design attributes, the multiple design attribute categories are sorted and aggregated to obtain the aggregated change information.

[0027] In some embodiments, classifying design objects based on the aggregated change information to obtain target objects includes:

[0028] Extract the internally defined attributes corresponding to each design object in the aggregated change information;

[0029] Based on the internally defined attributes, determine whether the design object contains geometric information, including geometric shape definition parameters or topological information of spatial connection relationships;

[0030] If the design object contains geometric information, then the target object is determined as the complex geometric object;

[0031] If the design object does not contain geometric information, then the target object is determined as the ordinary numerical object.

[0032] In some embodiments, generating the change results of the upper-level design geometric object feature parameters based on the new version design data and the old version design data includes:

[0033] Obtain the first design parameter of the new version design data and the second design parameter of the old version design data. The target design parameter includes the design tool type and data format. The target design parameter includes either the first design parameter or the second design parameter.

[0034] Based on the first design parameters, determine the first parsing configuration;

[0035] Based on the first parsing configuration, the new version design data is parsed to obtain the first original definition parameters;

[0036] Map the first original definition parameters to the first set of engineering feature parameters;

[0037] The first set of engineering feature parameters is validated to obtain the first feature parameters corresponding to the complex geometric objects in the new version of the design data.

[0038] Based on the second design parameters, determine the second parsing configuration;

[0039] Based on the second parsing configuration, the old version design data is parsed to obtain the second original definition parameters;

[0040] Map the second original definition parameters to the second set of engineering feature parameters;

[0041] The validity of the second set of engineering feature parameters is verified to obtain the second feature parameters corresponding to the complex geometric objects in the old version design data.

[0042] The first feature parameter and the second feature parameter are compared to obtain the change result.

[0043] In some embodiments, the step of validating the parameter validity of the first set of engineering feature parameters to obtain the first feature parameters corresponding to the complex geometric objects in the new version of the design data includes:

[0044] According to the preset inspection items, each parameter in the first set of engineering feature parameters is inspected to obtain the inspection results. The preset inspection items include parameter range, data type and geometric logical relationship defined by engineering specifications.

[0045] Geometric consistency verification is performed on the interrelated parameters in the first set of engineering feature parameters to obtain the geometric consistency verification results;

[0046] The first feature parameter is generated based on the test results and the geometric consistency verification results.

[0047] In some embodiments, the step of performing parameter comparison processing based on the change results and the lower-level design documents to obtain consistency comparison results includes:

[0048] Obtain the geometric representation of the lower-level design geometric objects in the lower-level design file;

[0049] The geometric analysis method is determined based on the type of the geometric representation.

[0050] The geometric representation is derived and fitted using the geometric analysis method to obtain the third set of engineering characteristic parameters.

[0051] The validity of the third set of engineering feature parameters is verified to obtain the third feature parameters corresponding to the lower-level design geometric object.

[0052] Extract the first feature parameter corresponding to the complex geometric object in the new version design data from the change results;

[0053] The consistency comparison between the third feature parameter and the first feature parameter is performed to obtain the consistency comparison result.

[0054] In some embodiments, after parsing the new version design data and generating a unique identifier for the design object, the method further includes:

[0055] The design object is structurally analyzed to obtain multiple sub-objects;

[0056] Based on the unique identifier, generate a sub-identifier corresponding to each sub-object;

[0057] When a sub-object is modified, the unique identifier is sent to the upper-level design platform, and the sub-identifier corresponding to the sub-object is sent to the lower-level design platform.

[0058] On the other hand, embodiments of the present invention provide a road design index inspection system, comprising:

[0059] The acquisition module is used to acquire design data for both new and old versions.

[0060] The parsing module is used to parse the new version design data and generate a unique identifier for the design object;

[0061] The aggregation module is used to aggregate the numerical change information between the new version design data and the old version design data based on the unique identifier to obtain aggregated change information.

[0062] The classification module is used to classify the design objects according to the aggregated change information to obtain target objects, including complex geometric objects or ordinary numerical objects.

[0063] The parameter change identification module is used to generate the change result of the feature parameters of the upper-level design geometric object based on the new version design data and the old version design data if the target object is a complex geometric object.

[0064] The consistency comparison module is used to perform parameter comparison processing based on the change results and the lower-level design documents to obtain the consistency comparison results.

[0065] The embodiments of this application include at least the following beneficial effects: First, the embodiments of this application acquire new version design data and old version design data. The new version design data is parsed to generate a unique identifier for the design object. Then, based on the unique identifier, the numerical change information between the new version design data and the old version design data is aggregated to obtain aggregated change information. Based on the aggregated change information, the design object is classified to obtain the target object. If the target object is a complex geometric object, the change result of the feature parameters of the upper-level design geometric object is generated based on the new version design data and the old version design data. Finally, the parameter comparison processing is performed based on the change result and the lower-level design file to obtain the consistency comparison result. Thus, the road design index check can be realized by combining the identification of complex geometric objects, thereby improving the accuracy.

[0066] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This is a flowchart of a road design index checking method according to an embodiment of the present invention;

[0069] Figure 2 This is a schematic diagram of a road design index inspection system according to an embodiment of the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements.

[0071] In related technologies, traditional cloud-based collaborative design platforms generally employ incremental recording storage methods when processing design data for large infrastructure projects to manage multiple versions of design data and save storage space. However, when designers modify complex geometric objects with inherent shape constraints and submit new versions, the platform only records numerical changes, neglecting the inherent shape definition logic that defines the geometry of these objects. This approach leads to subtle shape differences that deviate from the original design intent when downstream disciplines (lower-level designers) reference and recreate these complex geometric objects, due to their default creation methods. These differences are not simple numerical errors and therefore cannot be accurately detected by conventional numerical comparison methods, thus affecting the integrity and consistency of the design data.

[0072] For example, suppose a major transportation hub expansion project has an elevated expressway connecting the old and new urban areas as its core component. The design work is conducted by multiple professional teams specializing in routes, structures, and drainage on a unified cloud-based collaborative platform. The platform's underlying data version management mechanism uses an incremental storage model. When the route design team optimizes a key ramp connection, the road centerline at this point is a complex transition curve determined by a series of precisely distributed control points in space, which have inherent shape constraints. The route designer adjusts the positions of several control points and submits a new version. At this point, the platform's incremental recording unit only identifies and saves the numerical changes in the three-dimensional position information of a few control points, but fails to understand the changes in the specific shape model of the "transition curve" and its inherent shape constraint logic behind these points. Therefore, the platform only saves the numerical changes of the points, neglecting the inherent shape constraint logic that defines how these points collectively form a smooth curve.

[0073] Meanwhile, the drainage system design team's work relied on the precise geometry of this ramp curve. They accessed the latest version of data submitted by the route team. Because the data packets transmitted by the platform only contained updated control point location information and lacked specific geometric constraints, the drainage design software couldn't determine which transition curve model was initially used to generate these points. Faced with discrete points, the software initiated a default general curve fitting method, such as cubic spline curve fitting, to regenerate a curve passing through all the points. This newly generated curve precisely matched all the control points, but in the sections between the control points, its local curvature and tangent orientation showed slight deviations from the original curve seen by the route designers in the specialized software.

[0074] The project entered the review phase, and the project manager initiated a multi-version difference check. The checking tool compared the data from the two versions one by one. When comparing the route design indicators, it reported changes in the values ​​of several control point location information. When comparing the drainage design indicators, the drainage team's internal calculation logic was consistent, and no logical contradictions or numerical anomalies were found between the old and new versions of the drainage design indicators. The core logic of the checking tool is to compare direct data values, and it cannot identify implicit changes in the geometric properties of the curves that form the basis of the calculations. The final check report showed "Differences have been verified, no conflicts."

[0075] Based on this seemingly flawless inspection report, the design scheme passed multiple rounds of review smoothly. However, towards the end of the project, during a high-precision Building Information Modeling (BIM) overall professional conflict check, the system issued an alert. The model showed that in several specific sections of the ramp, the lowest point of the road slope and the bottom of the drainage ditch had a physical conflict, with the height difference less than the minimum safe distance, and even intersections at some points. The final result of tracing the problem was unexpected: the root cause lay in the inconsistency in the actual form of the design basis curves used by the route and drainage professionals during the data transfer and reconstruction process on the cloud platform. This major difference, because it did not exist in the form of a simple numerical error, successfully bypassed the multi-version discrepancy check method most relied upon in the project management process.

[0076] If the aforementioned issues are not addressed, these implicit design differences, stemming from information loss or interpretation biases during data transmission and reconstruction, and resulting in inconsistencies in the final actual form, will remain undetected by conventional numerical comparison methods. This directly leads to potential geometric inconsistencies between different disciplines in the design scheme, which may only be discovered later in the project, such as during overall professional conflict checks in high-precision Building Information Modeling (BIM). This delayed discovery can trigger physical conflicts, such as conflicts between road slopes and drainage ditches, leading to design rework, construction delays, and increased project costs. More seriously, these unidentified geometric inconsistencies can affect the structural integrity, functionality, and long-term operational safety of the project, negatively impacting project quality and user experience. Therefore, in-depth analysis and comparison of the geometric definitions and inherent constraints behind the design data are crucial for accurately identifying and revealing these implicit design differences, ensuring the quality of engineering design and the smooth implementation of the project.

[0077] Faced with the aforementioned problems, this application initially considered simply increasing data storage to save all versions of complete design data, thus avoiding information loss due to incremental storage. However, this method significantly increases the storage costs and data transmission bandwidth requirements of the cloud platform. For large and complex projects, the resource consumption is enormous, and the efficiency of version comparison also decreases drastically when the data volume is large. Therefore, this application further considers that the problem lies in the loss of the inherent shape definition logic of complex geometric objects. Thus, it can attempt to perform deeper feature extraction and comparison on these complex objects during data parsing and comparison. Initial attempts may be limited to comparing the vertex coordinates of geometric objects, but this still cannot capture changes in higher-order geometric features such as curve or surface types. Further consideration is the need for a mechanism to identify and classify design objects, distinguishing those with complex geometric attributes. For these complex geometric objects, it is not enough to simply compare the original values; instead, the feature parameters of their upper-level design geometric objects should be extracted and compared, such as the type of curve, the topological relationship between control points, and parametric definitions. Simultaneously, to verify the impact of these upper-level design changes on downstream disciplines, it is necessary to compare the results of these changes with the parameters of the lower-level design documents. This hierarchical, categorized, and characteristic-based comparison approach can more accurately identify implicit geometric differences, thereby avoiding the limitations of traditional numerical comparisons. In this way, even if the lower-level data is stored incrementally, its high-order geometric features can be reconstructed and analyzed during comparison, ensuring design consistency.

[0078] In view of this, the embodiments of this application introduce a unique identifier for the design object and classify the design object into complex geometric objects and ordinary numerical objects based on aggregated change information. Then, for complex geometric objects, the change results of the feature parameters of the upper-level design geometric objects are generated in depth and compared with the parameters of the lower-level design files. This solves the problem that the traditional incremental storage mode cannot accurately identify and verify the changes in the internal shape definition logic of complex geometric objects, and achieves the effect of fully revealing the implicit design differences and ensuring the consistency of multi-disciplinary collaborative design.

[0079] The embodiments of this application will be explained in detail below with reference to the accompanying drawings:

[0080] Figure 1 This is an optional flowchart of a road design index checking method provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0081] Step S101: Obtain the design data for the new version and the design data for the old version;

[0082] Step S102: Parse the new version design data and generate a unique identifier for the design object;

[0083] Step S103: Based on the unique identifier, aggregate the numerical change information between the new version design data and the old version design data to obtain aggregated change information.

[0084] Step S104: Based on the aggregated change information, classify the design objects to obtain the target objects, which include complex geometric objects or ordinary numerical objects.

[0085] Step S105: If the target object is a complex geometric object, then generate the change results of the feature parameters of the upper-level design geometric object based on the new version design data and the old version design data.

[0086] Step S106: Based on the change results and the lower-level design documents, perform parameter comparison processing to obtain consistency comparison results.

[0087] Steps S101 to S106 shown in the embodiments of this application can combine complex geometric object recognition to realize road design index checking, thereby improving accuracy.

[0088] In some embodiments, steps S101-S106 can first acquire new version design data and old version design data, parse the new version design data, and generate a unique identifier for the design object. The introduction of this identifier allows for accurate tracking and association of the design object's identity across different versions, even if the specific numerical values ​​or form of the design object change, thus overcoming the fragmentation problem of change information caused by the ambiguity of object identity in traditional methods. Then, based on the unique identifier, the numerical change information between the new version design data and the old version design data is aggregated to obtain aggregated change information. This aggregation process not only summarizes the change content but, more importantly, provides clear input for subsequent intelligent classification. Based on the aggregated change information, the design objects are then classified to obtain target objects, which include complex geometric objects or ordinary numerical objects. This classification can identify complex geometric objects with inherent shape constraints whose changes may have profound effects, distinguishing them from ordinary objects involving only simple numerical changes, thereby adopting differentiated processing paths for different types of objects.

[0089] If the target object is a complex geometric object, the changes to the feature parameters of the upper-level design geometric object are generated based on the new and old version design data. This means that instead of focusing solely on changes in surface values, it is possible to understand and extract the key feature parameters that define the intrinsic form and logic of complex geometric objects, and accurately compare the actual changes of these parameters between the old and new versions. Through in-depth analysis of geometric feature parameters, subtle geometric differences caused by the loss of intrinsic shape logic, which are undetectable by traditional methods, can be revealed. Finally, parameter comparison processing is performed based on the change results and the lower-level design documents to obtain consistency comparison results. This embodiment cross-validates the actual geometric changes of the upper-level design with the dependencies of the lower-level design, enabling timely detection and early warning of implicit design conflicts that are inconsistent in the final entity form due to data transmission or interpretation deviations, ensuring the integrity and accuracy of the entire collaborative design chain.

[0090] Understandably, numerical change information refers to the quantitative differences between the new and old versions of design data in specific design attributes or parameters. This can manifest as changes in coordinate values, adjustments to dimensional parameters, updates to attribute fields, or other quantifiable data variations. Its primary purpose is to initially record the original data traces of design changes. Aggregated change information refers to a data set formed by classifying, summarizing, and structuring multiple discrete numerical change information sets according to the unique identifier of the design object and preset aggregation rules. This can be implemented using change lists, change reports, difference matrices, or other structured data formats. Its main purpose is to provide a comprehensive and organized overview of design changes, facilitating subsequent classification and analysis. Complex geometric objects refer to design entities in engineering design whose geometric forms are not only defined by simple numerical parameters but also include inherent shape constraints, topological connection logic, or complex geometric configuration patterns. Examples include road transition curves, bridge structural components, and tunnel lining sections. This is primarily to distinguish ordinary objects defined only by independent numerical values ​​and to specifically address their inherent complexity. Ordinary numerical objects refer to design entities in engineering design whose attributes or forms are mainly defined by independent numerical parameters that do not involve complex geometric constraints. Examples include road width, speed limit, and material strength. Their primary purpose is to distinguish them from complex geometric objects and to employ different processing paths. Lower-level design documents refer to downstream professional design documents that rely on the geometric form or parameters of upper-level designs (especially complex geometric objects) for their own design and calculations within a collaborative design process. Examples include drainage design documents, structural reinforcement drawings, and pipeline layout drawings. These can be implemented using CAD drawings, BIM models, calculation sheets, or other professional design data formats. Their main purpose is to serve as a basis for verifying the impact of upper-level design changes on downstream processes.

[0091] To illustrate this technical solution more clearly, a specific example is used below. First, the latest version of the design data for a specific road project and its corresponding previous version are obtained from a cloud-based collaborative design platform. This data can be BIM model files stored in IFC format or CAD drawing files stored in DWG format. Next, the obtained new version of the IFC model data is parsed. A data parsing engine traverses all design entities in the model and generates a globally unique identifier for each entity (e.g., a road centerline, a bridge pier, a drainage ditch), serving as its unique identifier throughout the entire design lifecycle. Subsequently, using these identifiers, the attributes and geometric information of all design entities in the old and new versions of the IFC model data are compared to identify all data points with numerical changes. These discrete numerical change information are then aggregated. For example, changes in the coordinates of all control points and superelevation parameters of the same road centerline are grouped under the identifier of that centerline, forming a structured aggregated change information report. Based on this aggregated change information, each design object in the report can be categorized according to a pre-defined rule base. For example, if a design object is identified as a "road centerline" or a "bridge main beam," it is classified as a complex geometric object because these objects typically contain complex geometric definitions and inherent topological relationships. Conversely, if a design object is identified as a "road width" or a "speed limit sign," it is classified as a common numerical object. When a target object is determined to be a complex geometric object, such as a modified road transition curve, it uses specialized geometric analysis algorithms based on both new and old versions of the IFC model data to deduce the characteristic parameters defining the transition curve from the original geometric entity. These parameters include the starting and ending coordinates, tangent direction, rate of curvature, and superelevation. The specific numerical differences of these characteristic parameters between the old and new versions can be precisely compared, generating a change result for the characteristic parameters of the upper-level design geometric object. Finally, this change result, such as the actual geometric shape change of the transition curve, is compared with the parameters of the lower-level design file (e.g., the drainage ditch longitudinal slope design file generated by the drainage professionals based on the road centerline). This may involve reverse-analyzing the road geometry information referenced in the drainage ditch design documents and verifying its geometric consistency with the transition curve characteristic parameters derived from the upper-level road design. The result is a detailed consistency comparison report indicating whether there are any geometric conflicts or inconsistencies.

[0092] Through the above technical solution, this embodiment effectively solves the problem in road engineering design where, when a cloud-based collaborative platform uses an incremental storage mechanism to process modifications of complex geometric objects, it only records numerical changes and misses the underlying shape definition logic. This leads to subtle shape differences that do not conform to the original design intent when lower-level designs reference and recreate objects, and these differences cannot be detected by conventional numerical comparison methods. Specifically, by generating unique identifiers for design objects, accurate tracking of design entities is ensured in multi-version data comparison; by classifying design objects, specialized and in-depth geometric feature parameter analysis can be performed on complex geometric objects, thereby revealing the actual changes in their underlying shape definition logic; by comparing the feature parameter change results of upper-level design geometric objects with those of lower-level design documents, implicit design conflicts caused by geometric inconsistencies that cannot be detected by traditional numerical comparison methods can be accurately identified and warned of. This significantly improves the accuracy and reliability of design change checks in a multi-disciplinary collaborative design environment, avoiding potential risks and rework in later engineering implementation.

[0093] In some embodiments, step S102, parsing the new version design data and generating a unique identifier for the design object, may include, but is not limited to, the following steps:

[0094] Step S201: Obtain the first design parameters of the new version design data. The first design parameters include the design tool type and data format.

[0095] Step S202: Based on the first design parameters, use the parser to extract the original geometric entities and associated attributes from the new version design data;

[0096] Step S203: According to the preset engineering object definition strategy, the original geometric entities and associated attributes are mapped to obtain a standardized set of engineering feature parameters;

[0097] Step S204: Identify the design object based on the standardized set of engineering characteristic parameters;

[0098] Step S205: Perform geometric topology consistency verification on the standardized engineering feature parameter set to obtain the geometric topology consistency verification result;

[0099] Step S206: Generate a unique identifier for the design object based on the geometric topology consistency verification results and the standardized engineering feature parameter set.

[0100] In some embodiments, simply generating identifiers based on raw data lacks in-depth analysis and structured processing of the design data itself. This is especially problematic when the design data is complex and diverse in format, potentially leading to comparison discrepancies due to parsing errors or identifier conflicts. This compromises the accuracy and uniqueness of the identifiers, affecting the reliability of subsequent aggregation of change information. Therefore, accurate and comprehensive parsing of the new version design data is necessary. This can be achieved by first obtaining the first design parameters of the new version design data, including the design tool type and data format. Then, based on these first design parameters, a parser extracts the original geometric entities and associated attributes from the new version design data. To achieve a unified representation of design objects under different design tools and data formats, a pre-defined engineering object definition strategy can be used to map the original geometric entities and associated attributes, resulting in a standardized set of engineering feature parameters. This standardization process is crucial for eliminating data heterogeneity and ensuring the accuracy of subsequent comparisons, enabling the identification of specific design objects and clarifying their types and characteristics. The standardized set of engineering feature parameters is then used to identify the design objects. To ensure the intrinsic quality and reliability of the design data, a geometric topology consistency check can be performed on the standardized set of engineering feature parameters to obtain the geometric topology consistency check result. This verification process can detect potential geometric or topological errors in the design data, such as self-intersections, gaps, or incorrect connections, thus obtaining a geometric topological consistency verification result. Finally, based on the geometric topological consistency verification result and the standardized engineering characteristic parameter set, a unique identifier for the design object is generated. This unique identifier not only contains the design object's type, characteristics, and standardized parameter information, but also incorporates its geometric topological consistency status, enabling the identifier to more comprehensively and accurately reflect the true form and quality of the design object.

[0101] Understandably, a pre-defined engineering object definition strategy refers to a set of pre-defined rules, templates, or data structures used to standardize and guide the conversion of design data from different sources and formats into a unified, standardized representation of engineering objects. This can be implemented using XML / JSON-based configuration files, database schema definitions, or class definitions in programming languages. Its purpose is to provide a basis for the standardized processing of design data. A standardized set of engineering characteristic parameters refers to a set of parameters that, after standardization, can accurately and completely describe the geometric, topological, and non-geometric attributes of a design object. This can be represented using structured data tables, object-oriented attribute sets, or node attributes in a graph database. Its purpose is to eliminate differences caused by different design tools and data formats, facilitating subsequent identification, comparison, and analysis.

[0102] To illustrate this technical solution more clearly, a specific example is used below. First, the initial design parameters of the new version of the design data can be obtained. For example, when a new road design file is received, the system can identify that the file was generated by AutoCAD Civil 3D software and that its data format is DWG. This information constitutes the initial design parameters. Next, based on the identified design tool type (e.g., AutoCAD Civil 3D) and data format (e.g., DWG), a parser specifically for the DWG format can be invoked. This parser can read and extract the original geometric entities that constitute the road design object from the DWG file, such as points, line segments, arcs, splines, etc., as well as their associated attributes, such as layer information, color, line type, elevation value, and material properties. Then, according to a preset engineering object definition strategy, these extracted original geometric entities and associated attributes can be mapped. For example, the preset engineering object definition strategy can include an object definition for a "road centerline," which specifies that the road centerline should consist of a series of line segments or curves with specific topological relationships and includes key attributes such as "design speed" and "curve radius." The system can combine the extracted original line segments and curves, and map their attributes such as elevation and length to a standardized set of engineering characteristic parameters for the "road centerline".

[0103] After obtaining a standardized set of engineering characteristic parameters, the design object can be identified based on these parameters. For example, if the standardized parameter set contains the geometric composition pattern and key attributes of a "road centerline," the object being processed can be identified as a road centerline. If the parameter set conforms to the definition of a "bridge structure," it is identified as a bridge. Furthermore, the standardized engineering characteristic parameter set can be checked for geometric and topological consistency. For example, for an identified road centerline, it can be checked whether there are breaks, overlaps, or self-intersections between its constituent line segments or curves. For closed geometries, it can be checked whether their normal directions are consistent or whether there are unclosed boundaries. The verification results can indicate whether the design object is geometrically and topologically complete and correct.

[0104] Finally, a unique identifier for the design object can be generated based on the geometric topology consistency verification results and the standardized engineering feature parameter set. For example, if a road centerline passes the geometric topology consistency verification, and its standardized engineering feature parameter set includes its type, key geometric parameters (such as start coordinates, end coordinates, curve type, radius, etc.) and non-geometric attributes (such as design name, contract section), the system can use this information, combined with a hash algorithm or a unique serial number generator, to generate a unique string as the identifier for the road centerline. This identifier can be a combined string containing the object type, key parameter hash values, and verification status, such as "RoadCenterline_XYZ_CurveType_Validated_HashValue".

[0105] Through the above technical solution, this embodiment can accurately and comprehensively parse new version design data and generate an identifier that uniquely represents the design object. By acquiring design parameters, extracting original entities, mapping them to standardized parameters, and performing geometric topology consistency verification, it ensures that design data obtained from different design tools and data formats can be processed uniformly and in a standardized manner, and corrects potential geometric topology errors. This ensures that the generated unique identifier not only accurately reflects the type and characteristics of the design object, but also contains its inherent geometric topology consistency information, thereby effectively avoiding comparison deviations caused by inaccurate parsing or identifier conflicts. This provides a reliable foundation for subsequent accurate version comparison and change analysis, improving the accuracy and efficiency of design data management.

[0106] In some embodiments, in step S203, the original geometric entities and associated attributes are mapped according to a preset engineering object definition strategy to obtain a standardized set of engineering feature parameters, which may include, but is not limited to, the following steps:

[0107] Select a target template from the preset engineering object definition strategy. The target template includes the geometric composition pattern of the design object, key attribute fields, logical constraints between attributes, and parameter mapping relationships.

[0108] Based on the target template, the original geometric entities and associated attributes are subjected to structured parsing and pattern matching to obtain a combination of geometric entities;

[0109] Based on the parameter mapping relationship in the target template, the original attribute values ​​in the geometric entity combination are converted into a standardized set of engineering feature parameters.

[0110] In some embodiments, since different design objects may have different geometric composition patterns, key attribute fields, logical constraints between attributes, and parameter mapping relationships, if a uniform mapping processing method is used, the accuracy and completeness of the mapping cannot be guaranteed. This may result in the generated standardized engineering feature parameter set failing to accurately reflect the characteristics of the design object, thereby affecting subsequent design object identification and numerical change information aggregation processing. Mapping the original geometric entities and associated attributes can begin by selecting a target template from a preset engineering object definition strategy. This target template includes the geometric composition pattern, key attribute fields, logical constraints between attributes, and parameter mapping relationships of the design object. This target template predefines how to understand and process the structure and attributes of a specific design object, avoiding deviations that may occur when using general rules. It can be understood that a target template refers to a predefined set of data structures and transformation rules for a specific type of design object; specifically, it can be an XML file, a JSON configuration file, or a record in a database. Its purpose is to provide customized guidance for the standardized mapping of different design objects.

[0111] Then, based on the target template, the original geometric entities and their associated attributes are subjected to structured parsing and pattern matching to obtain a geometric entity combination. This process decomposes and identifies the original, potentially unstructured design data according to the preset geometric composition patterns in the target template. Through structured parsing, the original geometric entities are broken down into identifiable components; through pattern matching, these components are assigned specific roles and relationships within the design object, thus forming a geometric entity combination with clear hierarchy and logical relationships, accurately capturing the inherent structure and semantic information of the design object. Furthermore, based on the parameter mapping relationships in the target template, the original attribute values ​​in the geometric entity combination are converted into a standardized set of engineering feature parameters. This embodiment unifies the original attribute values ​​from different design tools, data formats, and even different units and coordinate systems into engineering feature parameters conforming to preset standards, thereby eliminating data heterogeneity. This allows subsequent design object identification, numerical change information aggregation processing, and consistency comparison to be performed on a unified and standardized data basis, thereby improving the accuracy and efficiency of data processing.

[0112] To illustrate this technical solution more clearly, a specific example is used below. Suppose we need to standardize the mapping of a "transition curve" object in a road design. First, a target template for the "transition curve" is selected from a preset engineering object definition strategy. This target template can be an XML file that defines the geometric composition pattern of the transition curve. For example, it consists of key attribute fields such as "starting point coordinates," "ending point coordinates," "starting point tangent direction," "ending point tangent direction," and "rate of curvature change," and includes logical constraints between these attributes, such as the rate of curvature change must be positive, and parameter mapping relationships for converting coordinate units (e.g., millimeters) in the original CAD data to standard engineering units (e.g., meters). Specifically, when the geometric entities and associated attributes representing the transition curve in the original design data are obtained, such as a series of discrete control point coordinates and some unstructured text attributes, structured parsing and pattern matching can be performed on this original data based on the selected transition curve target template. For example, a parser can identify which points are the key control points constituting the curve and which text information represents the curve type or design parameters. Through pattern matching, these discrete control points and text attributes are organized into a logical "transition curve geometric entity combination," which clarifies the role of each control point in the curve and the relationship between other attributes and the curve's geometric shape. Subsequently, based on the preset parameter mapping relationships in the target template, the original attribute values ​​in this transition curve geometric entity combination are converted into a standardized set of engineering feature parameters. For example, if the coordinates in the original data are in millimeters, the mapping relationship converts them to meters; if the curve type in the original data is a "spiral," the mapping relationship converts it to a unified "helix" standard type identifier; if the rate of curvature change in the original data is represented in a specific software's internal format, the mapping relationship converts it to a unified numerical format. Thus, a standardized set of engineering feature parameters, including standardized start-point coordinates, end-point coordinates, tangent direction, and rate of curvature change, is generated. These parameters all conform to preset engineering specifications and data standards, providing a unified and accurate data foundation for subsequent design object identification and comparison.

[0113] Through the above technical solution, this embodiment can perform mapping processing using customized target templates for different types of original geometric entities and associated attributes. This ensures that the generated standardized engineering feature parameter set accurately reflects the geometric composition pattern, key attribute fields, logical constraints between attributes, and parameter mapping relationships of the design object, thereby improving the accuracy and completeness of the mapping. This effectively solves the problem that the standardized engineering feature parameter set cannot accurately reflect the characteristics of the design object due to the use of a unified mapping method, thus improving the reliability of subsequent design object identification and numerical change information aggregation processing.

[0114] In some embodiments, in step S103, the numerical change information between the new version design data and the old version design data is aggregated based on the unique identifier to obtain aggregated change information, which may include, but is not limited to, the following steps:

[0115] Based on the unique identifier, the numerical change information is classified to obtain multiple design attribute categories;

[0116] Based on the priority of design attributes, multiple design attribute categories are sorted and aggregated to obtain aggregated change information.

[0117] In some embodiments, due to the numerous and varying importance of design attributes, simply aggregating all numerical change information may result in critical design attribute changes being buried under a large amount of unimportant change information, thus affecting the efficiency and accuracy of design change review. Accurate aggregation can be achieved by first classifying numerical change information based on unique identifiers, resulting in multiple design attribute categories. This classification process categorizes numerical changes of different natures, such as geometric dimensions, material properties, and constraints, into different design attribute categories. This classification avoids mixing change information of different natures together, laying the foundation for subsequent refined processing. It can be understood that design attribute categories refer to the classification of numerical change information based on the nature or function of the design attribute, specifically distinguishing between geometric attributes, physical attributes, material properties, textual attributes, logical attributes, etc. The purpose is to provide structured management of different types of change information, providing a basis for subsequent refined processing. Then, based on the priority of the design attributes, multiple design attribute categories are sorted and aggregated to obtain aggregated change information. In the sorting, geometric dimension changes directly related to structural safety or functional implementation can have a higher priority than non-critical textual description changes. Sorting and aggregation processes allow design attribute changes that are of higher importance in the engineering design to be prioritized or highlighted in the final aggregated change information. Through this synergistic effect of classification, sorting, and aggregation, it is ensured that when aggregating numerical changes between old and new versions of design data, not only are all changes comprehensively reflected, but key design attribute changes are also intelligently identified and highlighted.

[0118] Through the above technical solution, this embodiment can intelligently classify, sort, and aggregate numerical changes between old and new design data based on the inherent importance of design attributes. This enables key design attribute changes to be effectively identified and highlighted from a large number of non-critical changes during the design change review process, thus avoiding the problem of important information being buried. This embodiment can significantly improve the efficiency of design change review, allowing designers to quickly focus on changes that have a significant impact on the project, ensuring the accuracy of change review, and reducing design risks caused by missing key changes.

[0119] In some embodiments, in step S104, classifying the design objects based on the aggregated change information to obtain the target objects may include, but is not limited to, the following steps:

[0120] Extract the internally defined properties corresponding to each design object in the aggregated change information;

[0121] Based on the internally defined attributes, determine whether the design object contains geometric information, which includes topological information such as geometric shape definition parameters or spatial connection relationships;

[0122] If the design object contains geometric information, then the target object is defined as a complex geometric object;

[0123] If the design object does not contain geometric information, the target object is defined as a regular numerical object.

[0124] In some embodiments, simply classifying based on aggregated change information lacks in-depth analysis of the inherent attributes of the design object itself, making it difficult to accurately distinguish whether the design object is a complex geometric object or a simple numerical object, which may lead to deviations in subsequent parameter comparison and consistency verification. To achieve accurate classification, the internally defined attributes corresponding to each design object in the aggregated change information can be extracted first. These attributes are inherent to the design object when it is created and defined in the design tool, containing key information such as its type, constituent elements, and relationships with other objects. Then, based on the internally defined attributes, it can be determined whether the design object contains geometric information, including geometric shape definition parameters or topological information of spatial connections. For example, the geometric characteristics of the design object can be determined by checking whether there are geometric shape definition parameters in the internally defined attributes, such as the coordinates of control points of a curve, the radius and center of an arc, or the presence of topological information of spatial connections, such as adjacency or containment relationships between points, lines, and surfaces. If the design object contains geometric information, it indicates that the design object has a complex spatial form and internal logic, and the target object can be identified as a complex geometric object; if the design object does not contain geometric information, it indicates that the design object is composed only of simple numerical parameters, and the target object can be identified as a common numerical object. The accurate classification in this embodiment makes subsequent parameter comparison and consistency verification more precise and efficient, effectively avoiding deviations caused by incorrect object type judgment, thereby improving the reliability and accuracy of the entire road design index inspection method.

[0125] Understandably, internally defined attributes refer to a set of data describing the essential characteristics and structure of a design object. These can be implemented using structured data fields, metadata tags, or related data tables, and their purpose is to provide basic information for identifying and understanding the type of the design object. Geometric shape definition parameters refer to the numerical or functional expressions that constitute a geometric shape. These can be implemented using the radius of a circle, the coordinates of the start and end points of a line, the sequence of control points for a curve, or the parametric equations of a surface, and their purpose is to precisely define the shape of the geometric object. Topological information about spatial connections refers to data describing spatial relationships such as connections, adjacencies, or inclusions between geometric entities. This can be implemented using node-edge graphs, face-edge-vertex structures, or spatial index trees, and its purpose is to reveal the spatial logical relationships between geometric objects.

[0126] Through the above technical solution, this embodiment can accurately distinguish whether a design object is a complex geometric object or a common numerical object based on aggregated change information. This allows subsequent parameter comparison and consistency verification to adopt differentiated processing strategies for different types of objects. For example, geometric consistency verification is performed on complex geometric objects, while numerical comparison is performed on common numerical objects. This precise classification avoids the bias caused by judging solely based on changes in data values, thereby improving the accuracy and reliability of design data inspection and effectively solving potential design conflicts caused by incorrect object type judgment.

[0127] In some embodiments, step S105, generating the change results of the upper-level design geometric object feature parameters based on the new version design data and the old version design data, may include, but is not limited to, the following steps:

[0128] Step S301: Obtain the first design parameter of the new version design data and the second design parameter of the old version design data. The target design parameter includes the design tool type and data format. The target design parameter includes either the first design parameter or the second design parameter.

[0129] Step S302: Determine the first parsing configuration based on the first design parameters;

[0130] Step S303: Based on the first parsing configuration, parse the new version design data to obtain the first original definition parameters;

[0131] Step S304: Map the first original definition parameters to the first set of engineering feature parameters;

[0132] Step S305: Perform parameter validity verification on the first set of engineering feature parameters to obtain the first feature parameters corresponding to the complex geometric objects in the new version design data;

[0133] Step S306: Determine the second parsing configuration based on the second design parameters;

[0134] Step S307: Based on the second parsing configuration, parse the old version design data to obtain the second original definition parameters;

[0135] Step S308: Map the second original definition parameters to the second set of engineering feature parameters;

[0136] Step S309: Perform parameter validity verification on the second set of engineering feature parameters to obtain the second feature parameters corresponding to the complex geometric objects in the old version design data;

[0137] Step S310: Compare the first feature parameter and the second feature parameter to obtain the change result.

[0138] In some embodiments, since the design data of the old and new versions may use different design tools and data formats, direct parameter comparison may lead to inaccurate comparison results, fail to effectively identify the true situation of design changes, and lack of verification of parameter validity may result in invalid or incorrect parameters being used in subsequent comparisons, affecting the accuracy of the change results.

[0139] To accurately generate change results, we can first obtain the first design parameters of the new version design data and the second design parameters of the old version design data, thereby identifying the data characteristics under different design environments. The target design parameters include the design tool type and data format, and can be either the first or second design parameters. Then, based on the first design parameters, a first parsing configuration is determined. According to the first parsing configuration, the new version design data is parsed to obtain the first original definition parameters, which are then mapped to a first set of engineering feature parameters. This mapping process transforms the original data, which may not have direct engineering meaning, into feature parameters that conform to engineering specifications and have clear geometric or physical meaning. For example, discrete control points are converted into curvature or tangent directions describing the shape of a curve. The first set of engineering feature parameters is then validated to obtain the first feature parameters corresponding to complex geometric objects in the new version design data. This allows us to identify and exclude invalid or non-compliant parameters, such as values ​​exceeding reasonable ranges or parameters that do not satisfy geometric logical relationships.

[0140] In verifying old version design data, a similar approach to verifying new version design data can be adopted. Based on the second design parameters, a second parsing configuration is determined. According to the second parsing configuration, the old version design data is parsed to obtain the second original definition parameters. These second original definition parameters are then mapped to a second set of engineering feature parameters. The validity of these second set of engineering feature parameters is then verified to obtain the second feature parameters corresponding to complex geometric objects in the old version design data. The first and second feature parameters have been cleaned and verified to ensure their accuracy and reliability. Finally, the first and second feature parameters are compared to obtain the change results. This embodiment, based on feature parameters that have undergone parameter mapping and validity verification, can accurately identify the actual design changes of complex geometric objects (such as road transition curves), including subtle changes in their shape, size, or topological relationships, thereby improving the accuracy of the change results.

[0141] Through the above technical solution, this embodiment effectively solves the problem of inaccurate direct comparison of old and new design data due to differences in design tools and data formats. By performing parameter parsing, engineering feature mapping, and validity verification on the old and new design data respectively, it ensures that the parameters used for comparison have unified engineering meaning and reliability. This enables the system to accurately identify the actual design changes of complex geometric objects, avoiding comparison deviations caused by differences in data formats or tools, thereby improving the accuracy of change results and providing a reliable data foundation for subsequent design checks and collaborative work.

[0142] In some embodiments, step S305 involves validating the parameters of the first set of engineering feature parameters to obtain the first feature parameters corresponding to the complex geometric objects in the new version of the design data. This may include, but is not limited to, the following steps:

[0143] Based on the preset inspection items, each parameter in the first set of engineering characteristic parameters is inspected to obtain the inspection results. The preset inspection items include parameter range, data type and geometric logical relationship defined by engineering specifications.

[0144] Geometric consistency verification is performed on the interrelated parameters in the first set of engineering characteristic parameters to obtain the geometric consistency verification results;

[0145] Based on the test results and geometric consistency verification results, the first feature parameter is generated.

[0146] In some embodiments, due to the complexity and diversity of engineering design data, simply checking parameter range, data type, etc., may not be sufficient to guarantee the validity of the parameters. This is especially true for complex geometric objects, where parameters often have intricate geometrical relationships. Ignoring these relationships may result in geometrically unreasonable generated feature parameters, thus affecting subsequent design consistency comparisons. To accurately generate the first feature parameter, each parameter in the first set of engineering feature parameters can be checked according to preset check items to obtain check results. These preset check items include parameter range, data type, and geometrical logical relationships defined by engineering specifications. These preset check items not only cover basic checks such as parameter range and data type, but more importantly, they introduce geometrical logical relationships defined by engineering specifications. This means that when checking a single parameter, the system will determine whether it conforms to the inherent constraints of that parameter in a specific engineering geometric model; for example, whether the curvature parameter of a curve meets the requirements for smooth transition. Then, geometric consistency verification is performed on the interrelated parameters in the first set of engineering feature parameters to obtain geometric consistency verification results. Geometric consistency verification focuses on the overall coordination between parameters. For example, when multiple parameters constituting a complex geometric object (such as control point coordinates, radius, angle, etc.) are combined, can they form a geometrically reasonable and conflict-free entity? Based on the test results and the geometric consistency verification results, a first characteristic parameter is generated. For instance, when a parameter passes all individual tests and its interrelated parameters also pass the geometric consistency verification, it can be considered valid, and a first characteristic parameter is generated. This ensures the accuracy and geometric rationality of the first characteristic parameter of the complex geometric object extracted from the new version design data.

[0147] By employing the aforementioned technical solution, when validating the validity of the first set of engineering feature parameters, not only are basic checks such as parameter range and data type considered, but also the geometric logical relationships defined in engineering specifications are introduced, and geometric consistency verification is performed on interrelated parameters. This ensures that the first feature parameters corresponding to the generated complex geometric objects are accurate and reasonable in both numerical value and geometric form, effectively avoiding the problem of geometrically unreasonable feature parameters caused by ignoring the complex geometric logical relationships between parameters. This provides a more reliable and accurate data foundation for subsequent design consistency comparisons, significantly improving the accuracy of the comparison results.

[0148] In some embodiments, in step S106, parameter comparison processing is performed based on the change results and the lower-level design documents to obtain a consistency comparison result, which may include, but is not limited to, the following steps:

[0149] Obtain the geometric representation of the lower-level design geometric objects in the lower-level design file;

[0150] The geometric analysis method is determined based on the type of geometric representation;

[0151] The geometric representation is derived and fitted using geometric analysis to obtain the third set of engineering characteristic parameters.

[0152] The validity of the third set of engineering feature parameters is verified to obtain the third feature parameters corresponding to the lower-level design geometric objects.

[0153] Extract the first feature parameter corresponding to the complex geometric object in the new version design data from the change results;

[0154] The consistency comparison between the third feature parameter and the first feature parameter is performed to obtain the consistency comparison result.

[0155] In some embodiments, since lower-level design files typically contain geometric representations of geometric objects, these representations may differ from the parametric forms in the upper-level design data. Direct comparison may not accurately reflect design consistency. Furthermore, different geometric representation types require different analysis methods; using a uniform comparison method may lead to biased results. To improve the accuracy of parameter comparison, the geometric representations of the lower-level design geometric objects in the lower-level design files can be obtained first. Given that different geometric representation types have different inherent structures and definitions, a geometric analysis method can be determined based on the type of geometric representation. Then, the geometric analysis method is used to perform reverse derivation and fitting processing on the geometric representation to obtain a third set of engineering characteristic parameters. This transforms the non-parametric geometric representation of the lower-level design files into a parametric form corresponding to the upper-level design data, such as extracting engineering parameters like control point coordinates, radius, or curvature from the geometric shape of a curve. The fitting process further optimizes these extracted parameters to more accurately reflect the original design intent. Next, the validity of the third set of engineering feature parameters is verified to obtain the third feature parameters corresponding to the lower-level design geometric objects. This eliminates abnormal parameters caused by data conversion errors or defects in the original data, ensuring the accuracy of subsequent comparisons. The first feature parameters corresponding to complex geometric objects in the new version of the design data are extracted from the change results. These parameters represent the parameterized representation of complex geometric objects in the upper-level design data. Finally, the third feature parameters and the first feature parameters are compared for consistency, obtaining the consistency comparison results. This avoids the potential bias caused by directly comparing different forms of geometric representation, achieving a more accurate and reliable design consistency check.

[0156] Through the above technical solution, this embodiment effectively solves the problem of comparison difficulties caused by inconsistencies between the geometric representation of lower-level design documents and the parameter forms of upper-level design data. By converting the geometric representation of lower-level design documents into a comparable parameter form and performing rigorous parameter validity verification, the accuracy and reliability of the comparison are ensured. This enables refined design consistency checks in complex engineering designs, even when upper and lower-level data use different representation methods. It accurately identifies and reveals implicit design differences caused by data transmission or interpretation deviations, effectively avoiding potential engineering conflicts and rework, and improving design quality and efficiency.

[0157] In some embodiments, after parsing the new version design data and generating a unique identifier for the design object, the method further includes:

[0158] The design object is structurally analyzed to obtain multiple sub-objects;

[0159] Based on the unique identifier, generate a sub-identifier corresponding to each sub-object;

[0160] When a sub-object is modified, a unique identifier is sent to the upper-level design platform, and the corresponding sub-identifier of the sub-object is sent to the lower-level design platform.

[0161] In some embodiments, when a design object consists of multiple sub-objects and the design changes, simply sending the unique identifier of the design object to the upper-level design platform may not accurately locate the specific sub-object that has changed, nor may it transmit the change information of the sub-object to the lower-level design platform. This results in incomplete information synchronization between the upper and lower-level design platforms, affecting the efficiency and accuracy of collaborative design. To more accurately identify modified design objects, the design object can first be structurally analyzed, decomposing a complex overall design object into multiple smaller, more manageable sub-objects, thereby enabling the identification of independent variable units within the design object. Then, based on the unique identifier, a sub-identifier corresponding to each sub-object is generated. This sub-identifier generation mechanism establishes a clear association between the sub-object and the parent design object, while giving the sub-object an independent identity, allowing each sub-object to be uniquely identified and tracked throughout the entire design system. When a sub-object is modified, a unique identifier is sent to the upper-level design platform to inform it that the design object has been changed. The corresponding sub-identifier of the sub-object is also sent to the lower-level design platform to accurately indicate the specific location where the change occurred. This allows the upper-level design platform to grasp the overall design change situation, while the lower-level design platform can receive the change details down to the sub-object level.

[0162] Through the above technical solution, this embodiment enables fine-grained management and precise tracking of sub-objects within a complex design object. When a design change occurs, the system can not only transmit the overall design object change information to the upper-level design platform, but also precisely transmit the identifiers of the specific modified sub-objects to the lower-level design platform, thereby solving the problem of accurately locating and tracking sub-object changes in existing technologies. This ensures the integrity and accuracy of information synchronization between the upper and lower-level design platforms, significantly improving the efficiency and accuracy of collaborative design, and avoiding design conflicts and rework caused by information asymmetry.

[0163] The beneficial effects of implementing the embodiments of the present invention include: First, the embodiments of this application obtain new version design data and old version design data, parse the new version design data to generate a unique identifier for the design object, and then, based on the unique identifier, aggregate the numerical change information between the new version design data and the old version design data to obtain aggregated change information. Then, based on the aggregated change information, the design object is classified to obtain the target object. If the target object is a complex geometric object, the change result of the feature parameters of the upper-level design geometric object is generated based on the new version design data and the old version design data. Finally, based on the change result and the lower-level design file, parameter comparison processing is performed to obtain a consistency comparison result. Thus, it is possible to combine the identification of complex geometric objects to realize the road design index check, thereby improving the accuracy.

[0164] like Figure 2 As shown in the figure, this embodiment of the invention also provides a road design index inspection system, including:

[0165] Module 401 is used to obtain design data for both the new and old versions.

[0166] Parsing module 402 is used to parse the new version design data and generate a unique identifier for the design object;

[0167] The aggregation module 403 is used to aggregate the numerical change information between the new version design data and the old version design data based on the unique identifier to obtain aggregated change information.

[0168] The classification module 404 is used to classify design objects according to aggregated change information to obtain target objects, which include complex geometric objects or ordinary numerical objects.

[0169] The parameter change recognition module 405 is used to generate the change results of the feature parameters of the upper-level design geometric object based on the new version design data and the old version design data if the target object is a complex geometric object.

[0170] The consistency comparison module 406 is used to perform parameter comparison processing based on the change results and the lower-level design documents to obtain the consistency comparison results.

[0171] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0172] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for checking road design indicators, characterized in that, Includes the following steps: Obtain design data for both the new and old versions; The new version of the design data is parsed to generate a unique identifier for the design object; Based on the unique identifier, the numerical change information between the new version design data and the old version design data is aggregated to obtain aggregated change information. Based on the aggregated change information, the design objects are classified to obtain target objects, which include complex geometric objects or ordinary numerical objects. If the target object is a complex geometric object, then based on the new version design data and the old version design data, the change result of the feature parameters of the upper-level design geometric object is generated; Based on the changes and the lower-level design documents, parameter comparison processing is performed to obtain consistency comparison results; The step of performing parameter comparison processing based on the change results and the lower-level design documents to obtain consistency comparison results includes: Obtain the geometric representation of the lower-level design geometric objects in the lower-level design file; The geometric analysis method is determined based on the type of the geometric representation. The geometric representation is derived and fitted using the geometric analysis method to obtain the third set of engineering characteristic parameters. The validity of the third set of engineering feature parameters is verified to obtain the third feature parameters corresponding to the lower-level design geometric object. Extract the first feature parameter corresponding to the complex geometric object in the new version design data from the change results; The consistency comparison between the third feature parameter and the first feature parameter is performed to obtain the consistency comparison result.

2. The method according to claim 1, characterized in that, The process of parsing the new version design data to generate a unique identifier for the design object includes: The first design parameter of the new version design data is obtained, and the first design parameter includes the design tool type and data format; Based on the first design parameters, the parser is used to extract the original geometric entities and associated attributes from the new version design data; Based on the preset engineering object definition strategy, the original geometric entities and associated attributes are mapped to obtain a standardized set of engineering feature parameters. Identify the design object based on the standardized set of engineering characteristic parameters; Perform a geometric topology consistency check on the standardized engineering feature parameter set to obtain the geometric topology consistency check result; Based on the geometric topology consistency verification result and the standardized engineering feature parameter set, a unique identifier for the design object is generated.

3. The method according to claim 2, characterized in that, The process of mapping the original geometric entities and associated attributes according to a preset engineering object definition strategy to obtain a standardized set of engineering feature parameters includes: Select a target template from the preset engineering object definition strategy. The target template includes the geometric composition pattern of the design object, key attribute fields, logical constraints between attributes, and parameter mapping relationships. Based on the target template, the original geometric entities and associated attributes are subjected to structured parsing and pattern matching to obtain a combination of geometric entities; Based on the parameter mapping relationship in the target template, the original attribute values ​​in the geometric entity combination are converted into the standardized engineering feature parameter set.

4. The method according to claim 1, characterized in that, The step of aggregating the numerical change information between the new version design data and the old version design data based on the unique identifier to obtain aggregated change information includes: Based on the unique identifier, the numerical change information is classified to obtain multiple design attribute categories; Based on the priority of the design attributes, the multiple design attribute categories are sorted and aggregated to obtain the aggregated change information.

5. The method according to claim 1, characterized in that, The step of classifying design objects based on the aggregated change information to obtain target objects includes: Extract the internally defined attributes corresponding to each design object in the aggregated change information; Based on the internally defined attributes, determine whether the design object contains geometric information, including geometric shape definition parameters or topological information of spatial connection relationships; If the design object contains geometric information, then the target object is determined as the complex geometric object; If the design object does not contain geometric information, then the target object is determined as the ordinary numerical object.

6. The method according to claim 1, characterized in that, The step of generating the change results of the upper-level design geometric object feature parameters based on the new version design data and the old version design data includes: Obtain the first design parameter of the new version design data and the second design parameter of the old version design data. The target design parameter includes the design tool type and data format. The target design parameter includes either the first design parameter or the second design parameter. Based on the first design parameters, determine the first parsing configuration; Based on the first parsing configuration, the new version design data is parsed to obtain the first original definition parameters; Map the first original definition parameters to the first set of engineering feature parameters; The first set of engineering feature parameters is validated to obtain the first feature parameters corresponding to the complex geometric objects in the new version of the design data. Based on the second design parameters, determine the second parsing configuration; Based on the second parsing configuration, the old version design data is parsed to obtain the second original definition parameters; Map the second original definition parameters to the second set of engineering feature parameters; The validity of the second set of engineering feature parameters is verified to obtain the second feature parameters corresponding to the complex geometric objects in the old version design data. The first feature parameter and the second feature parameter are compared to obtain the change result.

7. The method according to claim 6, characterized in that, The step of validating the parameter validity of the first set of engineering feature parameters to obtain the first feature parameters corresponding to the complex geometric objects in the new version of the design data includes: According to the preset inspection items, each parameter in the first set of engineering feature parameters is inspected to obtain the inspection results. The preset inspection items include parameter range, data type and geometric logical relationship defined by engineering specifications. Geometric consistency verification is performed on the interrelated parameters in the first set of engineering feature parameters to obtain the geometric consistency verification results; The first feature parameter is generated based on the test results and the geometric consistency verification results.

8. The method according to claim 1, characterized in that, After parsing the new version design data and generating a unique identifier for the design object, the method further includes: The design object is structurally analyzed to obtain multiple sub-objects; Based on the unique identifier, generate a sub-identifier corresponding to each sub-object; When a sub-object is modified, the unique identifier is sent to the upper-level design platform, and the sub-identifier corresponding to the sub-object is sent to the lower-level design platform.

9. A road design index inspection system, characterized in that, include: The acquisition module is used to acquire design data for both new and old versions. The parsing module is used to parse the new version design data and generate a unique identifier for the design object; The aggregation module is used to aggregate the numerical change information between the new version design data and the old version design data based on the unique identifier to obtain aggregated change information. The classification module is used to classify the design objects according to the aggregated change information to obtain target objects, including complex geometric objects or ordinary numerical objects; The parameter change identification module is used to generate the change result of the feature parameters of the upper-level design geometric object based on the new version design data and the old version design data if the target object is a complex geometric object. The consistency comparison module is used to perform parameter comparison processing based on the change results and the lower-level design documents to obtain the consistency comparison results; The consistency comparison module is also used for: Obtain the geometric representation of the lower-level design geometric objects in the lower-level design file; The geometric analysis method is determined based on the type of the geometric representation. The geometric representation is derived and fitted using the geometric analysis method to obtain the third set of engineering characteristic parameters. The validity of the third set of engineering feature parameters is verified to obtain the third feature parameters corresponding to the lower-level design geometric object. Extract the first feature parameter corresponding to the complex geometric object in the new version design data from the change results; The consistency comparison between the third feature parameter and the first feature parameter is performed to obtain the consistency comparison result.

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