An engineering full-life-cycle multi-source heterogeneous data fusion mapping method

Abstract

The application relates to the technical field of data analysis, and is a fusion mapping method for multi-source heterogeneous data in the whole life cycle of engineering, which specifically comprises the following steps: establishing a unified entity identification coding rule through a multi-source engineering information set, converting engineering information in different stages into an identification component set, and constructing an entity initial expression matrix; performing hierarchical discrimination on through, superimposed and staggered relations through candidate relation screening and local topological measure calculation, converting each hierarchical spatial relation unit into a standard relation expression record, and forming a component relation set; forming a cross-stage component evolution path expression model through candidate association pair screening, fine semantic matching and chain segment splicing; and obtaining a continuous spatial semantic expression model covering the whole life cycle through unified space mapping, fusion weight calculation, space aggregation and consistency checking. The application solves the problem of historical semantic and present semantic fault caused by the division of construction stages in the prior art.

Description

Technical Field

[0001] This invention belongs to the field of data analysis, and more specifically, relates to a method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project. Background Technology

[0002] Throughout the entire lifecycle of an engineering project, such as the renovation and expansion of urban underground utility tunnels, the fusion and mapping of multi-source heterogeneous data is crucial for achieving structural status tracing and construction impact analysis. Existing technologies typically rely on geometric model matching or explicit identifier-based associations to establish correspondences between components, making it difficult to uniformly represent the fine topological semantics between components in complex spatial scenarios. In underground engineering, existing pipelines and new structures often exhibit non-connected spatial contact forms such as overlapping, intersecting, and crossing in physical space. Their spatial relationships evolve continuously with the construction phases, forming multi-stage and multi-type spatial coupling states. However, current mainstream data fusion methods mostly focus on static geometric expressions or simple spatial inclusion determinations, lacking consideration of local interaction zones, contact tightness, etc., between components. The comprehensive modeling capability of deep topological features such as directional intersection characteristics and elevation level differences makes it difficult to form a continuous and traceable expression between historical construction semantics and current structural semantics. Especially in the process of multi-stage data connection, due to the lack of an effective discrimination mechanism for the spatial inheritance relationship between components across construction stages, semantic breaks occur in the fusion mapping results for components with the same spatial location or functional association. It is impossible to accurately distinguish the different topological action forms such as crossing, overlapping, and intersecting, and it is also difficult to assign a reasonable hierarchical order to different relationship strengths. This spatial semantic discontinuity directly weakens the decision support capability of multi-source data fusion results in structural safety analysis, construction disturbance assessment, and life-cycle operation and maintenance management, and limits the continuous cognition and efficient utilization of engineering data on the time axis. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to resolve the aforementioned deficiencies and propose a method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project.

[0004] The present invention adopts the following technical solution; The first aspect of this invention discloses a method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project, as follows: S1: Establish a unified entity identification coding rule through a multi-source engineering information set, transform engineering information at different stages into a set of identification components, and construct an initial entity representation matrix; S2: Based on the initial entity representation matrix, through candidate relation screening and local topological measure calculation, the traversal, overlapping and intersecting relations are classified and identified, and each hierarchical spatial relation unit is converted into a standard relation representation record to form a set of component relations with topological semantics; S3: Based on the component relationship set, through candidate association pair screening, fine semantic matching and chain segment splicing, a local evolution chain segment reflecting the historical changes of components is constructed and standardized to form a cross-stage component evolution path expression model; S4: Based on the cross-stage component evolution path expression model, the cross-stage component evolution path is integrated into a continuous spatial semantic fragment through unified spatial mapping, fusion weight calculation, spatial aggregation and consistency verification, resulting in a continuous spatial semantic expression model covering the entire life cycle.

[0005] Specifically, S1 includes: S11: Unify the coordinates of the multi-source engineering information set and convert all structural boundaries into three-dimensional boundary segments; set the cutting points at the abrupt change locations, define the local structural segments between adjacent cutting points as candidate component segments, and calculate the spatial continuity coefficient of their adjacent segments to obtain the set of candidate component segments. S12: Based on the candidate component fragment set, perform component-level merging judgment according to the spatial continuity coefficient and functional consistency coefficient, merge the fragments that meet the conditions into complete component-level entities, and form a component-level entity set; S13: Extract the spatial location, functional classification, stage affiliation and boundary morphology features of each component-level entity in the component-level entity set, generate a unique entity identifier according to the unified coding rules, and output the identifier component set with the unique entity identifier. S14: Construct the initial entity representation matrix by using the set of identifier components as row objects and the representation features of the components as column objects.

[0006] Specifically, S2 includes: S21: Read the spatial center position, main direction and equivalent cross-sectional size of all component-level entities in the initial entity representation matrix, and establish a three-dimensional neighborhood search framework; by calculating the candidate proximity of component pairs, filter out component pairs with crossing, overlapping or intersecting relationships, and form a set of candidate spatial relationship pairs; S22: For each pair of components in the candidate spatial relationship pair set, perform discrete sampling along its geometry, calculate its equivalent minimum spacing, local projection overlap ratio, directional cross index and high-level difference, and form a set of local topological contact features.

[0007] Specifically, S2 also includes: S23: Based on the local topological contact feature set, determine the relationship category of the component pairs, classify the relationship level and determine the order of the high-level level to form a hierarchical spatial relationship unit, and output the hierarchical spatial relationship unit set simultaneously; The hierarchical spatial relationship unit includes: relationship category, high-level hierarchical order, and relationship strength; S24: Convert each hierarchical spatial relation unit in the hierarchical spatial relation unit set into a standard relation expression record, sort the standard relation expression records by priority through sorting weight, generate a unified spatial relation expression sequence, and summarize it into a component relation set with topological semantics; The component relationship set includes: starting component number, target component number, relationship type, relationship level, high-level hierarchy order, local action section location, relationship strength, and the construction stage code, functional category code, and unique entity identifier of the corresponding component.

[0008] Specifically, S3 includes: S31: The component relationship set is hierarchically divided according to the construction stage, with the unique entity identifier as the main index; the candidate association score is calculated to quantify the candidate inheritance probability, and the candidate association pair screening is performed between adjacent stages to form a stage candidate association pair set.

[0009] Specifically, S3 also includes: S32: Based on the set of candidate association pairs for each stage, the semantic matching strength is calculated by comprehensively considering four dimensions: relational structure similarity, spatial inheritance stability, stage transition rationality, and boundary change tolerance. Then, fine semantic matching is performed on the candidate association pairs to output the set of semantic matching units for each stage. S33: Based on the set of stage semantic matching units, the stage semantic matching units are spliced ​​together according to the principles of high matching priority, single meaning inheritance priority, and stage continuity priority to form local evolution chain segments, and the chain segment with the largest cumulative evolution gain is retained to form a set of local evolution chain segments. S34: Based on the set of local evolution chain segments, all local evolution chain segments are uniformly arranged according to component category, spatial cluster and stage order, stage node normalization and path relationship normalization are performed, and the final credibility of the path is calculated to obtain the cross-stage component evolution path expression model.

[0010] Specifically, S4 includes: S41: Based on the cross-stage component evolution path expression model, extract the nodes of each stage in the evolution path and project them onto a unified spatial reference frame. According to the path connection type, convert them into a continuous spatial mapping main line to form a unified spatial mapping primitive set.

[0011] Specifically, S4 also includes: S42: For each spatial mapping primitive in the unified spatial mapping primitive set, extract the final credibility of its path and calculate the fusion weight by combining stage coverage, relation continuity and current status validity, and output the path fusion weight set; S43: Under the unified spatial reference framework, spatial mapping primitives are fused and aggregated by introducing fusion semantic intensity according to spatial adjacency or path connection relationship, and continuous spatial semantic fragments are generated by using a sliding window method, and a set of continuous spatial semantic fragments is output. S44: Perform global arrangement on a continuous set of semantic segments, calculate the consistency value of each segment, and then perform consistency checks and merge adjacent segments to obtain multiple global semantic segments. Simultaneously, the global consistency strength of each global semantic segment is calculated, ultimately forming a continuous spatial semantic expression model and a lifetime-long consistent fusion mapping result output.

[0012] A second aspect of the present invention discloses a terminal, including a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project, as described in the first aspect.

[0013] The third aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, characterized in that, when the program is executed by a processor, it implements the steps of the fusion mapping method for multi-source heterogeneous data throughout the entire life cycle of an engineering project as described in the first aspect.

[0014] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following advantages: 1. This technical solution determines the relationship category of component pairs through local topological contact features, explicitly distinguishes complex spatial action forms such as crossing, overlapping, and intersecting, and achieves continuous expression from qualitative to quantitative through relationship strength scoring and relationship level classification. This makes different spatial actions have comparable density levels in the fusion results, solving the technical defects of traditional methods that rely solely on geometric overlap or simple distance thresholds to characterize the directional intersection characteristics and elevation level differences of local action sections. It provides a unified semantic modeling framework for the numerous non-connectivity spatial coupling relationships that exist in underground hidden engineering. 2. This technical solution converts hierarchical spatial relationship units into standard relationship expression records and introduces a sorting weight mechanism to prioritize spatial relationships formed from multiple sources and stages, generating a set of component relationships with topological semantics. This set records the full picture of each set of spatial actions in a complete manner with structured fields such as starting component, target component, relationship type, relationship level, high-level order, local action segment location, and construction stage code. It supports each component-level entity to quickly query its superior, inferior, traversed, superimposed, and intersecting adjacent objects, changing the limitations of fragmented spatial relationships, difficulty in indexing and reorganization in traditional fusion mapping results. 3. This technical solution hierarchically categorizes the component relationship set according to construction stages. It uses a unique entity identifier as the main index and combines it with auxiliary constraints such as spatial function segment, relationship type, functional category, and high-level hierarchy order. Through a candidate association scoring mechanism, it filters cross-stage association pairs between adjacent stages. It integrates multi-dimensional features such as geometric proximity, adjacency pattern similarity, and functional inheritability into quantitative evaluation indicators, realizing cross-stage connection of spatial semantics between existing components and newly added or modified components. This effectively curbs the problem of historical semantics and current semantic discontinuity caused by the separation of construction stages in renovation and expansion projects, and provides continuous and interpretable data support for structural safety analysis and construction disturbance tracing throughout the entire life cycle. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating a method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project, according to the present invention. Figure 2 This is a schematic diagram illustrating the principle of a method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project, as proposed by the present invention. Detailed Implementation

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0019] Example 1:

[0020] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project, comprising the following: It should be noted that, in this embodiment, the distance attenuation constant, direction attenuation constant, weighting coefficient, overlap enhancement index, and various preset thresholds involved in each step of the present invention are all adaptively set or calibrated through samples based on the spatial scale, geometric shape, data accuracy, and engineering scenario of the engineering object. The specific value selection methods are as follows: for linear components such as pipelines, a smaller value is taken for the attenuation constant to highlight the local neighborhood effect; for planar or volumetric structures such as cabins and well chambers, a larger value is taken for the attenuation constant to maintain spatial continuity; the weighting coefficient is determined through the analytic hierarchy process or sensitivity test to ensure that the contribution of each dimension matches the engineering focus; various thresholds are obtained based on the statistics of typical engineering samples, or the combination that makes the fusion result most consistent with the expert judgment is selected from multiple candidate values; the above value selection methods are technical choices that can be implemented by those skilled in the art according to specific engineering conditions, and do not constitute a limitation on the scope of protection of the present invention. S1: Establish a unified entity identification coding rule through a multi-source engineering information set, transform engineering information at different stages into a set of identification components, and construct an initial entity representation matrix; S1 includes: S11: Unify the coordinates of the multi-source engineering information set and convert all structural boundaries into three-dimensional boundary segments; set the cutting points at the abrupt change locations, define the local structural segments between adjacent cutting points as candidate component segments, and calculate the spatial continuity coefficient of their adjacent segments to obtain the set of candidate component segments. For example, in this embodiment, the source engineering information includes: component outline information in the design model, component attribute information in the as-built data, description information of newly added components in the renovation and expansion data, and structural boundary information formed by on-site detection; The mutation locations include: mutation locations of cross-sectional shape, mutation locations of orientation, mutation locations of material, mutation locations of installation elevation, and mutation locations marked by construction stage. It should be noted that, in this embodiment, pipeline objects are segmented based on centerline, cross-sectional dimensions, burial depth, and medium type; while compartment, support, maintenance well, and interface node objects are segmented based on closed boundary, installation location, and attachment relationship. For example, in this embodiment, the spatial continuity coefficient of the adjacent segments is specifically: ; In the formula, Let be the spatial continuity coefficient between fragment i and fragment j, and its value ranges from 0 to 1. A larger value indicates a higher degree of spatial continuity between the two segments; This is the shortest distance between the control points at the ends of the two segments; This is the distance attenuation scale constant, with a value ranging from 0.05 to 0.30; , These are the principal direction angles of the two segments; is the directional attenuation scale constant, and its value ranges from 0.10 to 0.35; Let be the area of ​​overlap of the projections of the two segments within the adjacent region; , These represent the local window areas at the adjacent ends of the two segments; It is a tiny positive number, and its value ranges from 10. -6 -10 -4 ; This is the overlap enhancement index, with a value range of 1.2-2.5; when When the threshold is exceeded, the adjacent segment pair is marked as pending merging, and its association is preserved; otherwise, it is not merged. It should also be noted that, in this embodiment, this step cuts the mixed engineering description into identifiable local structural units; S12: Based on the candidate component fragment set, perform component-level merging judgment according to the spatial continuity coefficient and functional consistency coefficient, merge the fragments that meet the conditions into complete component-level entities, and form a component-level entity set; It should be noted that, in this embodiment, when performing component-level merging, in addition to considering the geometric fit relationship, the consistency of functional attributes and construction stage information must also be checked. For pipeline objects, the functional attributes should include at least: medium type, pressure level, application type, and operation and maintenance responsibility unit; for structural objects, the functional attributes should include at least: load-bearing application, protection application, support application, and maintenance application; construction phase information should include at least: existing phase, demolition and modification phase, new addition phase, and replacement phase. For example, in this embodiment, a functional consistency coefficient is provided. The acquisition strategy is as follows: ; In the formula, , These represent the number of functional attribute items for the two segments; Number of items that match the functional attribute; , These are the equivalent cross-sectional dimensions of the two component-level entities, respectively. It should be noted that in this embodiment, the outer diameter is used for circular pipes, and the hydraulic diameter is used for rectangular hulls. This is the dimensional tolerance constant, with a value ranging from 0.02 to 0.20. To quantify the likelihood of fragment merging, the entity merging score between fragment i and fragment j is calculated. Specifically: ; This is the stage matching coefficient. It should be noted that when two segments are in the same construction stage, its value ranges from 1; when they are in the inheritable stage, its value ranges from 0.4 to 0.8; and when they are in the conflict stage, its value ranges from 0 to 0.2. The boundary conflict coefficient has a value range of 0-1 and is used to characterize the degree of discontinuity between two segments in terms of normal, thickness, and interface relationship. The larger the value, the more obvious the conflict. , , , These are the weighting coefficients, with values ​​ranging from 0.30-0.45, 0.20-0.35, 0.15-0.25, and 0.10-0.20, respectively. , , , The sum is 1; The conflict phase represents the coexistence of new construction and abandonment; An inheritable phase indicates that there is a clear inheritance relationship between the existing phase and the replacement phase; Merge entity scores Using higher-order segments as seeds, the merging region is gradually expanded, and the boundary segments are checked a second time. When entities are merged for scoring When the threshold is exceeded, fragments i and j are determined to be the same component-level entity; otherwise, they remain separated. It should also be noted that, in this embodiment, this step determines which local segments are ultimately considered the same component; S13: Extract the spatial location, functional classification, stage affiliation and boundary morphology features of each component-level entity in the component-level entity set, generate a unique entity identifier according to the unified coding rules, and output the identifier component set with the unique entity identifier. For example, in this embodiment, the encoding adopts a combination structure of spatial discrete code, functional category code, stage code and boundary check code, so that historical components and current components can be distinguished and traced in the evolution chain. A check value is generated by calculating the main identifier value, and then the check value is output as a boundary check code to form a unique entity identifier; For example, in this embodiment, a strategy for obtaining the main identifier value is provided, specifically as follows:

[0021] ; In the formula, ; ; In the formula, This is the primary identifier value of the k-th component-level entity; The coordinates of the geometric center of the component entity; The spatial distance is the walk distance, and its value ranges from 0.10 to 1.00; To round down; The modulus constant, , , , , All are weighted prime numbers; It also provides a check value The acquisition strategy is as follows: ; In the formula, This is the primary identifier value of the k-th component-level entity; Functional category coding is achieved by mapping water supply and drainage, electricity, communication, gas, and structural support to different integer ranges; The construction stage is coded as follows: In this embodiment, when the construction stage is in an existing stage, its value is 1; when it is in a demolition and renovation stage, its value is 2; when it is in a new stage, its value is 3; and when it is in a replacement stage, its value is 4. The boundary morphology is encoded as an integer, which is obtained by combining the cross-sectional shape, interface type, and installation method. S14: Construct the initial entity representation matrix by using the set of identifier components as row objects and the representation features of the components as column objects.

[0022] It should be noted that, in this embodiment, each row of the initial entity representation matrix corresponds to a component-level entity, and each column corresponds to a representation feature; The expressive features include at least: component-level entity number, spatial center location, main direction, equivalent cross-sectional size, functional category, construction stage, boundary morphology, number of identified interfaces, and expressive confidence of the component-level entity; For example, in this embodiment, a strategy for obtaining the expression confidence of component-level entities is provided, specifically as follows: ; In the formula, is the confidence level of the representation of the k-th component-level entity, and its value ranges from 0 to 1. The larger the value, the more complete and stable the component-level representation is. The effective continuous length of the component; This is the length normalization constant, and its value ranges from 1 to 20; This represents the number of identified interfaces. This is the interface normalization constant, and its value ranges from 1 to 10. This is a boundary closure index, and its value ranges from 0 to 1. This is the closure normalization constant, and its value ranges from 0.5 to 1; This is a conflict penalty term used to characterize the degree of conflict between the component and surrounding entities in terms of boundary occupancy; These are all weighting coefficients, with values ​​ranging from 0.10 to 0.40. The sum is 1; This is the compression factor, and its value ranges from 2 to 8; It should be noted that, in this embodiment, the confidence level of the component-level entity is expressed. As the last column of the matrix, it is written into the initial entity expression matrix along with the expression features of each component-level entity; The elements in the initial expression matrix of this entity This represents the value of the k-th component-level entity in the h-th feature dimension (such as spatial center coordinate components, functional encoding, stage encoding, expression confidence, etc.). It should be noted that, in this embodiment, the elements in the initial entity representation matrix... In this context, k represents the component number; h represents the characteristic number; when h corresponds to the spatial center position, These are the geometric center coordinate components; when h corresponds to a functional category, This is a functional code; when h corresponds to the construction stage, Encoding for the construction phase; when h corresponds to the confidence level. The confidence level of the representation of this component-level entity. ; S2: Based on the initial entity representation matrix, through candidate relation screening and local topological measure calculation, the traversal, overlapping and intersecting relations are classified and identified, and each hierarchical spatial relation unit is converted into a standard relation representation record to form a set of component relations with topological semantics; S2 includes: S21: Read the spatial center position, main direction and equivalent cross-sectional size of all component-level entities in the initial entity representation matrix, and establish a three-dimensional neighborhood search framework; by calculating the candidate proximity of component pairs, filter out component pairs with crossing, overlapping or intersecting relationships, and form a set of candidate spatial relationship pairs; For example, in this embodiment, the Gauss-Kruger projection plane coordinate system is first used as the spatial reference to unify all component-level entities under this reference frame. Then, based on the spatial center position, main direction, and equivalent cross-sectional dimensions of the components, the R-tree spatial index is used to organize the geometric placement of the components, and the search radius is set differently according to the component type. Specifically, for linear components such as pipelines, the search radius is 3 to 5 times the equivalent diameter of the cross-section, and for volumetric components such as well chambers and compartments, the search radius is 1 to 2 times the diagonal length of the outer box. At the same time, the corresponding neighborhood mode is set according to the component combination type. The centerline envelope neighborhood is used between linear pipelines, the outer expansion neighborhood is used between linear pipelines and volumetric components, and the outer box neighborhood is used between volumetric components. The neighboring components within the search radius of each component are quickly retrieved in the above manner, and the candidate proximity degree is calculated for initial screening, thereby forming a set of candidate spatial relationship pairs. For example, in this embodiment, a candidate proximity acquisition strategy is provided, specifically as follows: ; In the formula, Candidate proximity between component-level entity i and component-level entity j; The Euclidean distance between the geometric centers of two component-level solids; This is the distance attenuation constant, with a value ranging from 0.50 to 5.00; The angle between the principal directions of two component-level entities. The directional attenuation constant has a value range of 0.20-0.80; , These are the equivalent cross-sectional dimensions of the two component-level entities, respectively. Component pairs that exhibit crossing, overlapping, or intersecting relationships are specifically identified as follows: When candidate proximity When the value is greater than or equal to the preset threshold, it indicates that the component pair is highly likely to have a crossing, overlapping or intersecting relationship, and the component pair is recorded in the candidate spatial relationship pair set. When candidate proximity If the value is less than the preset threshold, it means that the probability of the component pair having a crossing, overlapping or intersecting relationship is extremely low, and it will not be included in the candidate spatial relationship pair set. It should be noted that, for different object types, this embodiment adopts a differentiated neighborhood search strategy, specifically including: a centerline envelope neighborhood is used between linear pipelines; an external expansion neighborhood is used between linear pipelines and planar or volumetric objects such as well chambers, supports, and cabins; for large-volume objects such as well chambers and cabins, a cross-sectional expansion radius is superimposed on their outer box to prevent missed detection of crossing relationships. S22: For each pair of components in the candidate spatial relationship pair set, perform discrete sampling along its geometry, calculate its equivalent minimum spacing, local projection overlap ratio, directional cross index and high-level difference, and form a set of local topological contact features; It should be noted that in this embodiment, a sliding window statistical method is used to summarize the single-point results into window-level feature values; The window length ranges from 0.3 to 2.00 m; For example, in this embodiment, a strategy for obtaining the equivalent minimum spacing is provided, specifically as follows: ; In the formula, It is the equivalent minimum distance between components i and j within the local window, used to characterize the degree of proximity of contact or crossing; This represents the number of discrete sampling points within the window. Let be the minimum distance from the t-th sampling point to the boundary of the other component; This is the distance mitigation constant, with a value ranging from 0.01 to 0.20; A strategy for obtaining the local projection overlap ratio is provided, specifically as follows: ; In the formula, The local projection overlap ratio is used to characterize the degree of overlap on the local reference plane, and its value ranges from 0 to 1. The area of ​​overlap between the two projections; , These are the projected areas of the two components on the same local reference plane; A strategy for obtaining directional crossover indices is also provided, specifically: ; In the formula, The directional cross index is used to characterize the cross strength between the axes or main directions of a component, and its value ranges from 0 to 1. , This represents the elevation value at the center of the local window. The elevation attenuation constant has a value range of 0.10-1.50. At the same time, the elevation level difference is determined by comparing the average elevation within a local window; The equivalent minimum spacing, local projection overlap ratio, directional crossover index, and high-level hierarchy difference are output as a set of local topological contact features. S2 also includes: S23: Based on the local topological contact feature set, determine the relationship category of the component pairs, classify the relationship level and determine the order of the high-level level to form a hierarchical spatial relationship unit, and output the hierarchical spatial relationship unit set simultaneously; The hierarchical spatial relationship unit includes: relationship category, high-level hierarchical order, and relationship strength; For example, in this embodiment, the determination of the relationship category of the component pairs specifically involves: If the equivalent minimum spacing Close to zero and directional crossover index If the value is high, the relationship category will be classified as a crossing candidate; If the local projection overlap ratio If the elevation difference is relatively high and the elevation level difference is stable, then the relationship category is determined to be an overlay candidate; If the equivalent minimum spacing Smaller, directional crossover index If the relationship is of medium quality but no actual crossing occurs, the relationship category will be classified as an interleaved candidate. Then, the relationship strength between components i and j is determined by the relationship strength score, and the relationship strength score is mapped to the relationship level to classify the relationship levels. For example, in this embodiment, a relationship strength score is provided. The acquisition strategy is as follows: ; In the formula, This represents the local average elevation difference. This is a length reference constant, with a value range of 0.05-0.50; , , , These are all weighting coefficients, with values ​​ranging from 0.20-0.35, 0.20-0.30, 0.20-0.30, and 0.10-0.25 respectively. , , , The sum is 1; A strategy for obtaining relationship levels is also provided, specifically: ; In the formula, This represents the relational level, with values ​​ranging from 1 to 3, and are integers. A larger value indicates a closer and more sensitive relationship; This is the gradation scale constant, with a value ranging from 0.60 to 1.20; The specific classification of relationship levels is as follows: intersecting entities in the central area or with minimal safety clearance are classified as Level 1 crossings; partially intersecting entities with identifiable boundaries are classified as Level 2 crossings; and entities that are clearly close to each other and located in areas sensitive to construction intervention are classified as Level 3 crossings. The higher-level hierarchy is determined by comparing local elevation averages, and the labels of the upper, lower, or same-level components are output. S24: Based on the hierarchical spatial relation unit set, each hierarchical spatial relation unit is converted into a standard relation expression record. The standard relation expression records are prioritized by sorting weights to generate a unified spatial relation expression sequence, which is then summarized into a component relation set with topological semantics. The component relationship set includes: starting component number, target component number, relationship type, relationship level, high-level hierarchy order, local action section location, relationship strength, and the construction stage code, functional category code, and unique entity identifier of the corresponding component.

[0023] For example, in this embodiment, a strategy for obtaining ranking weights is provided, specifically as follows: ; In the formula, For sorting weights, The larger the value, the earlier the relation expression record appears in the unified spatial relation expression sequence; Score the strength of the relationship. For relational levels, , Let i and j be the confidence levels of the representations of components i and j, respectively. , These are the construction stage codes for components i and j, respectively; , , These are all adjustment constants, with values ​​ranging from 0.10 to 0.50, 0.20 to 0.80, and 0.10 to 0.60, respectively. It should be noted that, in this embodiment, each component-level entity in the component relationship set can query its list of superior, subordinate, traversed, superimposed, and interleaved adjacent objects; The component relationship set contains multiple elements ,element This represents the association between the u-th component entity and the v-th relation record; where u represents the component number and v represents the sequence number of a related relation record; when When expanded, it must contain at least the associated object number, relation type code, relation level, high-level hierarchy order, and local segment index; S3: Based on the component relationship set, through candidate association pair screening, fine semantic matching and chain segment splicing, a local evolution chain segment reflecting the historical changes of components is constructed and standardized to form a cross-stage component evolution path expression model; S3 includes: S31: The component relationship set is hierarchically divided according to the construction stage, with the unique entity identifier as the main index; the candidate association score is calculated to quantify the candidate inheritance probability, and the candidate association pair screening is performed between adjacent stages to form a stage candidate association pair set.

[0024] For example, in this embodiment, the set of component relationships is layered according to the construction stage, including: existing stage layer, demolition and modification stage layer, new stage layer and replacement stage layer; Then, using the unique entity identifier as the primary index and spatial scope, relation type, functional category, and high-level hierarchy as secondary constraints, candidate association pairs are filtered between adjacent stages: First, limit the scope to the same local spatial cluster, then check whether the functional categories are consistent or inheritable, and finally check whether the adjacency patterns of the two components in the component relationship set are similar. It should be noted that in this embodiment, the division radius of the spatial cluster ranges from 1 to 8m; the main water supply and drainage trunk line and similar branch lines can be weakly inherited, while the power cable tray and communication cable tray cannot be directly inherited. For example, in this embodiment, a strategy for obtaining candidate association scores is provided, specifically as follows: ; In the formula, Scoring candidate associations The larger the value, the higher the likelihood of cross-stage inheritance; The distance between the geometric centers of the local action zones of the two components; The geometrical attenuation constant has a value range of 0.20-2.00; , The total number of adjacency terms for components i and j. The number of adjacency terms that can correspond to components i and j; This is the association enhancement index, with a value range of 1.2-2.8; , Code the function category. , Code for the construction phase; Then, when a component-level entity meets the above conditions, a candidate association pair is formed; It should also be noted that in this embodiment, this step first filters out cross-stage components that may have inheritance relationships, narrowing the scope for refined semantic matching; without this step, a large number of intersecting components in the underground integrated pipe gallery would cause confusion in the historical tracing chain, and large-scale misconnection would occur in the subsequent evolution path. S3 also includes: S32: Based on the set of candidate association pairs for each stage, the semantic matching strength is calculated by comprehensively considering four dimensions: relational structure similarity, spatial inheritance stability, stage transition rationality, and boundary change tolerance. Then, fine semantic matching is performed on the candidate association pairs to output the set of semantic matching units for each stage. For example, in this embodiment, a relational structure similarity is provided. The acquisition strategy is as follows: ; In the formula, , The number of relational terms between the two components in their respective stages; The number of relational items that can be matched one-to-one; , These are the hierarchical position numbers of the two components in their respective stages; This is the hierarchical attenuation constant, and its value ranges from 1 to 5; Provide a spatial inheritance stability The acquisition strategy is as follows: ; In the formula, , These are the effective continuous lengths of components i and j, respectively; This is the length attenuation constant, with a value ranging from 0.50 to 10.00; , Let i and j be the number of interfaces, respectively. This is the interface attenuation constant, and its value ranges from 1 to 6. For example, in this embodiment, a strategy for obtaining semantic matching strength is provided, specifically as follows: ; In the formula, Let be the semantic matching strength between component i and component j; For relational structure similarity, For spatial inheritance stability, The rationality coefficient for stage transition is obtained by looking up a table according to the preset engineering change rules, and its value ranges from 0 to 1. The boundary variation tolerance coefficient is obtained by combining the length change rate, cross-sectional change rate and interface change rate, and its value ranges from 0 to 1. , , , These are the corresponding weighting coefficients, with values ​​ranging from 0.30-0.45, 0.20-0.35, 0.15-0.25, and 0.10-0.20, respectively. , , , The sum is 1; It should be noted that, in this embodiment, the relational structure similarity indicates whether the combination forms of the superior connection, subordinate connection, traversal object, superimposed object, and intersecting object of two components in their respective stage relation sets are similar; the spatial inheritance stability indicates whether the functional sections of the two components continue along the same main control channel, rather than being replaced by adjacent heterogeneous components; the stage transition rationality indicates whether the transition from the existing stage to the demolition and renovation stage, and from the demolition and renovation stage to the new stage, conforms to the preset engineering change logic; the entity boundary change tolerance indicates that certain length changes, interface changes, and cross-sectional changes are allowed for components during renovation and expansion, but they cannot exceed the professional category and main function boundaries; Finally, when the semantic matching strength If the candidate association pair is greater than or equal to the preset matching threshold, then the candidate association pair is retained as a stage semantic matching unit. The matching threshold ranges from 0.6 to 0.85; it should be noted that in this embodiment, a high threshold is used for the main pipeline and a medium to low threshold is used for the auxiliary support. S33: Based on the set of stage semantic matching units, the stage semantic matching units are spliced ​​together according to the principles of high matching priority, single meaning inheritance priority, and stage continuity priority to form local evolution chain segments, and the chain segment with the largest cumulative evolution gain is retained to form a set of local evolution chain segments. It should be noted that, in this embodiment, "high matching priority" means retaining component pairs with higher matching strength first; "single inheritance priority" means avoiding mapping an old component to multiple new components simultaneously in the same stage transition, except in the branch expansion scenario; "stage continuity priority" means that the construction stage encoding in the chain segment is progressively arranged in the evolution order. In addition, for cases involving one-to-many or many-to-one relationships, the type of evolution can be determined by combining the changes in the relationship type and the local spatial control segment; For example, in this embodiment, if an existing main drainage pipe is split into two sections and an additional branch pipe is added during the renovation and expansion, it is judged as a split-type evolution; if two old communication supports are merged into an integrated support after renovation, it is judged as a merging-type evolution. For example, in this embodiment, a strategy for obtaining cumulative evolutionary gain is provided, specifically as follows: ; In the formula, Let r be the cumulative evolutionary gain of the r-th candidate chain segment. The larger the value, the more worthwhile the evolutionary segment is to be preserved; It is the sum of the semantic matching strengths of the matching units in each adjacent stage of the chain segment. For chain segment penalty terms; Provide a chain segment penalty item The acquisition strategy is as follows: ; In the formula, Penalty for skipping stages; The number of one-to-many or many-to-one joins is penalized for splitting or merging complexity. Punishment for relationship conflicts; , , These are the corresponding penalty coefficients, with values ​​ranging from 0.20 to 0.60, 0.15 to 0.50, and 0.20 to 0.70, respectively. It should also be noted that, in this embodiment, this step assembles discrete cross-stage matching units into readable, searchable, and extendable local evolutionary chain segments; semantic matching is essentially converted into an evolutionary path; S34: Based on the set of local evolution chain segments, all local evolution chain segments are uniformly arranged according to component category, spatial cluster and stage order, stage node normalization and path relationship normalization are performed, and the final credibility of the path is calculated to obtain the cross-stage component evolution path expression model.

[0025] The standardized stage node means compressing stage nodes from different sources and with different granularities into a unified stage level. Specifically, it means merging local modifications, local replacements, and extended installations into traceable standard stage nodes. The path relationship normalization specifically involves summarizing the traversal, overlay, and interleaving relationships at each stage node into a unified relationship signature. For example, in this embodiment, a strategy for obtaining path cumulative credibility is provided, specifically as follows: ; In the formula, Let be the final credibility of the r-th evolutionary path. The higher the value, the better the path represents the true and continuous evolution of the component from its history to its current state; This represents the cumulative evolutionary gain of the path. This represents the path stage coverage, and its value ranges from 0 to 1. This represents the continuity of the relation, and its value ranges from 0 to 1. This is the abnormal interruption factor, and its value ranges from 0 to 1; , , These are the corresponding adjustment constants, with values ​​ranging from 0.20 to 0.80, 0.20 to 0.80, and 0.30 to 1.00, respectively. According to the final credibility of the evolutionary path The output is sorted to represent the evolution path of components across stages, and the index links between each evolution path, the original component number, and the relationship record are preserved. The cross-stage component evolution path expression model Y contains multiple elements. ,element Let represent the standardized expression of the a-th evolutionary path at the b-th stage node; where 'a' represents the path number and 'b' represents the stage node number; when When expanding, it should at least include the component number, function category, relationship signature, path local credibility, and connection type with the next stage node corresponding to the current stage node; It should be noted that each evolution path must include at least: path number, initial stage component number, final stage component number, intermediate stage node sequence, summary of the relationship structure of each stage, path type identifier, and final credibility of the evolution path; S4: Based on the cross-stage component evolution path expression model, the cross-stage component evolution path is integrated into a continuous spatial semantic fragment through unified spatial mapping, fusion weight calculation, spatial aggregation and consistency verification, resulting in a continuous spatial semantic expression model covering the entire life cycle.

[0026] S4 includes: S41: Based on the cross-stage component evolution path expression model, extract the nodes of each stage in the evolution path and project them onto a unified spatial reference frame. According to the path connection type, convert them into a continuous spatial mapping main line to form a unified spatial mapping primitive set.

[0027] For example, in this embodiment, the CGCS2000 National Geodetic Coordinate System is used as the spatial reference, and the Gauss-Kruger projection is used to convert the three-dimensional spatial coordinates into planar coordinates. The central meridian of the projection is selected according to the geographical location of the project. It should be noted that in this embodiment, the division is based on 3-degree zones or 6-degree zones; the elevation direction adopts the normal height system and is unified to the 1985 National Height Datum; for cases where the original data uses other coordinate systems (such as local independent coordinate systems, WGS84 geographic coordinate systems, BIM design coordinate systems), spatial rectangular coordinate transformation is performed through a seven-parameter Bursa model. The transformation parameters are calibrated based on the measured data of no less than three evenly distributed control points in the project area, and the seven parameters are solved using the least squares method. For scenarios that only require planar transformation and have low elevation accuracy requirements, a simplified method of four-parameter planar fitting plus elevation fitting can be used; all transformation processes retain the metadata records of the original data to ensure the traceability and reversibility of coordinate transformation, and finally unify the spatial positioning of all component-level entities under the same spatial reference frame; The metadata record includes: original coordinate system identifier, transformation parameter version, and transformation date; The path connection type is determined as follows: by calculating the spatial continuity coefficient, geometric change rate, and interface matching degree between adjacent stage nodes, the evolution relationship is divided into four types: retention followed by extension, truncation followed by replacement, splitting, and merging. When the spatial continuity coefficient is higher than a preset threshold and the geometric change rate is lower than a change threshold, it is determined to be the retention followed by extension type. When the spatial continuity coefficient is lower than the preset continuity threshold and the geometric change rate exceeds the change threshold, it is determined to be the truncation followed by replacement type. When a single old component is mapped to multiple new components in adjacent stages and the number of interfaces increases, it is determined to be the splitting type. When multiple old components are merged into a new component in adjacent stages and the number of interfaces decreases, it is determined to be the merging type. Among them, the spatial continuity coefficient is calculated based on the geometric center distance, the included angle of the main direction and the rate of change of cross-sectional dimensions between adjacent stage nodes. The geometric change rate reflects the length change and cross-sectional change of the component during the renovation and expansion process. The interface matching degree is determined based on the change in the number of connection ports and the spatial correspondence. The conversion into a continuous spatial mapping mainline specifically involves: first, using the main control component segment of the existing stage in the path as the starting reference for the entire mainline; then, following the stage sequence of the evolution path, reading the spatial position, relation signature, and path connection type of each stage node, and converting it according to different rules, specifically: For paths that are extended after retention, the newly added segments are appended to the end of the original path; for paths that are replaced after truncation, a restricted transition segment is established between the end of the old component and the start of the new component; for split or merged paths, the hierarchical markers of the main path and the branch path are retained in the unified spatial reference frame. To prevent excessive local offsets during the renovation and expansion process from causing abrupt changes in the mapping main line, the range of the section smoothing length parameter is set to 0.3-3.0m; for transition sections, the range of the minimum effective connection length is set to 0.10-1.50m. By calculating a unified mapping scale, the principal mapping scale of the path in a unified space is quantized: ; In the formula, Let be the uniform mapping scale for the r-th evolutionary path; This represents the number of stages / segments contained in the path. For the r-th evolutionary path in the th... The effective continuous length of each stage segment; This refers to the inheritable extension length generated during the reconstruction and expansion of this section; , These are the center elevation of the section and the center elevation of the benchmark section, respectively. The elevation balance constant has a value range of 0.20-2.50; , These are the main direction angles of the section and the main direction angle of the reference section; The directional equilibrium constant has a value range of 0.15-0.80. A unique mapping main line is formed that is spatially continuous and semantically interpretable from history to the present; ultimately, each evolutionary path is transformed into a set of continuous, uniformly sortable and fused spatial mapping primitives, forming a unified set of spatial mapping primitives as output. It should be noted that each spatial mapping primitive in the unified spatial mapping primitive set corresponds to a standardized mapping unit of an evolution path under the unified spatial reference frame; S4 also includes: S42: For each spatial mapping primitive in the unified spatial mapping primitive set, extract the final credibility of its path and calculate the fusion weight by combining stage coverage, relation continuity and current status validity, and output the path fusion weight set; It should be noted that in this embodiment, even if the same component belongs to the evolution path with high final credibility in the underground integrated pipe gallery renovation and expansion scenario, it may be in a state of historical residue but current failure or current addition but historical non-coverage in some sections. Therefore, the weight should be calculated by integrating the final credibility, stage coverage, relationship continuity and current validity to generate the path fusion weight. For example, in this embodiment, a strategy for obtaining fusion weights is provided, specifically as follows: ; In the formula, Let be the fusion weight of the r-th spatial mapping primitive; The final credibility of the path; This represents the path stage coverage, and its value ranges from 0 to 1. This represents the continuity of the relationship, and its value ranges from 0 to 1. The current status validity coefficient has a value range of 0-1 and is obtained by the effective retention ratio of the mapping primitive in the current status structure. , , , These are the corresponding weighting coefficients, with values ​​ranging from 0.25-0.40, 0.20-0.30, 0.20-0.30, and 0.15-0.30, respectively. , , , The sum is 1; It should be noted that in this embodiment, if the fusion weight of a certain spatial mapping primitive is less than the elimination threshold, it will not enter the subsequent spatial semantic aggregation. The rejection threshold ranges from 0.35 to 0.55; S43: Under the unified spatial reference framework, spatial mapping primitives are fused and aggregated by introducing fusion semantic intensity according to spatial adjacency or path connection relationship, and continuous spatial semantic fragments are generated by using a sliding window method, and a set of continuous spatial semantic fragments is output. For example, in this embodiment, the sliding window length is 0.20-2.00m; the step size is 0.25-0.50 times the window length; The fusion and aggregation refers to combining local expression results from multiple stages, multiple sources, and multiple paths into a continuous spatial semantic segment. It should be noted that, in this embodiment, for homologous continuation type, continuous semantic segments are generated by weighting according to weight; for replacement transition type, transition semantic segments are generated between the termination point and the starting point, and replacement tags are attached; for parallel coexistence type, they cannot be forcibly merged into a single component semantics, but should generate composite segments containing main semantics and accompanying semantics. For example, in this embodiment, a fused semantic strength is provided. The acquisition strategy is as follows: ; In the formula, Let be the fused semantic strength of the p-th spatial window; To map the primitive fusion weights, Let be the effective coverage length of the r-th spatial mapping primitive in the p-th spatial window; This is the consistency coefficient between the spatial mapping primitive and the main semantic direction of the window; The strategy for obtaining the consistency coefficient between the spatial mapping primitive and the main semantic direction of the window is as follows: ; In the formula, , These are the basic average elevation and the window reference elevation, respectively. This is the elevation consistency constant, with a value ranging from 0.10 to 1.00; , These are the signature encoding values ​​for the primitive relation and the window master relation, respectively. This is the consistency constant for the relation, and its value ranges from 1 to 5. It should be noted that in this embodiment, if the number of spatial mapping primitives participating in the fusion and aggregation within the sliding window is too small, a neighboring window borrowing mechanism can be used, but the borrowing distance shall not exceed 1.50m; It should also be noted that in this embodiment, this step achieves semantic fusion across stages, paths, and local sections, so that the existing structural semantics and current structural semantics in the underground integrated utility tunnel are no longer independent of each other, but form a continuous and queryable spatial semantic fragment. S44: Perform global arrangement on a continuous set of semantic segments, calculate the consistency value of each segment, and then perform consistency checks and merge adjacent segments to obtain multiple global semantic segments. Simultaneously, the global consistency strength of each global semantic segment is calculated, ultimately forming a continuous spatial semantic expression model and a lifetime-long consistent fusion mapping result output.

[0028] For example, in this embodiment, semantic segments are first classified and attached according to spatial levels such as main channel, branch channel, node well chamber, and interface transfer area. Then, a global semantic connection chain is established according to the adjacency order, replacement relationship, coexistence relationship and hierarchical relationship between continuous spatial semantic segments. Finally, the source stage, component path, relationship type and current status of each continuous spatial semantic segment are written into the unified expression result. Next, a consistency check is performed on adjacent continuous spatial semantic segments: if adjacent continuous spatial semantic segments are continuous in spatial location, have the same relation signature, and have continuous source paths, they are merged into a global semantic segment; if they are continuous in spatial location but have significant changes in semantic attributes, the breakpoints are retained and marked as semantic boundaries. For example, in this embodiment, a strategy for obtaining a consistency value is provided, specifically as follows: ; In the formula, Let be the consistency value of the p-th continuous spatial semantic segment; The fusion semantic strength of the fragments; The path continuity coefficient; This is the stability coefficient of the relationship; The semantic mutation factor indicates the degree to which there are replacement breakpoints, parallel conflicts, or unexplained jumps within the segment, and its value ranges from 0 to 1. , , These are the corresponding adjustment constants, with values ​​ranging from 0.20 to 0.80, 0.20 to 0.80, and 0.30 to 1.00, respectively. A strategy for obtaining global consistency strength is also provided, specifically: ; In the formula, This represents the sum of the consistency values ​​of all segments belonging to this semantic segment. The number of semantic boundaries reserved within a semantic segment; It should be noted that the continuous space semantic representation model Z contains multiple elements. ,element This represents the standardized result of the u-th global semantic segment on the v-th semantic attribute dimension; where u represents the global semantic segment number and v represents the attribute sequence number; when v corresponds to the lifecycle state, This indicates that the semantic segment belongs to one of the following states: retention, replacement, extension, coexistence, or obsolescence; when v corresponds to the validity of the current state, This indicates the degree to which the segment is effectively preserved in the existing structure; when v corresponds to an index link, The component-level entity number and path number can be retrieved in reverse; It should also be noted that, in this embodiment, this step transforms the component evolution path expression model into a continuous spatial semantic expression model covering the entire process of underground integrated pipe gallery renovation and expansion, thereby realizing the consistent fusion mapping of multi-source heterogeneous data throughout the entire life cycle under the condition of concealed engineering. Example 2:

[0029] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project by calling computer programs stored in memory.

[0030] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the multi-source heterogeneous data fusion mapping method provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0031] Example 3:

[0032] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored. When a computer program runs on a computer device, it causes the computer device to execute the above-mentioned method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project.

[0033] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0034] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0035] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0036] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project, characterized in that, The method includes: S1: Establish a unified entity identification coding rule through a multi-source engineering information set, transform engineering information at different stages into a set of identification components, and construct an initial entity representation matrix; S2: Based on the initial entity representation matrix, through candidate relation screening and local topological measure calculation, the traversal, overlapping and intersecting relations are classified and identified, and each hierarchical spatial relation unit is converted into a standard relation representation record to form a set of component relations with topological semantics; S3: Based on the component relationship set, through candidate association pair screening, refined semantic matching, and chain segment splicing, a local evolution chain segment reflecting the historical changes of components is constructed and standardized to form a cross-stage component evolution path expression model; S3 includes: S31: The component relationship set is hierarchically divided according to the construction stage, with the unique entity identifier as the main index; the candidate association score is calculated to quantify the candidate inheritance probability, and the candidate association pair is screened between adjacent stages to form a stage candidate association pair set; S32: Based on the set of candidate association pairs for each stage, the semantic matching strength is calculated by comprehensively considering four dimensions: relational structure similarity, spatial inheritance stability, stage transition rationality, and boundary change tolerance. Then, fine semantic matching is performed on the candidate association pairs to output the set of semantic matching units for each stage. S33: Based on the set of stage semantic matching units, the stage semantic matching units are spliced ​​together according to the principles of high matching priority, single meaning inheritance priority, and stage continuity priority to form local evolution chain segments, and the chain segment with the largest cumulative evolution gain is retained to form a set of local evolution chain segments. S34: Based on the set of local evolution chain segments, all local evolution chain segments are arranged in a unified manner according to component category, spatial cluster and stage order, stage node normalization and path relationship normalization are performed, and the final credibility of the path is calculated to obtain the cross-stage component evolution path expression model. S4: Based on the cross-stage component evolution path expression model, the cross-stage component evolution path is integrated into a continuous spatial semantic fragment through unified spatial mapping, fusion weight calculation, spatial aggregation and consistency verification, resulting in a continuous spatial semantic expression model covering the entire life cycle.

2. The method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project, as described in claim 1, is characterized in that... S1 includes: S11: Unify the coordinates of the multi-source engineering information set and convert all structural boundaries into three-dimensional boundary segments; set the cutting points at the abrupt change locations, define the local structural segments between adjacent cutting points as candidate component segments, and calculate the spatial continuity coefficient of their adjacent segments to obtain the set of candidate component segments. S12: Based on the candidate component fragment set, perform component-level merging judgment according to the spatial continuity coefficient and functional consistency coefficient, merge the fragments that meet the conditions into complete component-level entities, and form a component-level entity set; S13: Extract the spatial location, functional classification, stage affiliation and boundary morphology features of each component-level entity in the component-level entity set, generate a unique entity identifier according to the unified coding rules, and output the identifier component set with the unique entity identifier. S14: Construct the initial entity representation matrix by using the set of identifier components as row objects and the representation features of the components as column objects.

3. The method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project according to claim 2, characterized in that, S2 include: S21: Read the spatial center position, main direction and equivalent cross-sectional size of all component-level entities in the initial entity representation matrix, and establish a three-dimensional neighborhood search framework; by calculating the candidate proximity of component pairs, filter out component pairs with crossing, overlapping or intersecting relationships, and form a set of candidate spatial relationship pairs; S22: For each pair of components in the candidate spatial relationship pair set, perform discrete sampling along its geometry, calculate its equivalent minimum spacing, local projection overlap ratio, directional cross index and high-level difference, and form a set of local topological contact features.

4. The method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project according to claim 3, characterized in that, S2 also includes: S23: Based on the local topological contact feature set, determine the relationship category of the component pairs, classify the relationship level and determine the order of the high-level level to form a hierarchical spatial relationship unit, and output the hierarchical spatial relationship unit set simultaneously; The hierarchical spatial relationship unit includes: relationship category, high-level hierarchical order, and relationship strength; S24: Convert each hierarchical spatial relation unit in the hierarchical spatial relation unit set into a standard relation expression record, sort the standard relation expression records by priority through sorting weight, generate a unified spatial relation expression sequence, and summarize it into a component relation set with topological semantics; The component relationship set includes: starting component number, target component number, relationship type, relationship level, high-level hierarchy order, local action section location, relationship strength, and the construction stage code, functional category code, and unique entity identifier of the corresponding component.

5. The method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project according to claim 1, characterized in that, S4 includes: S41: Based on the cross-stage component evolution path expression model, extract the nodes of each stage in the evolution path and project them onto a unified spatial reference frame. According to the path connection type, convert them into a continuous spatial mapping main line to form a unified spatial mapping primitive set.

6. The method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project, as described in claim 5, is characterized in that... S4 also includes: S42: For each spatial mapping primitive in the unified spatial mapping primitive set, extract the final credibility of its path and calculate the fusion weight by combining stage coverage, relation continuity and current status validity, and output the path fusion weight set; S43: Under the unified spatial reference framework, spatial mapping primitives are fused and aggregated by introducing fusion semantic intensity according to spatial adjacency or path connection relationship, and continuous spatial semantic fragments are generated by using a sliding window method, and a set of continuous spatial semantic fragments is output. S44: Perform global arrangement on a continuous set of semantic segments, calculate the consistency value of each segment, and then perform consistency checks and merge adjacent segments to obtain multiple global semantic segments; Simultaneously, the global consistency strength of each global semantic segment is calculated, ultimately forming a continuous spatial semantic expression model and a lifetime-long consistent fusion mapping result output.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for fusing and mapping multi-source heterogeneous data throughout the entire lifecycle of an engineering project, as described in any one of claims 1-6.

8. An electronic device, characterized in that, include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform operations that implement the method for fusing and mapping multi-source heterogeneous data throughout the engineering lifecycle as described in any one of claims 1-6.

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