Method for automatically detecting spatial conflicts of cable tray and pipeline based on three-dimensional model

Abstract

The present application relates to the technical field of electric power engineering, specifically is cable bridge and pipeline space conflict automatic detection method based on three-dimensional model, including: obtaining the three-dimensional model corresponding to cable bridge and pipeline, setting conflict area for cable bridge and pipeline;With the space boundary of the current conflict area as the basis for retrieval, combined with the conflict area detected last time, determine the conflict risk coefficient affected by conflict frequency, conflict type and spatial coincidence degree;With the conflict risk coefficient as the screening value, screen each conflict area to determine the output target area;When the target area meets the triggering condition of secondary inspection, determine the spatial sensitivity coefficient of the target area according to the geometric characteristics of the corresponding components of cable bridge and pipeline in each detection period;Based on the spatial sensitivity coefficient and conflict risk coefficient of the target area, determine the detection result of each detection period. Improve the efficiency and accuracy of conflict detection.

Description

Technical Field

[0001] This invention relates to the field of power engineering technology, specifically to an automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model. Background Technology

[0002] When laying out cable trays and pipelines in confined spaces such as power engineering manholes and mezzanines, the rationality of the spatial layout directly affects the feasibility of construction and the safety of subsequent operation and maintenance. Existing technologies generally use geometric intersection to perform collision detection, but this approach has problems such as limited screening dimensions, crude risk quantification, and difficulty in regional analysis.

[0003] For example, Chinese Patent Publication No. CN120781430A discloses a method and system for detecting and adjusting mechanical and electrical pipeline conflicts based on a BIM model, belonging to the field of mechanical and electrical installation. Its technical solution is as follows: parsing the BIM model to generate component geometric parameters and generating mechanical and electrical pipeline conflict data; automatically classifying conflict types based on the conflict data and calculating conflict ranking data; calling a knowledge rule engine based on the conflict type and ranking data to match conflict adjustment strategies; executing pipeline position adjustment actions and synchronously updating the BIM model data; recording the adjusted conflict handling results in a case library, and optimizing and updating the knowledge rule engine based on the case library data.

[0004] For example, Chinese Patent Publication No. CN121052130A discloses an intelligent detection method and system for pipeline intersection conflicts at user stations, including: S1: collecting CAD structural drawing data, CAD electrical drawing data, and BIM data, and preprocessing them to obtain preprocessed structural drawing data, preprocessed electrical drawing data, and a BIM model; S2: extracting the set of structural elements and the set of electrical elements, designing a hole potential energy generation network, calculating the axial continuity metric value and the semantic potential energy value of the wiring hole, and then constructing the structural diagram model and the electrical diagram model; S3: designing a geometric relationship gating network and a semantic relationship attention network respectively, determining conflicting structural elements and conflicting electrical elements, and specially marking the conflicting elements; S4: generating a table of conflicting component information, taking screenshots, and generating an examination report.

[0005] Existing technologies classify conflicts by type and update pipelines according to predefined rules; or they handle pipeline conflicts by geometric probability distribution and semantic conflict. Existing technologies tend to process outputs from discrete points, which makes it difficult to identify structural conflicts that recur over a long period of time, resulting in no scene distinction in the results and making it difficult to understand the rectification process. Furthermore, they cannot correspond to the changing trends of pipeline segments along their direction, resulting in the inability to identify overall structural conflicts and ultimately making conflict detection unable to adapt to the dynamic changes of the project. Summary of the Invention

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an automatic detection method for spatial conflict between cable trays and pipelines based on three-dimensional models, including: S1, obtaining three-dimensional models corresponding to cable trays and pipelines, and setting conflict areas for cable trays and pipelines.

[0007] S2 uses the spatial boundary of the current conflict area as the basis for retrieval, and combines it with the conflict area detected in the previous test to determine the conflict risk coefficient affected by conflict frequency, conflict type and spatial overlap.

[0008] S3 treats each conflict area as a risk point, connects the conflict areas along the installation direction to form a risk-related path, constructs a gradient range of the conflict risk coefficient using the conflict risk coefficient as the screening value, and filters the output target area based on the number of points on the risk-related path.

[0009] S4. When the target area meets the triggering conditions for secondary inspection, the spatial sensitivity coefficient of the target area is determined based on the geometric characteristics of the corresponding components of the cable tray and pipeline in each inspection cycle.

[0010] S5 extracts duplicate conflict samples from different detection cycles based on the spatial sensitivity coefficient and conflict risk coefficient of the target area, and determines the detection result of each detection cycle based on the number of occurrences of the duplicate conflict samples.

[0011] The beneficial effects of this invention are as follows: First, by collecting design drawings and 3D models, this invention establishes a coordinate system for cable trays and pipelines, recording the unified geometric spatial positions; it extracts the pipeline installation scene and pipeline type as constraints, and trims the space to form an initial detection area; within the initial detection area, it calculates the spatial distance between the cable trays and pipelines, determines adjacency relationships, and selects areas where the shortest distance is less than the safety distance as the final conflict area. Through the first round of spatial range trimming, invalid detection areas with no adjacency relationships and sufficient safety margins are eliminated, avoiding excessive computing resources occupied by large-scale global geometric calculations, improving the efficiency of periodic rapid detection, and reserving computing space for subsequent refined detection.

[0012] Second, this invention calculates the spatial overlap between the current conflict area and the previous period's area using bounding boxes, and quantifies the conflict risk coefficient for each period by combining the frequency coefficient of historically occurring conflicts and the weight of conflict types. It also corrects the conflict risk coefficient by incorporating the change type and volume of components within the area. This avoids ignoring the inertial patterns of historical conflicts and the risk fluctuations caused by layout changes during single-period conflict detection. Furthermore, it expands the risk assessment dimension beyond a single geometric distance determination, making this multi-dimensional risk assessment method more closely aligned with engineering implementation scenarios and further improving the accuracy of risk prediction.

[0013] Third, this invention abstracts discrete conflict areas into risk points, which are then sequentially arranged along the installation routes of pipelines and cable trays to form risk-related paths. Based on the risk coefficients of each point on the path, the gradient of risk distribution along the spatial distribution is calculated, dividing the risk into two categories: gradient increase and gradient decrease. Using the historical average conflict risk coefficient of the corresponding component as a benchmark, points with risk values ​​higher than the baseline risk coefficient are selected, ultimately forming the target area. By identifying continuously distributed risk points on pipelines, the risk identification can be further aligned with the actual pipe segment locations in the project, improving the foresight of risk identification.

[0014] Fourth, this invention establishes a secondary inspection mechanism that is activated on demand, which enables refined identification of the geometric features of components, determines the geometric sensitivity of abrupt changes in component shape and the influence of cross-sectional area, and determines the spatial constraints under different installation scenarios; it avoids missed detections in large cross-section straight sections and dense scenarios, making the test results closely match the actual engineering situation; at the same time, it determines repeated conflict samples based on cross-cycle intersection, making the multi-cycle inspection process more interpretable and the overall processing efficiency better. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] Figure 1 This is a flowchart illustrating an automatic detection method for spatial conflicts between cable trays and pipelines based on a 3D model. Figure 2 This is a flowchart illustrating step S1 of the automatic detection method for spatial conflicts between cable trays and pipelines based on a 3D model. Figure 3 This is a flowchart illustrating step S2 of the automatic detection method for spatial conflicts between cable trays and pipelines based on a 3D model. Figure 4 This is a flowchart illustrating step S3 of the automatic detection method for spatial conflicts between cable trays and pipelines based on a 3D model. Figure 5 This is a flowchart illustrating step S4 of the automatic detection method for spatial conflicts between cable trays and pipelines based on a 3D model. Figure 6 This is a flowchart illustrating step S5 of the automatic detection method for spatial conflicts between cable trays and pipelines based on a 3D model. Figure 7 This is a schematic diagram of the conflict risk at different periods of a three-dimensional model-based automatic detection method for conflicts between cable trays and pipelines. Figure 8 This is a risk correlation diagram showing the risk associated with the path gradient of an automatic detection method for spatial conflicts between cable trays and pipelines based on a 3D model. Detailed Implementation

[0017] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, they shall be performed in accordance with the techniques or conditions described in the literature in the art or in accordance with the product manual.

[0018] See Figure 1 The implementation of the present invention includes S1 to S5: S1, obtaining a three-dimensional model corresponding to the cable tray and pipeline, and setting a conflict area for the cable tray and pipeline.

[0019] For the deployed 3D model, the spatial location of the cable trays and pipelines needs to be combined into multiple points according to the data stored in each cycle, based on the data entered each time, in order to support the subsequent historical data matching and the identification of conflicts between points.

[0020] When obtaining the 3D models corresponding to cable trays and pipelines, the process includes: collecting the design drawing data and 3D models corresponding to the cable trays and pipelines; unifying the coordinates of the cable trays and pipelines based on the design drawing data and 3D models, and recording the geometric spatial positions after coordinate unification. This unified coordinate data serves as the data basis for the initial inspection, and the distances and bounding boxes corresponding to their geometric spatial positions are used as the data basis for determining safety distances.

[0021] In one embodiment of the present invention, the pipeline layout scenario and pipeline type are used as constraints to compare the cable trays around the pipeline to determine the areas where conflicts need to be detected, as well as the spatial distance for each area to be judged.

[0022] like Figure 2 As shown, one implementation of step S1 includes: S11, extracting the installation scene and pipeline type of the pipeline from the 3D model, and using the installation scene and pipeline type of the pipeline as constraints to perform spatial clipping to form an initial detection area.

[0023] In this example, the conflict zone is used to identify conflicts when pipelines and cable trays are too close. When the distance between the cable tray and the pipeline is less than the safe distance, the relevant data for the cable tray and pipeline are aggregated in the conflict zone. The safe distance is affected by the pipeline type and installation scenario. Each time the safe distance is obtained, it is necessary to check the value of the safe distance from the database according to the constraints of the current scenario.

[0024] In one scenario, the safe distance between fire ducts and cable trays in basement utility tunnels should be no less than 0.3m, and the safe distance between air supply ducts and cable trays in indoor suspended ceilings should be no less than 0.2m. In confined spaces such as pipe shafts and mezzanines, the safe distance should be increased by 50%. Simultaneously, due to the influence of different cable tray types, the currently obtained safe distances should be further adjusted according to the cable tray type. For example, the safe distance for high-voltage power cable trays should be increased by 30% compared to low-voltage cable trays, while the safe distance for low-voltage cable trays can be decreased by 10%. The set safe distances are further constrained according to the type of cable tray to obtain a safe distance that conforms to the judgment of the current scenario.

[0025] Specifically, most pipelines are fixed installations due to their type. However, cable trays are more flexible than pipelines. By considering the spatial constraints of pipeline installation, cable trays that conflict with pipelines can be identified, thus clarifying the inspection areas for different installation scenarios. Pipeline installation types include fire hydrants, drainage pipes, air supply pipes, cooling water pipes, and many others. Their installation scenarios are also influenced by the pipeline type, requiring different safety distances.

[0026] For each defined initial detection area, the attributes corresponding to the installation scene and the pipeline type are first combined and bound. Then, the preset clipping rules corresponding to each attribute combination are obtained. According to the clipping rules, a bounding box is generated by extending outward along the pipeline. The extension distance is the safety distance corresponding to the current pipeline. The area corresponding to the bounding box is the initial detection area. Each attribute combination has a different clipping method. For example, for fire pipes in basement pipe corridors, a bounding box is generated by extending outward based on their centerline; for air supply pipes in indoor ceilings, a bounding box is generated by extending outward based on their cross-sectional outline. The initial detection area needs to be clipped and generated according to the extension method of different pipelines, such as their outline or centerline.

[0027] S12, in each initial detection area, calculate the spatial distance between the cable tray and the pipeline to determine the adjacency relationship between the cable tray and the pipeline.

[0028] Regarding the adjacency relationship between cable trays and pipelines, this relationship is used to indicate the situation where the spatial positions of pipelines and cable trays are close to each other in the initial detection area, including adjacency relationships such as excessively close spacing and through overlap. These adjacency relationships are used to fill in the description of conflict types. At the same time, the adjacency relationship also records the spatial position between the flanges, valves, supports, and other components corresponding to the pipelines and the cable trays. The distance is calculated based on the enclosure formed by these components and the pipelines and the enclosure of the cable trays, thereby completing the recording of spatial distance.

[0029] S13. Utilize the adjacency relationship between cable trays and pipelines to filter initial detection areas where the shortest spatial distance is less than the safe distance, as the output conflict areas. If the shortest spatial distance is less than the safe distance, it indicates a conflict risk between the pipeline and cable tray at a specific location. It is necessary to compare each initial detection area with the corresponding part of each area, identifying areas with excessively close spacing or overlapping connections from the recorded adjacency relationships.

[0030] S2, using the spatial boundary of the current conflict area as the basis for retrieval, combined with the conflict area detected in the previous test, determines the conflict risk coefficient affected by conflict frequency, conflict type and spatial overlap. In one embodiment of the present invention, such as Figure 3 As shown, one implementation of step S2 includes: S21, for each detected conflict area, the spatial overlap between the current conflict area and the previous conflict area is calculated using a bounding box; wherein, the bounding box is a minimum convex polyhedron generated based on the three-dimensional coordinates of the pipelines and cable trays in the conflict area; the spatial overlap represents the intersection-union ratio of two consecutive conflict areas, representing the overlap of volume intersection and union, and the larger the value, the more obvious the overlap of pipelines / cable trays in the two detections.

[0031] S22. Based on the number of consecutive occurrences of the current conflict area in multiple detections, a conflict frequency coefficient is set for the current conflict area. When calculating the conflict frequency, the problematic pipelines and cable trays are taken as the main components. The cumulative number of conflicts occurring within all detection cycles in the historical data within the spatial range of the current conflict area is counted, and after normalization, the conflict frequency coefficient is obtained.

[0032] S23, assign type weights based on the conflict type of the current conflict area. The product of spatial overlap, conflict frequency coefficient, and type weight is considered as the output conflict risk coefficient. Conflict type provides a deeper interpretation of the conflict content, including entity penetration overlap, gas pipe / gravity flow pipe collision, large cross-section air duct collision, fire water pipe collision, etc. The range of type weight values ​​represents the degree of impact of pipeline and cable tray conflicts on building safety. When the conflict type is entity penetration overlap, it indicates a geometric logic error in the model, making on-site installation impossible, which is the most serious problem in conflict detection. When the conflict type is gas pipe / gravity flow pipe collision, it seriously affects building safety, creating a risk second only to the inability to install. When the conflict type is large cross-section air duct collision, it indicates that the installation volume of pipelines is large, and the adjustment cost is extremely high, making it the scenario where hidden problems are most likely to occur after pipeline installation. After clarifying the impact of different conflict types in the current scenario, the corresponding weight value of the type is extracted from the database based on the relative description of the current conflict type, thus completing the setting of type weights.

[0033] Based on the aforementioned determination of spatial overlap, conflict frequency coefficient, and type weight, a product weighting method is used to aggregate the conflict risk coefficient. Under this calculation logic, when any of the three parameters is at a high value, the final aggregated conflict risk coefficient will exhibit a non-linear steep increase characteristic, which can accurately amplify the risk level of highly sensitive conflicts. After the calculation is completed, the conflict risk coefficient needs to be globally normalized to uniformly map its value range to the 0-1 interval, eliminating the interference of size differences between different version models, and enabling horizontal comparison of conflict detection results after multiple rounds of model iteration and adjustment.

[0034] like Figure 7 As shown in the figure, six representative conflict areas are selected for display. Each broken line represents the risk change trajectory of an area over 10 cycles. Taking area 1 as an example, this area shows a continuous rise, indicating that the conflict between cable trays and pipelines at this location is continuously worsening. The corresponding location may be experiencing continuous pipeline compression or design iterations that exacerbate the conflict, making it a high-risk area requiring immediate intervention. In areas 6 and 11, the broken lines show a continuous fluctuation in risk, indicating that there is a structural and persistent spatial conflict in this area that cannot be eradicated by simple adjustments and requires a complete replanning and layout. Similarly, the rise followed by a fall in area 26 shows that after the pipeline treatment was carried out in the early stages of the conflict, the risk was significantly controlled. These conflict risks reflect the changing patterns of multiple areas in the current scenario, and it is necessary to combine the relative results of each detection to obtain the location of the conflict area for each time.

[0035] Furthermore, considering that spatial conflict is the core criterion for conflict area identification, this scheme performs enhanced assignment processing on spatial overlap: the calculation result of the original spatial overlap is superimposed with a constant 1. After superimposing the constant 1, it is uniformly mapped to the 0-1 interval in the conflict risk coefficient normalization process, which is used to amplify the risk weight of multiple overlapping areas. This makes the more significant the regional volume overlap feature in multiple detections, the stronger the amplification effect of this parameter, and the final output conflict risk coefficient value increases synchronously, accurately representing the cumulative risk of repeated conflicts in the same spatial area.

[0036] Furthermore, before outputting the conflict risk coefficient, the implementation method includes: extracting the components corresponding to cable trays and pipelines from the historical data of the current conflict area, determining the change type of the components, and the component density before and after the change; here, components refer to the changes of components such as flanges and valves in cable trays and pipelines. The identification scope is limited to the cable tray body, pipelines, and their attached flanges, valves, and other attached components. By comparing the component IDs and attribute differences between the two versions of the model, the three types of changes—addition, modification, and deletion—are determined for the corresponding components in the current conflict area, achieving full traceability of change behavior. Added components represent data parts without historical verification, with the highest probability of error; modified components represent operations such as component relocation, diameter change, and direction change, which are local adjustments during each model detection and are prone to causing conflicts in the surrounding area; deleted components are spatial adjustments, essentially reducing their own conflict risk, but it is still necessary to determine the operations of surrounding components during deletion; then, based on the number of component changes, the component change situation is synchronized to the conflict risk output process by quantifying the volume and quantity of components.

[0037] Based on the volume of components under various change types, a weighted summary is performed on all components within the current conflict area, and the change degree corresponding to the current conflict area is set. When weighting the summary of components under different component types, the components that are added, modified, and deleted in the model are obtained respectively, and the volume values ​​of these components are statistically calculated according to the differences between the two model tests. The change type is used as the basis for obtaining the weight, and the weight is set in sequence according to the impact of each construction type on the conflict risk.

[0038] Specifically, in one implementation scenario, weights of 1.2, 1.0, and 0.8 are assigned sequentially based on the order of addition, modification, and deletion. The volumes of the three change types are weighted and summed according to their respective weights. The resulting weighted value, after normalization, represents the degree of change in the current conflict area. In this scenario, even though deleting components is a risk-reduction approach, excessive deletion leading to significant changes indicates an unpredictable overall pipeline layout, potentially amplifying risks. Therefore, it is necessary to simultaneously verify the overall conflict risk under different change types based on the current weights.

[0039] Based on the ratio of the total volume of all components within the current conflict area to the volume of the corresponding three-dimensional space of the conflict area, the component density of the current conflict area is set. The component density is the ratio of the volume of the components within the conflict area to the volume of the corresponding three-dimensional space of the conflict area. The larger the value, the more crowded the space is, and the risk may increase synchronously with the space crowding.

[0040] Based on the degree of change and component density of the current conflict area, the conflict risk coefficient is adjusted, and the adjusted conflict risk coefficient is output.

[0041] Specifically, when using component density to calculate the total volume of components within the current conflict area, the sum of the degree of change and the component density is used as the adjustment ratio for the conflict risk coefficient. This allows the conflict risk coefficient to be effectively amplified or reduced, thus synchronizing the conflict risk under space occupancy conditions. In other words, without considering the space occupancy of components, the base multiple of the conflict risk coefficient is 1; when considering space occupancy, the base multiple is increased by adding the sum of the degree of change and the component density to amplify or reduce the conflict risk coefficient that needs to be normalized. The conflict risk coefficient adjusted using the degree of change will be globally normalized after the calculation is completed.

[0042] In one implementation scenario, if it is necessary to distinguish the impact of the degree of change and the density of components on conflict risk, weights for the degree of change and the density of components can be further introduced, with the weights of the degree of change and the density of components being 0.35 and 0.45 respectively. This approach emphasizes space occupancy while downplaying the degree of change, thus achieving an equivalent adjustment to conflict risk.

[0043] S3 treats each conflict area as a risk point, connects the conflict areas along the installation direction to form a risk-related path, constructs a gradient range of the conflict risk coefficient using the conflict risk coefficient as the screening value, and filters the output target area based on the number of points on the risk-related path.

[0044] Considering that conflict areas are often discretely distributed, the output conflict risk coefficients are severely discretized, making it impossible to form an executable processing list. This problem is addressed by abstracting each detected conflict area as a risk point fixed to a cable tray or pipeline. Using a single continuous pipeline or a single continuous cable tray segment as the basic unit, these segments are connected in series according to the installation direction of the cable tray / pipeline. All risk points are topologically grouped according to their respective components, forming multiple risk association paths to represent continuously distributed risk locations in the current installation environment. Then, a gradient interval along the pipeline / cable tray extension direction is determined. From this gradient interval, points with increasing risk and their corresponding relative conflict content are identified, completing the process from area discretization to aggregation. The aggregated conflict areas are then filtered for target areas based on the gradient values ​​and the values ​​of individual points.

[0045] In one embodiment of the present invention, such as Figure 4As shown, one implementation of step S3 includes: S31, abstracting the conflict area into risk points, and connecting the risk points in series along the installation direction to form a risk association path; this risk association path corresponds to the connection status of risk points on a single continuous pipeline or a single continuous cable tray. Taking the centerline of a single continuous pipeline or a single continuous cable tray as the reference path, the risk points are sorted and connected in ascending order of coordinates in the pipeline extension direction; when there are pipeline branches or cable tray branches, based on the main path, the branch points form separate sub-paths and are associated with the main branch branch nodes, and the associated risk points are output as risk connection paths for a single continuous pipeline or a single continuous cable tray according to the connection order.

[0046] S32. Based on the conflict risk coefficient of each risk point on the risk-related path, construct a gradient interval for the spatial variation of the conflict risk coefficient; whereby the gradient interval is used to represent the degree of risk diffusion and reduction. Taking the pipeline length between two adjacent risk points as the spatial step size, calculate the ratio of the difference in the conflict risk coefficients of the two points to the step size, which is taken as the risk gradient value of that path segment; a positive gradient value is marked as an ascending gradient interval, and a negative value is marked as a descending gradient interval.

[0047] S33. Filter target regions based on the gradient interval and the range of conflict risk coefficient values. The method for filtering target regions includes: when the current risk-related path corresponds to two or more risk points, distinguishing the risk points according to the intervals of gradient ascent and gradient descent.

[0048] When there are multiple risk points along the path, risk identification will focus on analyzing the gradient increase and gradient decrease intervals of the conflict risk coefficient along the path. The gradient increase interval represents the section where the risk increases rapidly in a short distance, corresponding to the scenario where spatial interference hazards are spreading rapidly along the extension direction of the component. The gradient decrease interval is the boundary section between the high-risk core area and the low-risk safe area, which is a key location that still needs to be reviewed and confirmed to be completely resolved after rectification.

[0049] If there is only one target point corresponding to the current risk-related path, the gradient verification process is skipped, and the point is directly screened according to the basic risk coefficient. The risk points with a risk coefficient greater than the basic risk coefficient are regarded as the output target areas.

[0050] When traversing each risk point along the risk-related path, risk points with conflict risk coefficients greater than the base risk coefficient are selected as the target area for output. The base risk coefficient is the average conflict risk coefficient of historical data; that is, the model prioritizes processing the portion exceeding the historical average each time, while the remaining portion is corrected through multiple iterations. If there is no historical data for the initial detection, the industry benchmark value of similar components in the same scenario is used as the initial base risk coefficient, which is then updated iteratively with each detection cycle.

[0051] For the identified risk points, the system iterates through the paths to which the risk points belong, and points with a risk coefficient greater than the basic risk coefficient are taken as the target area for output.

[0052] In the current processing, gradient partitioning is only used as a trend classification operation for points. It is to sort out the risk change patterns along the pipeline route and group and mark the points on the path according to risk changes. For the grouped and marked risk points, the threshold of conflict risk is checked in turn, and finally integrated into a target area containing trend description and absolute value.

[0053] S4. When the target area meets the triggering conditions for secondary inspection, the spatial sensitivity coefficient of the target area is determined based on the geometric characteristics of the corresponding components of the cable tray and pipeline in each inspection cycle.

[0054] In order to identify the areas requiring secondary verification from the acquired target area, we can examine the triggering conditions according to the trend description of the target area, find the descriptions of the triggering conditions, and then set a spatial sensitivity coefficient for the target areas that meet the conditions.

[0055] In one embodiment of the present invention, such as Figure 5 As shown, one implementation of step S4 includes: S41, when the current target area meets the triggering condition, extract components for each target area and determine the geometric features of the components; the geometric features include the positions of sensitive components with spatial morphological change attributes such as elbows, diameter changes, intersections, and large-section components, which are used to accurately compare the morphological change of a single point. The more significant the morphological change of a component with conflict risk, the higher the spatial sensitivity of the component, and the more stringent the spatial installation constraints that need to be followed.

[0056] The triggering conditions include: 1. A gradient increase along the installation direction, with corresponding risk points exceeding the basic risk coefficient. This area represents a dynamically deteriorating zone where potential hazards are expanding, requiring the identification of geometric weak points through spatial sensitivity coefficients to simultaneously improve detection accuracy; 2. A single risk point exceeding 1.2 times the basic risk coefficient. This is an abnormally high point exceeding the historical average. Regardless of the gradient direction, it is necessary to determine whether there is a real geometric conflict hazard; 3. Risk points where the gradient changes from rising to falling or vice versa, and the conflict risk coefficient exceeds the basic risk coefficient. This area represents locations of geometric abrupt changes such as pipeline bends, cable tray branches, and pipeline-cable tray intersections, which are sensitive points with a high incidence of conflict. The detection output results need to be determined in conjunction with actual physical level verification. A secondary verification is performed when any of the above triggering conditions are met; the remaining risk points remain unchanged within this cycle and are only used as remark data for conflict identification.

[0057] like Figure 8As shown, during gradient statistics, the conflict risk of each risk point will be affected by the value of its own conflict risk. Some risk points will have a higher risk coefficient than the basic risk coefficient, and some risk points will have differences that need to be eliminated. These risk points need to be determined according to the distance along the installation direction to identify the risk points that need to be checked again. Based on the geometric characteristics of these points, the risk identification and processing of pipeline conflicts in each area will be completed.

[0058] S42, based on the geometric characteristics of the component, assign geometric sensitivity to the shape mutation of the component; wherein, the geometric sensitivity is assigned a value to each type of geometric feature according to the collision probability of the shape mutation; the shape mutation matched in this scheme includes four levels: ordinary straight shape, single-dimensional shape mutation, multi-dimensional shape mutation and interactive mutation, and the geometric sensitivity weights corresponding to different levels are different, so as to realize the matching and processing of spatial constraints and component shape risks.

[0059] Secondly, the ordinary straight shape refers to the shape corresponding to straight pipe sections and straight cable tray sections. This shape has a uniform shape and the lowest probability of collision. The single-dimensional shape mutation corresponds to the single-dimensional turning changes such as pipe bends and cable tray turns. The overall shape shows a local expansion, and the probability of collision begins to increase significantly. The multi-dimensional shape mutation corresponds to the diverse shapes such as pipe tees / crosses and cable tray branches. The shape protrudes in multiple directions, and the probability of collision will be further increased. The interactive mutation corresponds to the scenarios of pipe and cable tray intersections and multiple pipe convergence points. It represents the spatial intersection of multiple components and has the highest probability of collision.

[0060] In one implementation, values ​​of 0.2, 0.5, 0.7, and 1.0 are assigned sequentially based on the order of ordinary flat shape, single-dimensional shape mutation, multi-dimensional shape mutation, and interactive mutation, serving as the base values ​​for spatial sensitivity analysis.

[0061] S43 extracts the cross-sectional dimensions from the geometric features of the component and combines them with the installation scenario of the component to aggregate the geometric sensitivity into the output spatial sensitivity coefficient. The spatial sensitivity coefficient is used to explain the spatial constraints caused by shape mutations, component cross-sectional dimensions, and installation scenario. The influence of component cross-sectional volume is superimposed on the shape mutation, and then the component is placed in the specific installation scenario. This allows each risk point to be traced and processed according to the shape mutation, cross-sectional size, and scenario space, making the direction of pipeline conflict rectification clear and improving the processing efficiency of secondary inspection.

[0062] Specifically, first, based on the cross-sectional area of ​​the current component and the maximum standard cross-sectional area of ​​the same type of component, set the normalized weight value of the cross-sectional size, and then use the normalized weight to correct the geometric sensitivity; where the maximum standard cross-sectional area is the area corresponding to the maximum standard cross-sectional size allowed in the design specifications for the same type of cable tray / medium pipeline in the current project.

[0063] The components are then placed into the installation scene. After determining the scene weight corresponding to whether the current scene belongs to a confined installation scene, a regular installation scene, or an open installation scene, the product of the scene weight and the corrected geometric sensitivity is taken as the output spatial sensitivity coefficient. The value range of each spatial sensitivity coefficient is synchronized to the value range of 0-1. The scene weight is configured according to the installation scene; the larger the space margin in the corresponding scene, the lower the scene weight.

[0064] In one implementation, when the normalized weight value of the cross-sectional dimensions is used to correct the geometric sensitivity, a fixed weight of 0.6 is taken as the base weight of the geometric sensitivity, and the remaining weight of 0.4 is allocated to the cross-sectional dimensions. Finally, the two parts are weighted and summed to obtain the geometric sensitivity corrected by the cross-sectional dimensions.

[0065] S5 extracts duplicate conflict samples from different detection cycles based on the spatial sensitivity coefficient and conflict risk coefficient of the target area, and determines the detection result of each detection cycle based on the number of occurrences of the duplicate conflict samples.

[0066] In one embodiment of the present invention, such as Figure 6 As shown, one implementation of step S5 includes: S51, treating each complete conflict detection as a detection cycle, and setting a risk relationship matrix corresponding to each detection cycle based on the spatial sensitivity coefficient and conflict risk coefficient of the target area.

[0067] S52, perform multi-period comparison on the risk relationship matrix, extract the common subset of continuous conflicts within multiple periods, and mark it as repeated conflict samples; compare all risk relationship matrices across periods, find the positions where repeated conflicts exist within at least two periods according to the position index corresponding to the matrix rows and columns, take them as the common subset of the output, and mark the detection period of repeated conflicts.

[0068] When detecting common subsets, the risk relationship matrix uses bridge component ID as the row index and pipeline component ID as the column index, and the matrix elements are the spatial sensitivity coefficient and conflict risk coefficient of the corresponding position. When comparing across periods, if the spatial overlap of conflict areas in two periods is greater than 60%, they are determined to be the same conflict area and included in the common subset of repeated conflicts.

[0069] S53. Based on the frequency of repeated conflict samples, the target regions in the current detection cycle are sorted from largest to smallest to obtain the output detection results. The first result in the output detection results is the high-risk region that has repeatedly encountered conflicts in multiple rounds of detection. The later the result is, the more likely it is that the region has only encountered a conflict for the first time in the current single cycle. Prioritizing this region can prevent such historical conflicts from being ignored, making the detection results in each cycle comparable horizontally. It can intuitively trace the conflict iteration in the same region and support the global optimization of subsequent pipeline layout.

[0070] Furthermore, when sorting the target region using the occurrence frequency of repeated conflict samples, the spatial sensitivity coefficient and conflict risk coefficient corresponding to the repeated conflict samples will be sorted in descending order based on the spatial sensitivity coefficient and conflict risk coefficient when they have the same occurrence frequency. The conflict risk coefficient is used as the first sorting basis. When the conflict risk coefficients are consistent, the spatial sensitivity coefficient is used for sorting, thereby characterizing the conflict problem and spatial sensitivity problem corresponding to the repeated occurrence of risk.

[0071] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered within the protection scope of the present invention.

Claims

1. An automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model, characterized in that, include: S1, obtain the 3D model of the cable tray and pipeline, and set the conflict area for the cable tray and pipeline; S2, using the spatial boundary of the current conflict area as the basis for retrieval, combined with the conflict area detected in the previous test, determines the conflict risk coefficient affected by conflict frequency, conflict type and spatial overlap. S3 treats each conflict area as a risk point, connects the conflict areas along the installation direction to form a risk association path, uses the conflict risk coefficient as a screening value to construct a gradient range of the conflict risk coefficient, and combines the number of points on the risk association path to screen the output target area. S4. When the target area meets the triggering conditions for secondary inspection, determine the spatial sensitivity coefficient of the target area based on the geometric characteristics of the corresponding components of the cable tray and pipeline in each inspection cycle. S5 extracts duplicate conflict samples from different detection cycles based on the spatial sensitivity coefficient and conflict risk coefficient of the target area, and determines the detection result of each detection cycle based on the number of occurrences of the duplicate conflict samples.

2. The automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model according to claim 1, characterized in that, When obtaining the 3D model corresponding to cable trays and pipelines, the methods include: Collect design drawings and 3D models corresponding to cable trays and pipelines; Based on the design drawings and 3D model, the coordinates of the cable trays and pipelines are unified, and their geometric spatial positions after the coordinate unification are recorded.

3. The automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model according to claim 1, characterized in that, The methods for setting the conflict zone in step S1 include: S11: Extract the installation scene and pipeline type of the pipeline from the 3D model, and use the installation scene and pipeline type as constraints to perform spatial trimming to form the initial detection area; S12, in each initial detection area, calculate the spatial distance between the cable tray and the pipeline to determine the adjacency relationship between the cable tray and the pipeline; S13 utilizes the adjacency relationship between cable trays and pipelines to select initial detection areas where the shortest spatial distance is less than the safe distance, which are then used as the output conflict areas.

4. The automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model according to claim 1, characterized in that, The conflict risk coefficient in step S2 is implemented in the following ways: S21, For each detected conflict region, calculate the spatial overlap between the current conflict region and the previous conflict region using bounding boxes; S22, Set a conflict frequency coefficient for the current conflict area based on the number of consecutive occurrences in multiple detections of the current conflict area; S23. Assign type weights based on the conflict type of the current conflict area. The product of spatial overlap, conflict frequency coefficient and type weight is regarded as the output conflict risk coefficient.

5. The automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model according to claim 4, characterized in that, Before outputting the conflict risk coefficient, its implementation methods include: Extract the components corresponding to cable trays and pipelines from historical data of the current conflict area, determine the type of component change, and the component density before and after the change; Based on the volume of the components under various change types, a weighted summary is performed on all components in the current conflict area, and the change level corresponding to the current conflict area is set. Based on the ratio of the total volume of all components within the current conflict area to the volume of the corresponding three-dimensional space of the conflict area, set the component density corresponding to the current conflict area; Based on the degree of change and component density of the current conflict area, the conflict risk coefficient is adjusted, and the adjusted conflict risk coefficient is output.

6. The automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model according to claim 1, characterized in that, The implementation methods for the target region in step S3 include: S31, the conflict area is abstracted into risk points, and the risk points are connected along the installation direction to form a risk association path; S32, Based on the conflict risk coefficient of each risk point on the risk association path, construct the gradient interval of the spatial variation of the conflict risk coefficient; S33. Filter the target area based on the gradient interval and the range of values ​​for the conflict risk coefficient.

7. The automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model according to claim 6, characterized in that, The methods for filtering target regions include: When the current risk-related path corresponds to two or more risk points, the risk points are distinguished according to the gradient ascending and gradient descending intervals. When traversing each risk point along the risk-related path, the risk points with conflict risk coefficients greater than the basic risk coefficient are taken as the target areas for output. When a risk point corresponds to a current risk-related path, the risk point with a conflict risk coefficient greater than the basic risk coefficient is taken as the output target area.

8. The automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model according to claim 1, characterized in that, The implementation methods of the spatial sensitivity coefficient in step S4 include: S41, when the current target area meets the triggering condition, extract components for each target area and determine the geometric features of the components; S42, based on the geometric characteristics of the component, assigns geometric sensitivity to abrupt changes in the shape of the component; S43 extracts the cross-sectional dimensions from the geometric features of the component, and combines the geometric sensitivity with the installation scene of the component to aggregate the output spatial sensitivity coefficient.

9. The automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model according to claim 8, characterized in that, The methods for aggregating geometric sensitivity into output spatial sensitivity coefficients include: Based on the current component's cross-sectional area and the maximum standard cross-sectional area of ​​similar components, set the normalized weight value for the cross-sectional dimensions, and use the normalized weight to correct the geometric sensitivity. Determine the scene weight to which the current scene belongs, and consider the product of the scene weight and the corrected geometric sensitivity as the output spatial sensitivity coefficient.

10. The automatic detection method for spatial conflicts between cable trays and pipelines based on a three-dimensional model according to claim 1, characterized in that, The detection results in step S5 are achieved in the following ways: S51, each complete conflict detection is regarded as a detection cycle, and a risk relationship matrix corresponding to each detection cycle is set based on the spatial sensitivity coefficient and conflict risk coefficient of the target area; S52, perform multi-period comparison of the risk relationship matrix, extract the common subset of conflicts that persist across multiple periods, and mark them as repeated conflict samples; S53: Sort the target regions of the current detection period from largest to smallest according to the occurrence frequency of duplicate conflict samples, and obtain the output detection results.

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