Data processing method and device applied to underground power distribution pipeline and electronic equipment

Abstract

Embodiments of the present disclosure disclose a data processing method and device applied to an underground power distribution pipeline and an electronic device. A specific implementation of the method comprises: generating an initial three-dimensional model; performing variable-scale voxel block segmentation on the initial three-dimensional model to obtain a voxel block information set; determining a data collection point set according to the voxel block information set; and performing the following processing steps for the data collection points: collecting real-time state of the power distribution pipeline at the data collection points according to the voxel block information corresponding to the data collection points; determining an influence domain according to the pipeline real-time state information; and performing model updating on a local power distribution pipeline contained in at least one voxel block within the influence domain in the initial three-dimensional model according to the pipeline real-time state information. The implementation guarantees the construction accuracy of the underground power distribution pipeline.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of computer technology and electric power, and particularly to a data processing method and device applied to an underground power distribution pipeline and an electronic device. BACKGROUND

[0002] With the continuous development of urbanization, the corresponding power demand is also increasing. Limited by ground building constraints, power transmission cables gradually change from overhead laying to underground power distribution pipeline laying. In the laying process of the power distribution pipeline, the pipe jacking and the pipe arrangement are usually used to lay the power distribution pipeline. However, due to the complex structure of the underground power distribution pipeline and the poor data collection environment in the power distribution pipeline, it is difficult to effectively collect data, which may lead to laying errors, especially for long-distance laying scenarios, which may accumulate errors and further affect the construction accuracy of the underground power distribution pipeline.

[0003] The above information disclosed in this BACKGROUND section is only for the purpose of enhancing the understanding of the background of the present inventive concepts, and therefore, it can contain information that does not form the prior art that is already known to those of ordinary skill in the art. SUMMARY

[0004] The summary of the present disclosure is used to introduce the concepts in a brief manner, which will be described in detail in the specific embodiments section. The summary of the present disclosure is not intended to identify key or essential features of the claimed technical solution, nor is it intended to be used to limit the scope of the claimed technical solution.

[0005] Some embodiments of the present disclosure propose a data processing method and device applied to an underground power distribution pipeline and an electronic device to solve the technical problems mentioned in the background section.

[0006] In a first aspect, some embodiments of the present disclosure provide a data processing method applied to an underground power distribution pipeline, the method comprising: generating an initial three-dimensional model according to a power distribution pipeline layout, wherein the power distribution pipeline layout is marked with a pre-planned pipeline distribution for the underground power distribution pipeline of a target area; performing variable-scale voxel block segmentation on the initial three-dimensional model to obtain a set of voxel block information, wherein the voxel block information includes voxel block description information and pipeline description information, and the pipeline description information represents the pipeline specifications of the local power distribution pipeline located in the voxel block corresponding to the voxel block information; determining a set of data collection points according to the set of voxel block information; for each data collection point in the set of data collection points, performing the following processing steps: collecting real-time state information of the power distribution pipeline at the data collection point according to the voxel block information corresponding to the data collection point to obtain pipeline real-time state information; determining an influence domain according to the pipeline real-time state information, wherein the influence domain is a limited domain centered on the voxel block corresponding to the data collection point; and updating the local power distribution pipeline contained in at least one voxel block in the influence domain in the initial three-dimensional model according to the pipeline real-time state information.

[0007] In a second aspect, some embodiments of the present disclosure provide a data processing device applied to an underground power distribution pipeline, the device comprising: a generation unit configured to generate an initial three-dimensional model according to a power distribution pipeline layout, wherein the power distribution pipeline layout is marked with a pre-planned pipeline distribution for the underground power distribution pipeline of a target area; a voxel block segmentation unit configured to perform variable-scale voxel block segmentation on the initial three-dimensional model to obtain a set of voxel block information, wherein the voxel block information includes voxel block description information and pipeline description information, and the pipeline description information represents the pipeline specifications of the local power distribution pipeline located in the voxel block corresponding to the voxel block information; a determination unit configured to determine a set of data collection points according to the set of voxel block information; and an execution unit configured to, for each data collection point in the set of data collection points, perform the following processing steps: collecting real-time state information of the power distribution pipeline at the data collection point according to the voxel block information corresponding to the data collection point to obtain pipeline real-time state information; determining an influence domain according to the pipeline real-time state information, wherein the influence domain is a limited domain centered on the voxel block corresponding to the data collection point; and updating the local power distribution pipeline contained in at least one voxel block in the influence domain in the initial three-dimensional model according to the pipeline real-time state information.

[0008] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0009] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0010] The above-described embodiments of this disclosure have the following beneficial effects: The data processing method applied to underground power distribution pipelines through some embodiments of this disclosure achieves data acquisition for complex underground power distribution pipeline structures and under poor data acquisition environments, and effectively avoids error accumulation through model updates, thereby ensuring the construction accuracy of underground power distribution pipelines. Specifically, firstly, this disclosure generates an initial three-dimensional model based on the power distribution pipeline layout map, wherein the power distribution pipeline layout map is marked with a pre-planned pipeline distribution for the target area. By combining the pre-planned power distribution pipeline layout map, the pipeline distribution for the underground power distribution pipeline under the three-dimensional model structure is determined. Secondly, this disclosure performs variable-scale voxel block segmentation on the initial three-dimensional model to obtain a voxel block information set, wherein the voxel block information includes: voxel block description information and pipeline description information, wherein the pipeline description information represents the pipeline specifications corresponding to the local power distribution pipeline located within the voxel block corresponding to the voxel block information. Next, this disclosure determines the data acquisition point set based on the above voxel block information set. In practice, considering the influence of the data acquisition environment, data is often collected only from easily accessible data acquisition points for convenience, resulting in data with limited reference value. This disclosure determines suitable data sampling points through voxel block segmentation, improving the reference value of the real-time pipeline status information subsequently acquired at these sampling points. In particular, conventional voxel block segmentation methods often use fixed voxel block sizes. When the voxel block size is set too small, it leads to excessive voxel block segmentation operations and a large data processing volume. When the voxel block size is too large, it is difficult to represent the relationship between the voxel blocks and the corresponding local power distribution pipelines, thus affecting the determination of data acquisition points. Therefore, this disclosure adopts a variable-scale voxel block segmentation method, flexibly segmenting voxel blocks based on the characteristics of the initial 3D model, thereby ensuring the effectiveness of the determined data sampling points. Furthermore, for each data acquisition point in the above data acquisition point set, the following processing steps are performed: First, based on the voxel block information corresponding to the above data acquisition point, real-time status data of the power distribution pipeline is acquired at the above data acquisition point to obtain the real-time pipeline status information. The second step involves determining the influence domain based on the real-time pipeline status information. This influence domain is a finite region centered on the voxel block corresponding to the data acquisition point. In practice, due to the connectivity of power distribution pipelines, an anomaly in the power distribution pipeline corresponding to a data acquisition point may affect surrounding power distribution pipelines. Therefore, determining the influence domain provides a quantified range of impact. The third step involves updating the local power distribution pipelines within at least one voxel block in the initial 3D model and within the influence domain, based on the real-time pipeline status information. This method ensures the accuracy and reliability of data acquisition while achieving a full update of the initial 3D model through limited data acquisition at data sampling points, thus guaranteeing the construction accuracy of underground power distribution pipelines. Attached Figure Description

[0011] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0012] Figure 1 This is a flowchart of some embodiments of the data processing method applied to underground power distribution pipelines according to the present disclosure;

[0013] Figure 2 This is a schematic diagram of the keypoint sampling scene corresponding to the initial keypoint;

[0014] Figure 3 This is a schematic diagram of the structure of some embodiments of a data processing device applied to underground power distribution pipelines according to the present disclosure;

[0015] Figure 4 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0016] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0017] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0018] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0019] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0020] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0021] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] refer to Figure 1 The diagram illustrates a flow 100 of some embodiments of a data processing method for underground power distribution pipelines according to the present disclosure. This data processing method for underground power distribution pipelines includes the following steps:

[0023] Step 101: Generate an initial 3D model based on the power distribution pipeline layout diagram.

[0024] In some embodiments, the execution entity (e.g., a computing device) of the data processing method applied to underground power distribution pipelines can generate an initial three-dimensional model based on the power distribution pipeline layout map. The power distribution pipeline layout map indicates the pre-planned distribution of underground power distribution pipelines for a target area. The target area can be the area where underground power distribution pipelines are to be laid. The initial three-dimensional model can be a three-dimensional model of the power distribution pipeline. In practice, especially in urban areas (target areas), due to the limitations of above-ground buildings, power transmission cables are increasingly being laid in the form of underground power distribution pipelines. Based on electricity demand and electricity planning, it is often necessary to pre-plan underground power distribution pipelines. Specifically, the power distribution pipeline layout map may include, but is not limited to: pipeline type, pipeline elevation, pipeline burial method, pipeline number, pipeline specifications, and pipeline location coordinates. In practice, pipeline types may include, but are not limited to: precast reinforced concrete pipelines, CPVC (chlorinated polyvinyl chloride) pipelines, and MPP (microporous foamed polypropylene) pipelines. Pipeline elevation represents the height of the pipeline relative to a reference plane. Pipeline burial methods include, but are not limited to: pipe jacking and trenching. Pipe number identifies the pipe. Pipe specifications include, but are not limited to, outer diameter, inner diameter, and length. Pipe location coordinates indicate the pipe's laying position, specifically including, but not limited to, the coordinates of the pipe's starting and ending vertices. In practice, an initial 3D model can be generated using the iPipe Modeler system, combined with a power distribution pipeline layout diagram. The iPipe Modeler system is an automated 3D pipeline modeling system.

[0025] It should be noted that the aforementioned computing devices can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed on the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0026] In some optional implementations of certain embodiments, the execution entity generates an initial three-dimensional model based on the power distribution pipeline layout diagram, including:

[0027] The first step is to uniformly sample key points on the above power distribution pipeline layout diagram to obtain an initial set of key points.

[0028] In practice, the aforementioned implementing entity can preset a sampling frequency to uniformly sample key points of the power distribution pipelines within the power distribution pipeline layout diagram, thereby obtaining the aforementioned initial key point set. In particular, to effectively sample key points of the power distribution pipeline layout diagram, a higher sampling frequency can be set to obtain the initial key point set. These initial key points are distributed along the power distribution pipelines.

[0029] As an example, see Figure 2 The diagram shows the sampling scenario for the initial key points. In this diagram, the power distribution pipelines in the layout are approximately tree-like; therefore, the initial key points are evenly distributed across the power distribution pipelines included in the layout. In practice, power distribution pipelines are often influenced by the transmission line structure and have branching structures. Therefore, to ensure that the initial key points cover the branching points, a higher sampling frequency is used.

[0030] The second step is to perform the following first keypoint processing steps for each initial keypoint in the above initial keypoint set:

[0031] The first sub-step is to extract the key point description information corresponding to the initial key points mentioned above.

[0032] The key point description information includes: key point location, the pipe specification to which the key point belongs, and a list of key point connection relationships. In practice, since the pipe location coordinates and sampling frequency are known, the pipe location coordinates can be used as the base coordinates, and the displacement corresponding to the sampling frequency can be used as the displacement increment to obtain the key point location corresponding to the initial key point. Since the initial key points are distributed on the power distribution pipes, the pipe specification of the power distribution pipe where the initial key point is located can be used as the pipe specification to which the key point belongs. The key point connection relationship list contains the key point identifiers of other initial key points adjacent to the initial key point.

[0033] As an example, further reference Figure 2 Taking the initial key point A7 as an example, its corresponding key point description information includes a list of key point connection relationships, which may be [initial key point A6, initial key point A8, initial key point A9].

[0034] The second sub-step involves determining the connectivity of the key points corresponding to the initial key points based on the list of key point connections included in the key point description information.

[0035] In practice, the aforementioned executing entity can use the length of the list of keypoint connection relationships as the keypoint connectivity. Specifically, the list of keypoint connection relationships can be stored using a List structure, and therefore the len() function can be used to determine the length of the list of keypoint connection relationships, which can then be used as the keypoint connectivity.

[0036] The third sub-step is to determine the key point type of the initial key point as the first type in response to the above key point connectivity being the first target value.

[0037] In practice, the first target value can be 1. The first type can represent keypoints whose initial keypoints are of the boundary point type.

[0038] In response to the above key point connectivity being the second target value, the key point type of the above initial key points is determined as the second type.

[0039] In practice, the second target value can be 2. The second type can represent keypoints whose initial keypoints are of the intermediate point type.

[0040] In response to the fact that the connectivity of the aforementioned key points is greater than the second target value, the initial key point type is determined to be the third type.

[0041] In practice, the third type can represent key points whose initial key points are of the connection point type.

[0042] As an example, see further. Figure 2 Among them, the keypoint type of initial keypoint A1 can be type 1. The keypoint types of initial keypoints A6, A8, and A9 are all type 2. The keypoint type of initial keypoint A7 is type 3.

[0043] The third step is to generate the initial 3D model based on the key point type and key point description information corresponding to the initial key points in the initial key point set.

[0044] As an example, since the initial key points correspond to key point description information, the 3D model of the power distribution pipeline can be discretely performed on the initial key point locations by combining the key point types corresponding to the initial key points. In particular, since the key point description information includes a list of key point connection relationships, the pipeline direction can be determined by combining the list of key point connection relationships, thereby obtaining the initial 3D model.

[0045] In some optional implementations of certain embodiments, the execution entity generates the initial 3D model based on the keypoint type corresponding to the initial keypoint in the initial keypoint set and the keypoint description information corresponding to the initial keypoint, including:

[0046] The first sub-step involves performing the following second keypoint processing step for each candidate keypoint in the candidate keypoint set, where the candidate keypoints are the initial keypoints in the aforementioned initial keypoint set whose corresponding keypoint type is the second type:

[0047] Step 1: Determine the connection of key points based on the list of key point connection relationships included in the key point description information corresponding to the above candidate key points.

[0048] The keypoint connection line represents the equation of the straight line between two initial keypoints adjacent to the candidate keypoint. In practice, since the keypoint positions of the candidate keypoints (corresponding to initial keypoints of type 2) are known, and the connectivity of the corresponding keypoints is 2 (meaning there is one other initial keypoint on each side of the candidate keypoint), the equation of the straight line corresponding to the keypoint connection line can be obtained by substituting the keypoint positions corresponding to the initial keypoints contained in the keypoint connection relationship list into the point-slope form equation.

[0049] Step 2: In response to the fact that the key point corresponding to the above candidate key point is not located on the line connecting the above key points, the above candidate key point is determined as the target key point.

[0050] As an example, the positions of the candidate key points can be substituted into the equation of the line connecting the key points to determine whether the positions of the candidate key points are located on the line connecting the key points.

[0051] The second sub-step involves identifying the initial keypoints in the aforementioned initial keypoint set that correspond to either the first or third type of keypoint as target keypoints.

[0052] In practice, a high sampling frequency can be set to effectively sample key points in power distribution pipeline layout diagrams. The second type of initial key points represents intermediate points between non-boundary points and connection points. Considering laying and subsequent maintenance costs, power distribution pipelines are often laid in approximately straight lines. Therefore, an excessively high sampling frequency leads to a large number of intermediate points, i.e., a large number of intermediate points (initial key points) located on straight lines. For this type of initial key point, modeling and connecting boundary points (initial key points) allows for rapid model construction. Therefore, a large number of intermediate points significantly impacts the efficiency of subsequent 3D modeling.

[0053] The third sub-step involves updating the list of key point connection relationships, which is included in the key point description information corresponding to each target key point in the obtained target key point set, for each target key point.

[0054] In practice, the aforementioned executing entity can remove initial keypoints from the initial keypoint set, excluding the target keypoint, thereby reducing the number of initial keypoints. At this time, since the initial keypoints adjacent to the target keypoint have changed, it is necessary to update the list of keypoint connection relationships included in the keypoint description information corresponding to the target keypoint.

[0055] The fourth sub-step involves generating a 3D map of key points based on the obtained set of target key points and the corresponding key point description information, including the updated list of key point connections and key point locations.

[0056] In practice, the aforementioned implementing entity can construct a 3D keypoint graph with target keypoints as graph nodes and the connections between target keypoints in the keypoint connection relationship list as graph edges. Specifically, since the elevation of power distribution pipelines may vary, meaning that power distribution pipelines may be at different plane heights, constructing a 3D keypoint graph can effectively depict the positional relationships of target keypoints in three dimensions.

[0057] The fifth sub-step involves rendering the power distribution pipeline of the aforementioned key point 3D map based on the key point description information corresponding to the target key point, including the pipeline specifications to which the key point belongs, to obtain the aforementioned initial 3D model.

[0058] In practice, firstly, the power distribution pipeline can be rendered at the location of the target key point by combining the pipeline specifications of the key point description information. Then, the connection relationship (edge) between the target key points in the 3D key point map is used as the orientation of the power distribution pipeline to connect the pipelines. This method can greatly improve the rendering speed of the initial 3D model.

[0059] Step 102: Perform variable-scale voxel segmentation on the initial 3D model to obtain a voxel block information set.

[0060] In some embodiments, the aforementioned execution entity can perform variable-scale voxel block segmentation on the initial 3D model to obtain a voxel block information set. The voxel block information includes voxel block description information and pipe description information. The pipe description information represents the pipe specifications corresponding to the local power distribution pipes located within the voxel block corresponding to the voxel block information. In practice, the voxel block can be a cube voxel block. Specifically, the voxel block description information can include: voxel block specifications and voxel block location. Since the voxel block is a cube voxel block, the voxel block specifications can include: voxel block side length. The voxel block location can be represented by the coordinates of the voxel block's center point. The pipe description information can include: pipe identifier, pipe outer diameter, pipe inner diameter, and pipe length. In practice, an octree partitioning method can be used to perform variable-scale voxel block segmentation on the initial 3D model to obtain the voxel block information set.

[0061] In some optional implementations of certain embodiments, the execution entity performs variable-scale voxel block segmentation on the initial 3D model to obtain a voxel block information set, including:

[0062] The first step is to segment the initial 3D model to obtain a set of local 3D models.

[0063] The local 3D model is a 3D model of a single power distribution pipeline. In practice, underground power distribution pipelines in the target area often have complex structures. Dividing the initial 3D model into voxel blocks as a whole is time-consuming. Therefore, this disclosure divides the initial 3D model into local 3D model sets, using the power distribution pipeline as a unit. This allows for parallel voxel block division of each local 3D model within the set. As the number of power distribution pipelines increases, the processing speed is increased exponentially compared to dividing the initial 3D model into voxel blocks as a whole.

[0064] The second step is to perform the following first voxel block processing steps for each local 3D model in the above set of local 3D models:

[0065] The first sub-step involves determining the initial voxel block description information based on the target length and initial quantity.

[0066] Wherein, the aforementioned target length is the pipe length corresponding to the power distribution conduit in the aforementioned local 3D model, and the aforementioned initial quantity is a pre-set number of voxel blocks for the local 3D model. The initial voxel block description information characterizes the voxel block specification corresponding to the initial voxel block. In practice, since the voxel block is a cube voxel block, and the voxel block specification can include the voxel block side length, therefore, voxel block side length = target length / initial quantity. In particular, when the ratio of the target length to the initial quantity is not an integer, it is rounded up.

[0067] The second sub-step involves dividing the local 3D model into initial voxel blocks based on the initial voxel block description information to obtain an initial voxel block group.

[0068] In practice, since the side lengths of the voxel blocks are known, the local 3D model can be divided into voxel blocks using the side lengths as the step size, resulting in an initial voxel block group. The ordered assembly of the initial voxel blocks in the initial voxel block group is equivalent to the local 3D model.

[0069] Third, for each initial voxel block in the obtained initial voxel block set, perform the following voxel block processing steps:

[0070] The first sub-step is to determine the positional relationship between the initial voxel block and the local power distribution conduit corresponding to the initial voxel block.

[0071] In practice, since the local 3D model is a 3D model of a single power distribution conduit, the initial voxel block will correspond to a segment of the local power distribution conduit, i.e., a part of the conduit. Because the initial voxel block is constructed only based on the target length and initial quantity, the initial voxel block and the corresponding segment of the local power distribution conduit are often not tightly fitted. That is, the initial voxel block cannot effectively represent the conduit features of the corresponding local power distribution conduit. Therefore, it is necessary to determine the positional relationship between the initial voxel block and the corresponding local power distribution conduit. Specifically, the positional relationship can include: inclusion relationship (first positional relationship), fitting relationship, and being included relationship (second positional relationship). The inclusion relationship (first positional relationship) indicates that the initial voxel block includes the local power distribution conduit, i.e., the initial voxel block is too large to effectively fit the initial voxel block to the corresponding local power distribution conduit. The being included relationship (second positional relationship) indicates that the initial voxel block is included within the local power distribution conduit, i.e., the initial voxel block is too small to effectively fit the initial voxel block to the corresponding local power distribution conduit. The bonding relationship characterizes the bonding between the initial voxel block and the corresponding local power distribution pipe, that is, from the perspective of cross-section, the boundary of the initial voxel block is tangent to the cross-section of the local power distribution pipe.

[0072] The second sub-step, in response to the positional relationship being the first positional relationship, involves shrinking the initial voxel block to obtain the shrunken voxel block and generating the voxel block information corresponding to the shrunken voxel block.

[0073] In practice, the aforementioned execution entity can shrink the side length of the voxel block corresponding to the initial voxel block to obtain the shrunken voxel block and update the voxel block information. The shrunken voxel block and its corresponding local power distribution conduit are in a fitted relationship.

[0074] The third sub-step, in response to the positional relationship being the second positional relationship, involves enlarging the initial voxel block to obtain the enlarged voxel block and generating the voxel block information corresponding to the enlarged voxel block.

[0075] In practice, the aforementioned execution entity can enlarge the side length of the voxel block corresponding to the initial voxel block to obtain an enlarged voxel block and update the voxel block information. The enlarged voxel block and its corresponding local power distribution conduit are in a fitted relationship.

[0076] The voxel block segmentation method disclosed herein greatly improves segmentation efficiency. Compared with conventional octree segmentation based on recursion, it reduces the number of splits and merges, thus significantly improving segmentation performance.

[0077] Step 103: Determine the data collection point set based on the voxel block information set.

[0078] In some embodiments, the execution entity can determine the data collection point set based on the voxel block information set. In practice, the execution entity can use random sampling to select voxel blocks as data sampling points from the voxel blocks corresponding to the voxel block information in the voxel block information set, thus obtaining the data sampling point set. Specifically, since the voxel block corresponds to a local power distribution pipe, that is, the voxel block corresponds to a section of local power distribution pipe with a known location, the location of the corresponding local power distribution pipe can be used as a data sampling point to obtain the data sampling point set.

[0079] In some optional implementations of some embodiments, the execution entity determines the data acquisition point set based on the voxel block information set, including:

[0080] The first step is to extract features from each voxel block information in the above voxel block information set to generate voxel block features.

[0081] The voxel block features include voxel block description features and pipeline description features. In practice, since the voxel block description information can include voxel block specifications and voxel block positions, firstly, the voxel block specifications can be normalized using Z-scores to obtain normalized voxel block specifications. Secondly, the voxel block positions can be converted into polynomial combinations. For example, if the voxel block position is (x, y, z), then the converted polynomial combination can be [x, y, z, x]. 2 y 2 , z 2 The feature vector [xy, xz, yz, xyz] is improved by introducing redundant features. Then, the normalized voxel block size and polynomial combination are concatenated to construct a 1×11 one-dimensional feature vector. In addition, the pipeline description information is encoded using the Word2Vec model to obtain pipeline description features.

[0082] The second step is to determine the voxel importance of each voxel block in the above voxel block information set based on the voxel block information features corresponding to the voxel block information.

[0083] The voxel block importance is used to characterize the structural importance of local power distribution conduits within a voxel block in the initial 3D model. In practice, since the voxel block description features and conduit description features reside in different feature spaces, a dual-tower model is used to map them to the same feature space. This dual-tower model employs two parallel Tiny-BERT models to perform feature mapping on the voxel block and conduit description features respectively, ensuring that the mapped features reside in the same feature space. Next, the mapped features are input into a multi-classifier to obtain the voxel block importance. This multi-classifier is constructed using multiple fully connected layers connected in series to reduce the feature dimension and output the voxel block importance. Specifically, the feature extraction, feature mapping, and classification steps in steps one through three are treated as a whole for supervised model training.

[0084] The third step is to select a target number of voxel blocks from the voxel blocks corresponding to the voxel block information in the above voxel block information set, based on the importance of the voxel blocks corresponding to the voxel block information, as a candidate data sampling point set.

[0085] In practice, firstly, the aforementioned executing entity can use the importance of the voxel block information corresponding to the voxel block information as a ranking score, and sort the voxel block information in the voxel block information set in descending order to obtain a voxel block information sequence. Then, the aforementioned executing entity can use the voxel blocks corresponding to the top target number of voxel block information in the voxel block information sequence as a candidate data sampling point set.

[0086] The fourth step is to select candidate data sampling points that meet the selection criteria from the above set of candidate data sampling points, and use them as data sampling points to obtain the above set of data sampling points.

[0087] The aforementioned screening criterion is that the distance between a candidate data sampling point and its neighboring data sampling points is greater than or equal to a preset distance. In practice, relying solely on voxel block importance to determine data sampling points may result in small intervals between data sampling points. Therefore, based on the screened candidate data sampling points, the distance between sampling points is used as a secondary filtering condition to further refine the set of data sampling points, thereby improving the effectiveness of the obtained data sampling points.

[0088] Step 104: For each data collection point in the data collection point set, perform the following processing steps:

[0089] Step 1041: Based on the voxel block information corresponding to the data acquisition point, collect the real-time status of the power distribution pipeline at the data acquisition point to obtain the real-time status information of the pipeline.

[0090] In some embodiments, the aforementioned execution entity can collect real-time status data of power distribution pipelines at the data acquisition points based on the voxel block information corresponding to the data acquisition points, thereby obtaining real-time pipeline status information. This real-time pipeline status information includes: pipeline anomaly type and pipeline anomaly confidence level. In practice, since voxel block information corresponds to local power distribution pipelines, and different power distribution pipelines have different characteristics, different acquisition methods are selected for local power distribution pipelines corresponding to different voxel block information. The acquired data is then analyzed using a machine learning model to obtain the real-time pipeline status information. The machine learning model can employ a model such as IMRnet to analyze point cloud data and obtain the pipeline anomaly location and anomaly type. Alternatively, a YOLOv4-Tiny model can be used to analyze images and obtain the pipeline anomaly location and anomaly type.

[0091] As an example, taking a precast reinforced concrete pipe type local power distribution pipeline (data acquisition point) as an example, since the pipeline has a large internal space, a scanning method such as a LiDAR point cloud scanner can be used. This involves placing the LiDAR point cloud scanner inside the local power distribution pipeline corresponding to the data sampling point to collect point cloud data. Especially for situations where the lighting inside the power distribution pipeline is poor, supplementary lighting equipment can be used. The collected point cloud data is then analyzed using an IMRnet model to obtain the real-time status information of the pipeline.

[0092] As another example, taking CPVC (chlorinated polyvinyl chloride) or MPP (microporous foamed polypropylene) pipe types as local power distribution pipes (data acquisition points), since the inside of the pipe is relatively narrow, miniature cameras and other equipment can be used to acquire images, and the images can be analyzed using the YOLOv4-Tiny model to obtain the location and type of abnormality in the pipe, and obtain the real-time status information of the pipe.

[0093] Step 1042: Determine the area of ​​influence based on the real-time status information of the pipeline.

[0094] In some embodiments, the aforementioned execution entity can determine the influence domain based on the real-time status information of the pipeline. The influence domain is a finite domain centered on the voxel block corresponding to the data acquisition point. In practice, since power distribution pipelines are in a connected state, an anomaly in a local power distribution pipeline contained within the voxel block corresponding to the data acquisition point may affect the connected power distribution pipelines. Therefore, it is necessary to combine the real-time status information of the pipeline to determine the scope of influence, which serves as the influence domain.

[0095] In some optional implementations of certain embodiments, the execution entity determines the influence domain based on the real-time pipeline status information, including:

[0096] The first step is to determine the real-time status characteristics of the pipeline based on the aforementioned real-time pipeline status information.

[0097] In practice, since real-time pipeline status information includes both pipeline anomaly types and pipeline anomaly confidence levels, and the number of anomaly types is limited, one-hot encoding can be used to encode the anomaly types. Furthermore, the pipeline anomaly confidence level is a probability value between 0 and 1, which can be understood as a standardized value. Therefore, the one-hot encoded anomaly types and pipeline anomaly confidence levels can be concatenated as the real-time pipeline status features.

[0098] The second step is to determine the feature differences between the real-time status features of the pipeline and the pipeline description features corresponding to the voxel block information of the data acquisition points.

[0099] In practice, since the feature dimension of pipeline description features is larger than that of pipeline real-time state features, it is first necessary to downsample the pipeline description features to obtain downsampled pipeline description features. Then, the feature differences between the downsampled pipeline description features and the pipeline real-time state features are determined by calculating cosine similarity. A larger feature difference indicates a more obvious anomaly, while a smaller feature difference indicates a less obvious difference.

[0100] The third step is to determine the extent of coverage based on the aforementioned differences in characteristics.

[0101] In practice, interval mapping can be used to map the sweep rate corresponding to the feature differences.

[0102] The fourth step is to map the aforementioned influence domain based on the aforementioned reach.

[0103] In practice, since power distribution pipelines are approximately straight, the spread of their influence is often linear, meaning the influence decreases with increasing distance from the data collection point. Therefore, a standard influence distance can be set, and the product of the sweepability and the standard influence distance can be used as the semi-major axis of the influence domain to obtain the aforementioned influence domain.

[0104] Step 1043: Based on the real-time status information of the pipeline, update the model of the local power distribution pipeline contained in at least one voxel block within the influence domain in the initial three-dimensional model.

[0105] In some embodiments, the aforementioned execution entity can update the local power distribution pipelines contained in at least one voxel block within the influence domain of the initial 3D model based on the real-time pipeline status information. In practice, when a local power distribution pipeline corresponding to a data acquisition point is abnormal, it is only a local abnormality. Therefore, the pipeline status can be updated by combining the real-time pipeline status information with the local power distribution pipelines contained in at least one voxel block within the influence domain to obtain an updated 3D model. This method can reduce the amount of data updated and improve the update speed.

[0106] In some optional implementations of certain embodiments, the execution entity updates the model of the local power distribution pipeline contained in at least one voxel block within the influence domain of the initial three-dimensional model based on the real-time pipeline status information, including:

[0107] Based on the real-time status information of the pipelines, and taking the voxel block corresponding to the data acquisition point as the starting voxel block, a diffusion-style model update is performed on the local power distribution pipelines contained in at least one voxel block within the influence domain of the initial 3D model.

[0108] In practice, the influence of at least one voxel block within the influence domain gradually weakens as the distance from the voxel block corresponding to the data sampling point increases. Therefore, taking the voxel block corresponding to the data sampling point as the starting voxel block, during the local update of the local power distribution pipelines contained in at least one voxel block within the influence domain in the initial 3D model, an update probability is set. The update probability is inversely proportional to the distance from the voxel block corresponding to the data sampling point; that is, the closer the distance, the higher the update probability; the farther the distance, the lower the update probability. In particular, each voxel block within the influence domain corresponds to an update probability, which is used to determine whether to update the corresponding local power distribution pipeline model.

[0109] The above-described embodiments of this disclosure have the following beneficial effects: The data processing method applied to underground power distribution pipelines through some embodiments of this disclosure achieves data acquisition for complex underground power distribution pipeline structures and under poor data acquisition environments, and effectively avoids error accumulation through model updates, thereby ensuring the construction accuracy of underground power distribution pipelines. Specifically, firstly, this disclosure generates an initial three-dimensional model based on the power distribution pipeline layout map, wherein the power distribution pipeline layout map is marked with a pre-planned pipeline distribution for the target area. By combining the pre-planned power distribution pipeline layout map, the pipeline distribution for the underground power distribution pipeline under the three-dimensional model structure is determined. Secondly, this disclosure performs variable-scale voxel block segmentation on the initial three-dimensional model to obtain a voxel block information set, wherein the voxel block information includes: voxel block description information and pipeline description information, wherein the pipeline description information represents the pipeline specifications corresponding to the local power distribution pipeline located within the voxel block corresponding to the voxel block information. Next, this disclosure determines the data acquisition point set based on the above voxel block information set. In practice, considering the influence of the data acquisition environment, data is often collected only from easily accessible data acquisition points for convenience, resulting in data with limited reference value. This disclosure determines suitable data sampling points through voxel block segmentation, improving the reference value of the real-time pipeline status information subsequently acquired at these sampling points. In particular, conventional voxel block segmentation methods often use fixed voxel block sizes. When the voxel block size is set too small, it leads to excessive voxel block segmentation operations and a large data processing volume. When the voxel block size is too large, it is difficult to represent the relationship between the voxel blocks and the corresponding local power distribution pipelines, thus affecting the determination of data acquisition points. Therefore, this disclosure adopts a variable-scale voxel block segmentation method, flexibly segmenting voxel blocks based on the characteristics of the initial 3D model, thereby ensuring the effectiveness of the determined data sampling points. Furthermore, for each data acquisition point in the above data acquisition point set, the following processing steps are performed: First, based on the voxel block information corresponding to the above data acquisition point, real-time status data of the power distribution pipeline is acquired at the above data acquisition point to obtain the real-time pipeline status information. The second step involves determining the influence domain based on the real-time pipeline status information. This influence domain is a finite region centered on the voxel block corresponding to the data acquisition point. In practice, due to the connectivity of power distribution pipelines, an anomaly in the power distribution pipeline corresponding to a data acquisition point may affect surrounding power distribution pipelines. Therefore, determining the influence domain provides a quantified range of impact. The third step involves updating the local power distribution pipelines within at least one voxel block in the initial 3D model and within the influence domain, based on the real-time pipeline status information. This method ensures the accuracy and reliability of data acquisition while achieving a full update of the initial 3D model through limited data acquisition at data sampling points, thus guaranteeing the construction accuracy of underground power distribution pipelines.

[0110] Further reference Figure 3 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a data processing device applied to underground power distribution pipelines. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this data processing device applied to underground power distribution pipelines can be specifically applied to various electronic devices.

[0111] like Figure 3 As shown, a data processing device 300 for underground power distribution pipelines in some embodiments includes: a generation unit 301, a voxel segmentation unit 302, a determination unit 303, and an execution unit 304. The generation unit 301 is configured to generate an initial three-dimensional model based on a power distribution pipeline layout diagram, wherein the power distribution pipeline layout diagram is marked with a pre-planned pipeline distribution for a target area. The voxel segmentation unit 302 is configured to perform variable-scale voxel segmentation on the initial three-dimensional model to obtain a voxel block information set, wherein the voxel block information includes voxel block description information and pipeline description information, wherein the pipeline description information represents the pipeline specifications corresponding to a local power distribution pipeline located within the voxel block corresponding to the voxel block information. The determination unit 303 is configured to determine the voxel block information set based on the voxel block information set. The data acquisition point set is determined. Execution unit 304 is configured to perform the following processing steps for each data acquisition point in the data acquisition point set: real-time status acquisition of the power distribution pipeline is performed at the data acquisition point based on the voxel block information corresponding to the data acquisition point, obtaining real-time pipeline status information; an influence domain is determined based on the real-time pipeline status information, wherein the influence domain is a finite domain centered on the voxel block corresponding to the data acquisition point; and the model is updated based on the real-time pipeline status information for at least one voxel block in the initial 3D model containing the local power distribution pipeline within the influence domain.

[0112] It is understandable that the units described in the data processing device 300 applied to underground power distribution pipelines are related to the reference... Figure 1 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the data processing device 300 and its constituent units applied to underground power distribution pipelines, and will not be repeated here.

[0113] The following is for reference. Figure 4 It illustrates a schematic diagram of the structure of an electronic device (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of this disclosure. Figure 4As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the methods described above. The processor provides computational and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the execution of the computer program in the non-volatile storage medium; when executed by the processor, the computer program causes the processor to perform any of the methods described above. The network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the computer device to which the present disclosure is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0115] In one embodiment, the processor is configured to run a computer program stored in a memory to perform the following steps: generating an initial three-dimensional model based on a power distribution pipeline layout diagram, wherein the power distribution pipeline layout diagram is marked with a pre-planned pipeline distribution for an underground power distribution pipeline in a target area; performing variable-scale voxel block segmentation on the initial three-dimensional model to obtain a voxel block information set, wherein the voxel block information includes: voxel block description information and pipeline description information, wherein the pipeline description information characterizes the pipeline specifications corresponding to a local power distribution pipeline located within the voxel block corresponding to the voxel block information; based on the above... A set of voxel block information is used to determine a set of data acquisition points. For each data acquisition point in the set of data acquisition points, the following processing steps are performed: Based on the voxel block information corresponding to the data acquisition point, real-time status acquisition of the power distribution pipeline is performed at the data acquisition point to obtain real-time pipeline status information; Based on the real-time pipeline status information, an influence domain is determined, wherein the influence domain is a finite domain centered on the voxel block corresponding to the data acquisition point; Based on the real-time pipeline status information, the model of at least one local power distribution pipeline contained in at least one voxel block within the influence domain in the initial 3D model is updated.

[0116] This disclosure also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can be referred to the various embodiments of the methods described above.

[0117] The aforementioned computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. Alternatively, the aforementioned computer-readable storage medium may be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0118] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0119] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A data processing method applied to underground power distribution pipelines, characterized in that, include: An initial three-dimensional model is generated based on the power distribution pipeline layout map, wherein the power distribution pipeline layout map is marked with the pre-planned pipeline distribution of underground power distribution pipelines for the target area; The initial three-dimensional model is divided into voxel blocks with variable scale to obtain a set of voxel block information. The voxel block information includes voxel block description information and pipe description information. The pipe description information represents the pipe specifications of the local power distribution pipe located in the voxel block corresponding to the voxel block information. Based on the voxel block information set, determine the data acquisition point set; For each data collection point in the set of data collection points, perform the following processing steps: Based on the voxel block information corresponding to the data acquisition point, the real-time status of the power distribution pipeline is acquired at the data acquisition point to obtain the real-time status information of the pipeline. Based on the real-time status information of the pipeline, the influence domain is determined, wherein the influence domain is a finite domain centered on the voxel block corresponding to the data acquisition point; Based on the real-time status information of the pipeline, the local power distribution pipelines contained in at least one voxel block within the influence domain of the initial 3D model are updated. The step of performing variable-scale voxel block segmentation on the initial 3D model to obtain a voxel block information set includes: The initial three-dimensional model is segmented to obtain a set of local three-dimensional models, wherein the local three-dimensional models are the three-dimensional models of power distribution pipelines corresponding to a single power distribution pipeline; For each local 3D model in the set of local 3D models, perform the following first voxel block processing steps: Based on the target length and the initial quantity, the initial voxel block description information is determined, wherein the target length is the pipe length of the power distribution pipeline corresponding to the local 3D model, the initial quantity is the pre-set number of voxel blocks for the local 3D model, and the initial voxel block description information characterizes the voxel block specification corresponding to the initial voxel block. Based on the initial voxel block description information, the local 3D model is segmented into initial voxel blocks to obtain an initial voxel block group. For each initial voxel block in the resulting initial voxel block set, perform the following voxel block processing steps: Determine the positional relationship between the initial voxel block and the local power distribution conduit at the corresponding position of the initial voxel block; In response to the positional relationship being a first positional relationship, the initial voxel block is shrunk to obtain a shrunk voxel block, and voxel block information corresponding to the shrunk voxel block is generated, wherein the first positional relationship is an inclusion relationship that characterizes the inclusion of local power distribution pipes within the initial voxel block; In response to the second positional relationship, the initial voxel block is enlarged to obtain an enlarged voxel block, and voxel block information corresponding to the enlarged voxel block is generated, wherein the second positional relationship is a relationship that characterizes the inclusion relationship of the initial voxel block being contained in the local power distribution pipeline.

2. The method according to claim 1, characterized in that, The process of generating an initial three-dimensional model based on the power distribution pipeline layout diagram includes: Uniform key point sampling is performed on the power distribution pipeline layout diagram to obtain an initial key point set; For each initial keypoint in the initial keypoint set, perform the following first keypoint processing step: Extract the key point description information corresponding to the initial key point, wherein the key point description information includes: key point location, pipe specification to which the key point belongs, and a list of key point connection relationships; Based on the list of key point connection relationships included in the key point description information, the connectivity of the key points corresponding to the initial key points is determined; In response to the connectivity of the key points being a first target value, the key point type of the initial key point is determined to be a first type, wherein the first target value is 1, and the first type represents the initial key point as a boundary point type key point; In response to the keypoint connectivity being a second target value, the keypoint type of the initial keypoint is determined to be a second type, wherein the second target value is 2, and the second type represents the initial keypoint as an intermediate point type keypoint; In response to the key point connectivity being greater than the second target value, the initial key point type is determined to be a third type, wherein the third type represents the initial key point as a connection point type key point; The initial 3D model is generated based on the key point type and key point description information corresponding to the initial key points in the initial key point set.

3. The method according to claim 2, characterized in that, The step of generating the initial 3D model based on the keypoint type and keypoint description information corresponding to the initial keypoints in the initial keypoint set includes: For each candidate keypoint in the candidate keypoint set, perform the following second keypoint processing step, where the candidate keypoint is an initial keypoint of type second from the initial keypoint set: Based on the list of key point connection relationships included in the key point description information corresponding to the candidate key point, the key point connection line is determined, wherein the key point connection line represents the straight line equation between two initial key points adjacent to the candidate key point. In response to the fact that the location of the key point corresponding to the candidate key point is not located on the line connecting the key points, the candidate key point is determined as the target key point; The initial key points in the initial key point set that are of type 1 or type 3 are identified as target key points; For each target key point in the obtained set of target key points, update the list of key point connection relationships included in the key point description information corresponding to the target key point; Based on the obtained set of target key points and the corresponding key point description information, including the updated list of key point connection relationships and key point locations, a 3D map of key points is generated. Based on the key point description information corresponding to the target key points, including the pipeline specifications to which the key points belong, the 3D map of the key points is rendered as a power distribution pipeline to obtain the initial 3D model.

4. The method according to claim 3, characterized in that, The step of determining the data acquisition point set based on the voxel block information set includes: For each voxel block information in the voxel block information set, feature extraction is performed on the voxel block information to generate voxel block features, wherein the voxel block features include: voxel block description features and pipeline description features; Based on the voxel block information features corresponding to the voxel block information, the voxel block importance of each voxel block information in the voxel block information set is determined, wherein the voxel block importance is used to characterize the structural importance of the local power distribution pipeline within the voxel block in the initial three-dimensional model. Based on the importance of the voxel block corresponding to the voxel block information, a target number of voxel blocks are selected from the voxel blocks corresponding to the voxel block information in the voxel block information set as a candidate data sampling point set. Candidate data sampling points that meet the filtering criteria are selected from the candidate data sampling point set and used as data sampling points to obtain the data sampling point set. The filtering criteria are: the sampling point distance between the candidate data sampling point and its neighboring data sampling points is greater than or equal to a preset distance.

5. The method according to claim 4, characterized in that, The step of determining the influence domain based on the real-time status information of the pipeline includes: Based on the real-time status information of the pipeline, determine the real-time status characteristics of the pipeline; Determine the feature differences between the real-time status features of the pipeline and the pipeline description features corresponding to the voxel block information of the data acquisition point; Based on the aforementioned differences in characteristics, the extent of coverage is determined; The influence domain is obtained by mapping based on the said sweep rate.

6. The method according to claim 5, characterized in that, The step of updating the model of local power distribution pipelines contained in at least one voxel block within the influence domain of the initial 3D model based on the real-time status information of the pipeline includes: Based on the real-time status information of the pipeline, taking the voxel block corresponding to the data acquisition point as the starting voxel block, a diffusion-style model update is performed on the local power distribution pipeline contained in at least one voxel block within the influence domain in the initial 3D model.

7. A data processing device for use in underground power distribution pipelines, characterized in that, include: The generation unit is configured to generate an initial three-dimensional model based on the power distribution pipeline layout diagram, wherein the power distribution pipeline layout diagram is marked with the pre-planned pipeline distribution of underground power distribution pipelines for the target area; A voxel block segmentation unit is configured to perform variable-scale voxel block segmentation on the initial three-dimensional model to obtain a voxel block information set, wherein the voxel block information includes: voxel block description information and pipe description information, wherein the pipe description information represents the pipe specification corresponding to the local power distribution pipe located in the voxel block corresponding to the voxel block information. The determining unit is configured to determine a set of data acquisition points based on the set of voxel block information. The execution unit is configured to perform the following processing steps for each data acquisition point in the set of data acquisition points: acquiring real-time status data of the power distribution pipeline at the data acquisition point based on the voxel block information corresponding to the data acquisition point, thereby obtaining real-time pipeline status information; determining an influence domain based on the real-time pipeline status information, wherein the influence domain is a finite domain centered on the voxel block corresponding to the data acquisition point; and updating the model of at least one local power distribution pipeline contained in at least one voxel block within the influence domain in the initial 3D model based on the real-time pipeline status information. The step of performing variable-scale voxel block segmentation on the initial 3D model to obtain a voxel block information set includes: The initial three-dimensional model is segmented to obtain a set of local three-dimensional models, wherein the local three-dimensional models are the three-dimensional models of power distribution pipelines corresponding to a single power distribution pipeline; For each local 3D model in the set of local 3D models, perform the following first voxel block processing steps: Based on the target length and the initial quantity, the initial voxel block description information is determined, wherein the target length is the pipe length of the power distribution pipeline corresponding to the local 3D model, the initial quantity is the pre-set number of voxel blocks for the local 3D model, and the initial voxel block description information characterizes the voxel block specification corresponding to the initial voxel block. Based on the initial voxel block description information, the local 3D model is segmented into initial voxel blocks to obtain an initial voxel block group. For each initial voxel block in the resulting initial voxel block set, perform the following voxel block processing steps: Determine the positional relationship between the initial voxel block and the local power distribution conduit at the corresponding position of the initial voxel block; In response to the positional relationship being the first positional relationship, the initial voxel block is shrunk to obtain the shrunk voxel block, and voxel block information corresponding to the shrunk voxel block is generated. In response to the second positional relationship, the initial voxel block is enlarged to obtain the enlarged voxel block, and voxel block information corresponding to the enlarged voxel block is generated.

8. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 6.

9. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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