KKS code generation method and system based on space grid and three-dimensional data fusion

Abstract

The invention provides a KKS code generation method and system based on space grid and three-dimensional data fusion, and the method comprises the steps: obtaining an original three-dimensional space data set covering a target facility region and a basic attribute data set of a corresponding facility, and carrying out the grid division of the original three-dimensional space data set; generating a multi-level spatial grid unit set matched with the spatial range of the target facility region, and performing association mapping on the basic attribute data set and the multi-level spatial grid unit set to generate a fusion data set with a binding relationship between the spatial position and the attribute information, and calling a pre-constructed KKS coding rule model to carry out coding generation on the fusion data set, obtaining a KKS coding sequence corresponding to the facility space position and attribute information, carrying out uniqueness verification, generating a verification result set containing a code passing verification and a code not passing verification, and carrying out adjustment processing to generate an effective KKS code. According to the method, the quality and practicability of KKS coding are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, to a method and system for generating KKS codes based on the fusion of spatial grids and three-dimensional data. Background Art

[0002] With the increasing demand for the standardization of industrial facility management, KKS code generation technology has been widely applied in complex industrial systems such as power and chemical industries. Its core is to uniquely identify key information such as the type, function, and spatial location of facilities through codes, providing a unified identification basis for facility operation and maintenance, repair, and management. Currently, common KKS code generation methods usually match coding rules based on the basic attribute information of facilities. Some methods combine two-dimensional plane coordinate information to assist in positioning, but mainly use grid division with a fixed resolution or simple spatial area annotation as the carrier of spatial information. However, due to the insufficient utilization of the three-dimensional spatial data characteristics of facilities, traditional methods have two deficiencies: one is that grid division cannot adapt to the spatial complexity of different components of facilities, resulting in inaccurate expression of the spatial location information of the codes; the other is that the association method between attribute information and spatial information is relatively simple, and the codes can only reflect the single-dimensional characteristics of facilities, making it difficult to carry the dual information of "what the facility is" and "where the facility is" simultaneously, and it is easy to cause problems such as code duplication or unclear identification due to insufficient information dimensions. Summary of the Invention

[0003] The present invention provides a method and system for generating KKS codes based on the fusion of spatial grids and three-dimensional data.

[0004] In a first aspect, an embodiment of the present invention provides a method for generating KKS codes based on the fusion of spatial grids and three-dimensional data, the method including: Obtaining an original three-dimensional spatial data set covering the target facility area and a basic attribute data set of the corresponding facility; Performing grid division on the original three-dimensional spatial data set to generate a multi-level spatial grid cell set matching the spatial range of the target facility area; Associating and mapping the basic attribute data set with the multi-level spatial grid cell set to generate a fusion data set with a binding relationship between spatial position and attribute information; Invoking a pre-constructed KKS coding rule model to perform coding generation on the fusion data set to obtain a KKS coding sequence corresponding to the facility spatial position and attribute information; Performing uniqueness verification on the KKS coding sequence to generate a verification result set including verified codes and unverified codes, and performing parameter adjustment processing on the fusion data set corresponding to the unverified codes to regenerate valid KKS codes.

[0005] Second aspect, an embodiment of the present invention provides a computer system, including: A memory in which a computer program is stored; A processor for loading the computer program to implement the KKS code generation method based on the fusion of spatial grid and three-dimensional data as described above.

[0006] The KKS code generation method based on the fusion of spatial grid and three-dimensional data provided by the present invention can simultaneously obtain the spatial position information and attribute feature information of the facility by acquiring the original three-dimensional spatial data set covering the target facility area and the basic attribute data set of the corresponding facility, providing a multi-dimensional data basis for code generation; performing grid division processing on the original three-dimensional spatial data set to generate a multi-level spatial grid cell set, and the grid resolution can be dynamically adjusted according to the spatial complexity of different parts of the facility, avoiding the problem that the traditional single-resolution grid cannot accurately match the facility structure, and making the grid division more conform to the actual spatial distribution of the facility; performing an association mapping process on the basic attribute data set and the multi-level spatial grid cell set to generate a fusion data set, realizing the deep binding of the facility attribute information and the spatial position information, enabling the code to carry the dual information of "what the facility is" and "where the facility is", enriching the information volume of the code; calling the pre-constructed KKS code rule model to perform code generation processing on the fusion data set to ensure that the code meets the requirements of the standardization rules; performing uniqueness verification processing on the generated KKS code sequence and adjusting the parameters for the codes that fail the verification to regenerate valid codes, avoiding the problem of duplicate coding caused by data feature defects in the traditional coding method, forming a dynamically optimized code generation link, and effectively improving the effectiveness and reliability of the code. Through the synergistic effect of the above steps, this method can generate KKS codes that accurately reflect the spatial position and attribute characteristics of the facility and meet the uniqueness requirements, solving the problems of single information dimension, inaccurate grid division, high coding repetition rate, etc. in the traditional coding method, and significantly improving the quality and practicality of the KKS code. Description of the Drawings

[0007] Figure 1 is a flowchart of a KKS code generation method based on the fusion of spatial grid and three-dimensional data provided by an embodiment of the present invention.

[0008] Figure 2 is a schematic diagram of the composition of a computer system provided by an embodiment of the present invention. Detailed Embodiments

[0009] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0010] Please refer to Figure 1 , Figure 1 which is a flowchart of a KKS code generation method based on the fusion of spatial grid and three-dimensional data provided by an embodiment of the present invention. The KKS code generation method based on the fusion of spatial grid and three-dimensional data can be executed by a computer system, and the KKS code generation method based on the fusion of spatial grid and three-dimensional data may include the following steps: Step S100: Obtain an original three-dimensional space data set covering the target facility area and a basic attribute data set of the corresponding facility.

[0011] The original three-dimensional space data set contains point cloud information with spatial coordinate marks collected continuously, and the basic attribute data set contains facility type identifiers, functional classification descriptions, and installation environment feature descriptions.

[0012] The original three-dimensional space data set refers to a data set containing point cloud information obtained after collecting spatial information of the target facility area. These point cloud information have the characteristics of continuous collection, and each point is marked with spatial coordinates, which can accurately reflect the three-dimensional space structure of the target facility area. Point cloud information is a set composed of a large number of points, and each point represents a specific position in the target facility area. Its spatial coordinate marks usually include X coordinates, Y coordinates, and Z coordinates, which are used to determine the specific position of the point in the three-dimensional space. The basic attribute data set is a set of attribute information related to the target facility. Among them, the facility type identifier is used to clarify the specific type of the facility, such as whether it is an electrical equipment, a mechanical equipment, or other types of facilities; the functional classification description details the function of the facility, such as whether the facility is used for power generation, transmission, or other functions; the installation environment feature description describes the environmental characteristics of the facility installation.

[0013] The process of obtaining the original three-dimensional spatial data set covering the target facility area and the basic attribute data set of the corresponding facility may include, for example: collecting spatial information on the target facility area through a laser scanning device to obtain an original point cloud data sequence containing X coordinates, Y coordinates, and Z coordinates. Then, filtering the noise of the original point cloud data sequence to remove discrete abnormal points caused by device errors and generating an effective point cloud data set with continuous spatial distribution characteristics. Next, obtaining the basic attribute record file of the target facility, which includes type identification fields, function classification fields, and installation environment characteristic fields during the design stage of the facility. After that, cleaning the fields of the basic attribute record file to remove duplicate or missing field contents and generating a basic attribute data set containing complete attribute information. Finally, aligning the time stamps of the effective point cloud data set and the basic attribute data set to make them consistent in the data collection time dimension, thereby obtaining the original three-dimensional spatial data set and the basic attribute data set.

[0014] As an implementation manner, step S100 may specifically include the following steps S110 to S150: Step S110: Collect spatial information on the target facility area through a laser scanning device to obtain an original point cloud data sequence containing X coordinates, Y coordinates, and Z coordinates.

[0015] The original point cloud data sequence is a set of a series of points collected by a laser scanning device. Each point contains X coordinates, Y coordinates, and Z coordinates, and these coordinates are used to represent the position of the point in three-dimensional space.

[0016] Specifically, the scanning parameters of the laser scanning device can be set to a preset scanning frequency and scanning angle range to cover the entire spatial range of the target facility area. Then, control the laser scanning device to start from the initial scanning point of the target facility area and perform point-by-point scanning processing according to the preset scanning path. During each scan, record the emission time, reception time, and reflection intensity information of the laser signal. Next, calculate the distance parameter between the scanning point and the laser scanning device according to the time difference between the emission time and the reception time. Then, calculate the X coordinates, Y coordinates, and Z coordinates of the scanning point according to the scanning angle range and the distance parameter. Finally, associate and store the X coordinates, Y coordinates, Z coordinates, and reflection intensity information to generate an original point cloud data sequence containing spatial coordinates and reflection intensity.

[0017] For example, when scanning the interior space of a building, place the laser scanning device at an appropriate location, set the scanning frequency to 1000 times per second, and the scanning angle range to 360 degrees horizontally and 180 degrees vertically to ensure coverage of the entire interior space of the building. Starting from a corner of the building as the initial scanning point, perform point-by-point scanning along a preset spiral scanning path. During each scan, use a high-precision clock to record the emission time and reception time of the laser signal, and simultaneously record the reflection intensity information. Based on the time difference between the emission time and the reception time, calculate the distance parameter between the scanning point and the laser scanning device using the formula for the speed of light. According to the scanning angle range and the distance parameter, calculate the X coordinate, Y coordinate, and Z coordinate of the scanning point through trigonometric functions. For example, in the horizontal direction, X coordinate = d * cos(θ), Y coordinate = d * sin(θ), where θ is the horizontal angle of the scanning point relative to the laser scanning device; in the vertical direction, the Z coordinate is calculated based on the vertical scanning angle and the distance parameter. Finally, store the calculated X coordinate, Y coordinate, Z coordinate, and reflection intensity information in a database to generate the original point cloud data sequence.

[0018] As an implementation, in step S110, collect spatial information of the target facility area through a laser scanning device to obtain the original point cloud data sequence containing X coordinates, Y coordinates, and Z coordinates, which may specifically include the following steps S111 to S116: Step S111: Set the scanning parameters of the laser scanning device to the preset scanning frequency and scanning angle range so as to cover the entire spatial range of the target facility area.

[0019] Scanning parameters refer to the parameters used by the laser scanning device during scanning, including the scanning frequency and the scanning angle range. The scanning frequency refers to the number of laser beams emitted by the laser scanning device per second, which determines the scanning speed and the density of the data. The scanning angle range refers to the angle range that the laser scanning device can scan in the horizontal and vertical directions, which determines the coverage range of the scanning. The preset scanning frequency and scanning angle range are preset according to the size, shape, and complexity of the target facility area to ensure complete coverage of the entire spatial range of the target facility area. The appropriate scanning frequency and scanning angle range can be determined according to the actual situation of the target facility area. Then, set the scanning parameters to the preset values through the operation interface or programming interface of the laser scanning device.

[0020] Step S112: Control the laser scanning device to start from the initial scanning point of the target facility area and perform point-by-point scanning processing along the preset scanning path.

[0021] The initial scan point is the starting position where the laser scanning device begins to scan, and it is predetermined according to the characteristics of the target facility area and the scanning requirements. The preset scanning path refers to the path followed by the laser scanning device during the scanning process, which can be in different shapes such as a straight line, a spiral line, a grid line, etc. The purpose is to ensure that the target facility area can be scanned comprehensively and evenly.

[0022] For example, move the laser scanning device to the position of the initial scan point, and perform calibration and positioning to ensure the accurate position and attitude of the device. Then, according to the preset scanning path, control the laser scanning device to perform point-by-point scanning in sequence and at a certain speed. During the scanning process, a motor can be used to drive the rotation and movement of the laser scanning device to achieve scanning in different directions.

[0023] Step S113: Record the emission time, reception time, and reflection intensity information of the laser signal during each scan.

[0024] The emission time of the laser signal is the moment when the laser scanning device emits the laser beam, the reception time is the moment when the laser beam is reflected back from the target object and received by the laser scanning device, and the reflection intensity information is the energy information lost by the laser beam during the reflection process, which is related to factors such as the material and surface roughness of the target object.

[0025] For example, in the hardware circuit of the laser scanning device, a high-precision clock chip is used to record the emission time and reception time of the laser signal. At the same time, a photodetector is used to detect the intensity of the reflected laser beam and convert it into an electrical signal for recording.

[0026] Step S114: Calculate the distance parameter between the scan point and the laser scanning device based on the time difference between the emission time and the reception time.

[0027] The time difference between the emission time and the reception time reflects the time taken for the laser beam to travel from emission to reception. According to the speed-of-light formula, the distance parameter between the scan point and the laser scanning device can be calculated.

[0028] Step S115: Calculate the X coordinate, Y coordinate, and Z coordinate of the scan point based on the scanning angle range and the distance parameter.

[0029] The scanning angle range includes the horizontal scanning angle and the vertical scanning angle. Through trigonometric functions, the X coordinate, Y coordinate, and Z coordinate of the scan point can be calculated based on the scanning angle range and the distance parameter. The specific process has been described above and will not be elaborated here.

[0030] Step S116: Correlate and store the X coordinate, Y coordinate, Z coordinate, and reflection intensity information to generate an original point cloud data sequence containing spatial coordinates and reflection intensity.

[0031] Associative storage means storing the X coordinate, Y coordinate, Z coordinate and reflection intensity information in accordance with a set format and rules, so that there is a corresponding relationship between them. The original point cloud data sequence is a set composed of these stored data.

[0032] Specifically, in the database, a data table can be created, including fields such as X coordinate, Y coordinate, Z coordinate and reflection intensity information, and the data obtained from each scan is inserted into the data table. In the file system, a text file or a binary file can be used to store the data, and the X coordinate, Y coordinate, Z coordinate and reflection intensity information are written into the file in sequence according to the set format. Exemplarily, when using the database to store data, a data table named "point_cloud" is created, including four fields: "x_coordinate", "y_coordinate", "z_coordinate" and "reflection_intensity". After the data is obtained from each scan, the calculated X coordinate, Y coordinate, Z coordinate and reflection intensity information are inserted into the data table. When using the file system to store data, a text file is created, and the data obtained from each scan is written into the file in the format of "X coordinate, Y coordinate, Z coordinate, reflection intensity", and each line represents the data of a scan point. In this way, the original point cloud data sequence containing spatial coordinates and reflection intensity can be generated.

[0033] Step S120: Perform noise filtering on the original point cloud data sequence, remove the discrete abnormal points generated due to equipment errors, and generate an effective point cloud data set with continuous spatial distribution characteristics.

[0034] Discrete abnormal points refer to points that are far away from the surrounding points due to errors of the laser scanning device, unevenness of the target object surface, etc. The effective point cloud data set refers to the point cloud data set with continuous spatial distribution characteristics obtained after removing the noise points.

[0035] Exemplarily, a statistical filtering algorithm can be used for noise filtering. First, calculate the statistical characteristics of the neighborhood points of each point in the original point cloud data sequence, such as the average distance and standard deviation of the neighborhood points. Then, set a threshold according to the statistical characteristics, and remove the points whose distance from the neighborhood points exceeds the threshold as discrete abnormal points.

[0036] Step S130: Obtain the basic attribute record file of the target facility. The basic attribute record file includes the type identification field, function classification field and installation environment feature field of the facility in the design stage.

[0037] The basic attribute record file refers to a file created during the design phase of the target facility that contains facility-related attribute information. The type identification field is used to specify the specific type of the facility, the functional classification field details the function of the facility, and the installation environment characteristic field describes the environmental characteristics of the facility installation.

[0038] The execution process of obtaining the basic attribute record file of the target facility is as follows: the basic attribute record file can be obtained from the design document, database or other storage medium of the target facility.

[0039] Step S140: Clean the fields of the basic attribute record file, remove duplicate or missing field contents, and generate a basic attribute data set containing complete attribute information.

[0040] Field cleaning is to check and process the field contents in the basic attribute record file, remove duplicate or missing field contents, and ensure that the generated basic attribute data set contains complete attribute information.

[0041] Specifically, each field in the basic attribute record file can be traversed to check whether there is repeated field content. If there is repeated field content, only one of them is retained. Then, each field is checked to see whether there is missing content. If there is missing content, the field is supplemented or deleted according to the actual situation.

[0042] Step S150: aligning the timestamps of the valid point cloud data set and the basic attribute data set to ensure consistency between the two in the data acquisition time dimension, thereby obtaining the original three-dimensional space data set and the basic attribute data set.

[0043] Timestamp alignment is to match and align the data in the valid point cloud dataset and the basic attribute dataset according to the acquisition time to ensure that the two are consistent in the data acquisition time dimension. For example, the acquisition time of each data point is recorded in the valid point cloud dataset and the basic attribute dataset. By comparing the acquisition time, data points with similar acquisition time are associated.

[0044] For example, in the valid point cloud data set, each point cloud data has a collection timestamp, and in the basic attribute data set, each attribute data also has a collection timestamp. By writing a program, traverse the valid point cloud data set and the basic attribute data set, and associate the data points whose collection time difference is within a set range (such as 1 second). In this way, the valid point cloud data set and the basic attribute data set can be time-stamped and aligned to obtain the original three-dimensional space data set and the basic attribute data set.

[0045] Step S200: Perform grid division on the original three-dimensional space data set to generate a multi-level space grid cell set that matches the spatial range of the target facility area. The multi-level space grid cell set includes grid structures with different spatial resolutions.

[0046] Grid division is to divide the three-dimensional space represented by the original three-dimensional space data set into several small grid cells. The multi-level space grid cell set is a set composed of grid cells with different levels and different spatial resolutions. Spatial resolution refers to the size of the grid cell. Different spatial resolutions can better adapt to different structures and details of the target facility area.

[0047] Exemplarily, extract the maximum and minimum values of the X, Y, and Z coordinates of all point clouds in the original three-dimensional space data set to determine the spatial boundary range of the target facility area. Then, select an initial grid resolution according to the spatial boundary range, and perform three-dimensional space division on the target facility area with the initial grid resolution as the standard to generate a basic space grid cell set. Next, improve the resolution of the grid cells in the basic space grid cell set that contain key components of the facility, and reduce their grid size to a preset ratio of the initial grid resolution to generate a first-level grid set containing fine grid cells. At the same time, reduce the resolution of the grid cells in the basic space grid cell set that only contain the support structure of the facility, and expand their grid size to a preset multiple of the initial grid resolution to generate a second-level grid set containing rough grid cells. Finally, perform hierarchical association on the basic space grid cell set, the first-level grid set, and the second-level grid set to generate a multi-level space grid cell set with different spatial resolutions.

[0048] As an implementation manner, step S200 may specifically include the following steps S210 to S250: Step S210: Extract the maximum and minimum values of the X, Y, and Z coordinates of all point clouds in the original three-dimensional space data set to determine the spatial boundary range of the target facility area.

[0049] The spatial boundary range of the target facility area is the smallest three-dimensional space range that can completely contain the target facility area. This range can be determined by extracting the maximum and minimum values of the X, Y, and Z coordinates of all point clouds in the original three-dimensional space data set.

[0050] Exemplarily, traverse all point cloud data in the original three-dimensional space data set, and record the X, Y, and Z coordinates of each point cloud. During the traversal process, compare the size of each coordinate value to find the maximum and minimum values of the X coordinate, the maximum and minimum values of the Y coordinate, and the maximum and minimum values of the Z coordinate. These maximum and minimum values determine the spatial boundary range of the target facility area.

[0051] Step S220: Select an initial grid resolution according to the spatial boundary range, and perform three-dimensional spatial division on the target facility area based on the initial grid resolution to generate a set of basic spatial grid cells.

[0052] The initial grid resolution is the size of the grid cells first selected during grid division, and the set of basic spatial grid cells is a set composed of grid cells obtained by dividing according to the initial grid resolution.

[0053] Exemplarily, calculate the length in the X-axis direction, the width in the Y-axis direction, and the height in the Z-axis direction of the spatial boundary range. Then, select the initial grid resolution according to the length, width, and height, so that the size of each basic spatial grid cell in the X-axis, Y-axis, and Z-axis directions is the initial grid resolution. Next, starting from the minimum X coordinate, minimum Y coordinate, and minimum Z coordinate of the spatial boundary range, divide the grid lines in the X-axis, Y-axis, and Z-axis directions in sequence according to the initial grid resolution. Finally, the intersection points of the grid lines in the X-axis, Y-axis, and Z-axis directions form grid vertices in three-dimensional space, and the adjacent grid vertices enclose basic spatial grid cells with a cubic structure. Store all the basic spatial grid cells in a set to generate a set of basic spatial grid cells.

[0054] As an implementation manner, step S220 may specifically include the following steps S221 to S224: Step S221: Calculate the length in the X-axis direction, the width in the Y-axis direction, and the height in the Z-axis direction of the spatial boundary range.

[0055] The length of the spatial boundary range in the X-axis direction refers to the difference between the maximum coordinate value and the minimum coordinate value of the target facility area in the X-axis direction. The width in the Y-axis direction refers to the difference between the maximum coordinate value and the minimum coordinate value of the target facility area in the Y-axis direction. The height in the Z-axis direction refers to the difference between the maximum coordinate value and the minimum coordinate value of the target facility area in the Z-axis direction.

[0056] Exemplarily, by obtaining the maximum and minimum values of the X coordinate, Y coordinate, and Z coordinate of the spatial boundary range, calculate the differences between them respectively.

[0057] Step S222: Select an initial grid resolution according to the length, width, and height, so that the size of each basic spatial grid cell in the X-axis, Y-axis, and Z-axis directions is the initial grid resolution.

[0058] Exemplarily, an appropriate initial grid resolution can be selected according to experience or actual needs. For example, if the structure of the target facility area is relatively simple, a larger initial grid resolution can be selected; if the structure of the target facility area is relatively complex and more details need to be described, a smaller initial grid resolution is selected.

[0059] Step S223: Starting from the minimum X coordinate, minimum Y coordinate, and minimum Z coordinate of the spatial boundary range, divide the grid lines in the X-axis, Y-axis, and Z-axis directions in sequence according to the initial grid resolution.

[0060] Grid lines are virtual lines used to divide a three-dimensional space. Dividing the grid lines in the X-axis, Y-axis, and Z-axis directions in sequence according to the initial grid resolution can divide the target facility area into several small grid cells. Exemplarily, starting from the minimum X coordinate of the spatial boundary range, a grid line is divided in the X-axis direction at intervals of the distance of the initial grid resolution; starting from the minimum Y coordinate of the spatial boundary range, a grid line is divided in the Y-axis direction at intervals of the distance of the initial grid resolution; starting from the minimum Z coordinate of the spatial boundary range, a grid line is divided in the Z-axis direction at intervals of the distance of the initial grid resolution.

[0061] Step S224: The intersection points of the grid lines in the X-axis, Y-axis, and Z-axis directions form grid vertices in the three-dimensional space. The adjacent grid vertices enclose a basic spatial grid cell with a cubic structure. All the basic spatial grid cells are stored in a set to generate a basic spatial grid cell set.

[0062] Grid vertices are the intersection points of grid lines in the X-axis, Y-axis, and Z-axis directions. A basic spatial grid cell is a small space with a cubic structure enclosed by adjacent grid vertices. A basic spatial grid cell set is a set composed of all these basic spatial grid cells.

[0063] Exemplarily, the intersection coordinates of the grid lines can be calculated to obtain the grid vertices in the three-dimensional space. For each grid vertex, find the other grid vertices adjacent to it, and these adjacent grid vertices enclose a basic spatial grid cell with a cubic structure. All the basic spatial grid cells are stored in a data structure, such as an array or a linked list, to generate a basic spatial grid cell set.

[0064] Step S230: Increase the resolution of the grid cells in the basic spatial grid cell set that contain the key components of the facility, and reduce their grid size to a preset ratio of the initial grid resolution to generate a first-level grid set containing fine grid cells.

[0065] The key components of the facility are the components that play a key role in its function and performance. Resolution increase means reducing the grid size of the grid cells that contain the key components of the facility to better describe the details of the key components. Fine grid cells refer to the grid cells obtained after resolution increase. A first-level grid set is a set composed of these fine grid cells.

[0066] Exemplarily, extract the structural complexity features of the facility components within each grid cell in the basic spatial grid cell set. The structural complexity features are determined based on the uneven feature density on the component surface, the number of connection interfaces, and the depth of the internal cavity. Then, establish a mapping relationship table between the structural complexity and the resolution adjustment ratio. In the mapping relationship table, the higher the structural complexity, the smaller the corresponding resolution reduction ratio. Next, query the mapping relationship table according to the structural complexity features to obtain the corresponding adjustment ratio, and improve the resolution of the grid cells containing the key facility components to generate the first-level grid set containing fine grid cells.

[0067] As an implementation manner, step S230 may specifically include the following steps S231 to S237: Step S231: Extract the structural complexity features of the facility components within each grid cell in the basic spatial grid cell set. The structural complexity features are determined based on the uneven feature density on the component surface, the number of connection interfaces, and the depth of the internal cavity.

[0068] The structural complexity features are the features used to describe the structural complexity of the facility components. The uneven feature density on the component surface refers to the ratio of the number of uneven features on the component surface to the surface area of the component. The number of connection interfaces refers to the number of interfaces where the component is connected to other components. The depth of the internal cavity refers to the depth of the internal cavity of the component.

[0069] Exemplarily, for each grid cell in the basic spatial grid cell set, calculate the uneven feature density on the component surface, the number of connection interfaces, and the depth of the internal cavity of the facility components within the grid cell by means of 3D modeling or point cloud data analysis. Then, combine these features to form the structural complexity features. For example, for a mechanical part within a grid cell, use a 3D laser scanner to scan the part to obtain its point cloud data. Through the point cloud data analysis algorithm, calculate the number of uneven features and the surface area on the part surface to obtain the uneven feature density on the surface. At the same time, determine the number of connection interfaces and the depth of the internal cavity of the part by analyzing the point cloud data or referring to the design drawings of the part. Combine these features to form the structural complexity features of the part.

[0070] Step S232: Establish a mapping relationship table between the structural complexity and the resolution adjustment ratio. In the mapping relationship table, the higher the structural complexity, the smaller the corresponding resolution reduction ratio.

[0071] The mapping relationship table is a table used to store the corresponding relationship between the structural complexity and the resolution adjustment ratio. The resolution adjustment ratio refers to the ratio of reducing the grid size of the grid cell.

[0072] Exemplarily, according to experience or experimental data, determine the resolution adjustment ratios corresponding to different structural complexities. For example, divide the structural complexity into three levels: high, medium, and low. For high structural complexity, the corresponding resolution adjustment ratio is 0.1; for medium structural complexity, the corresponding resolution adjustment ratio is 0.2; for low structural complexity, the corresponding resolution adjustment ratio is 0.5. Store these corresponding relationships in a table to form a mapping relationship table.

[0073] Step S233: Query the mapping relationship table according to the structural complexity characteristics to obtain the corresponding adjustment ratio, and improve the resolution of the grid cells containing the key components of the facility to generate the first-level grid set containing fine grid cells.

[0074] Querying the mapping relationship table means finding the corresponding resolution adjustment ratio in the mapping relationship table according to the structural complexity characteristics. Resolution improvement means reducing the grid size of the grid cells containing the key components of the facility.

[0075] Exemplarily, for the grid cells containing the key components of the facility in the basic spatial grid cell set, find the corresponding adjustment ratio in the mapping relationship table according to their structural complexity characteristics. Reduce the grid size of this grid cell to the initial grid resolution multiplied by the adjustment ratio. Perform the same processing on all grid cells containing the key components of the facility to generate the first-level grid set containing fine grid cells.

[0076] Step S234: Reduce the resolution of the grid cells in the basic spatial grid cell set that only contain the support structure of the facility, and expand their grid size to a preset multiple of the initial grid resolution to generate the second-level grid set containing rough grid cells.

[0077] The support structure of the facility refers to the structure used to support the target facility, such as steel beams, columns, etc. Resolution reduction means expanding the grid size of the grid cells that only contain the support structure of the facility. Rough grid cells refer to the grid cells obtained after resolution reduction. The second-level grid set is a set composed of these rough grid cells.

[0078] Exemplarily, extract the functional redundancy characteristics of the support structure of the facility in each grid cell of the basic spatial grid cell set. The functional redundancy characteristics are determined according to the uniqueness of the load-bearing direction and the material uniformity of the support structure. Then, establish a mapping relationship table between the functional redundancy and the resolution adjustment multiple. The higher the functional redundancy in the mapping relationship table, the greater the resolution expansion multiple. Next, query the mapping relationship table according to the functional redundancy characteristics to obtain the corresponding adjustment multiple, and reduce the resolution of the grid cells that only contain the support structure of the facility to generate the second-level grid set containing rough grid cells.

[0079] Step S235: Extract the functional redundancy features of the facility support structures within each grid cell in the basic spatial grid cell set. The functional redundancy features are determined based on the uniqueness of the load-bearing direction and the material uniformity of the support structures.

[0080] The functional redundancy features are the features used to describe the degree of functional redundancy of the facility support structures. The uniqueness of the load-bearing direction refers to whether the direction of the force borne by the support structure during the load-bearing process is unique, and the material uniformity refers to whether the material of the support structure is evenly distributed throughout the structure.

[0081] Exemplarily, for each grid cell in the basic spatial grid cell set, the uniqueness of the load-bearing direction and the material uniformity of the facility support structure within the grid cell are determined by means of structural mechanics analysis or material testing. Then, these features are combined to form the functional redundancy features.

[0082] Step S236: Establish a mapping relationship table between the functional redundancy and the resolution adjustment multiple. In the mapping relationship table, the higher the functional redundancy, the larger the corresponding resolution magnification multiple.

[0083] The mapping relationship table is a table used to store the corresponding relationship between the functional redundancy and the resolution adjustment multiple. The resolution adjustment multiple refers to the multiple by which the grid size of the grid cell is enlarged.

[0084] Exemplarily, the resolution adjustment multiple corresponding to different functional redundancies can be determined according to experience or experimental data. For example, the functional redundancy is divided into three levels: high, medium, and low. For high functional redundancy, the corresponding resolution adjustment multiple is 5; for medium functional redundancy, the corresponding resolution adjustment multiple is 3; for low functional redundancy, the corresponding resolution adjustment multiple is 2. These corresponding relationships are stored in a table to form the mapping relationship table.

[0085] Step S237: Query the mapping relationship table according to the functional redundancy features to obtain the corresponding adjustment multiple, and reduce the resolution of the grid cells containing only the facility support structures to generate a second-level grid set containing coarse grid cells.

[0086] Querying the mapping relationship table means finding the corresponding resolution adjustment multiple in the mapping relationship table according to the functional redundancy features. Resolution reduction means enlarging the grid size of the grid cells containing only the facility support structures.

[0087] Exemplarily, for the grid cells containing only the facility support structures in the basic spatial grid cell set, the corresponding adjustment multiple can be found in the mapping relationship table according to their functional redundancy features. The grid size of the grid cell is enlarged to the initial grid resolution multiplied by the adjustment multiple. The same processing is performed on all grid cells containing only the facility support structures to generate a second-level grid set containing coarse grid cells.

[0088] Step S240: Reduce the resolution of the grid cells in the basic spatial grid cell set that only contain facility support structures, expand their grid sizes to a preset multiple of the initial grid resolution, and generate a second-level grid set containing rough grid cells.

[0089] The specific execution process of this step is similar to steps S234 - S237 and will not be elaborated here.

[0090] Step S250: Hierarchically associate the basic spatial grid cell set, the first-level grid set, and the second-level grid set to generate a multi-level spatial grid cell set with different spatial resolutions.

[0091] Hierarchical association means associating the basic spatial grid cell set, the first-level grid set, and the second-level grid set according to preset rules so that they form a multi-level structure with different spatial resolutions. Exemplarily, a data structure can be used to store these grid cell sets and establish the association relationships between them. For example, a tree data structure can be used, with the basic spatial grid cell set as the root node, the first-level grid set and the second-level grid set as child nodes, and the association relationships are established through pointers or indexes between the nodes.

[0092] For example, in a tree data structure, the root node stores the basic spatial grid cell set, and the two child nodes of the root node store the first-level grid set and the second-level grid set respectively. Each grid cell has a unique identifier in the data structure, and the corresponding grid cell can be quickly searched and accessed through the identifier. In this way, the basic spatial grid cell set, the first-level grid set, and the second-level grid set are hierarchically associated to generate a multi-level spatial grid cell set with different spatial resolutions.

[0093] Step S300: Perform an association mapping between the basic attribute data set and the multi-level spatial grid cell set to generate a fused data set with a binding relationship between spatial positions and attribute information.

[0094] Association mapping means corresponding and associating the attribute information in the basic attribute data set with the grid cells in the multi-level spatial grid cell set. A fused data set means a data set in which spatial position information and attribute information are bound together.

[0095] Exemplarily, extract the type identification field of each facility in the basic attribute data set, and determine the core functional area of the facility in the three-dimensional space according to the type identification field. Then, determine the target grid cells in the multi-level spatial grid cell set that coincide with the spatial position of the core functional area. Next, extract the functional classification description field of each facility in the basic attribute data set, and determine the functional association range of the facility according to the functional classification description field. Determine the associated grid cells in the multi-level spatial grid cell set that overlap with the spatial position of the functional association range. Then, extract the installation environment characteristic field of each facility in the basic attribute data set, and determine the environmental impact area of the facility according to the installation environment characteristic field. Determine the environmental grid cells in the multi-level spatial grid cell set that overlap with the spatial position of the environmental impact area. Finally, bind the spatial coordinate information of the target grid cells, the associated grid cells, and the environmental grid cells to the type identification field, the functional classification description field, and the installation environment characteristic field of the corresponding facility one by one to generate a fusion data set with a binding relationship between spatial position and attribute information.

[0096] For example, in the power facility area of a factory, there is the attribute information of a transformer in the basic attribute data set, including the type identification field as "transformer", the functional classification description field as "voltage conversion", and the installation environment characteristic field as "indoor dry environment". According to the type identification field, determine that the core functional area of the transformer in the three-dimensional space is the spatial range where the transformer body is located. In the multi-level spatial grid cell set, find the target grid cells that coincide with the spatial position of this core functional area. According to the functional classification description field, determine that the functional association range of the transformer is the spatial range where the equipment such as cables and switches connected to the transformer are located, and find the associated grid cells in the multi-level spatial grid cell set that overlap with the spatial position of the functional association range. According to the installation environment characteristic field, determine that the environmental impact area of the transformer is the space within a certain range around the transformer, and find the environmental grid cells in the multi-level spatial grid cell set that overlap with the spatial position of the environmental impact area. Finally, bind the spatial coordinate information of the target grid cells, the associated grid cells, and the environmental grid cells to the type identification field, the functional classification description field, and the installation environment characteristic field of the transformer one by one to generate a fusion data set with a binding relationship between spatial position and attribute information.

[0097] As an implementation manner, step S300 may specifically include the following steps S310 to S370: Step S310: Extract the type identification field of each facility in the basic attribute data set, and determine the core functional area of the facility in the three-dimensional space according to the type identification field.

[0098] The type identification field is a field used to clarify the specific type of the facility, and the core functional area refers to the area where the facility realizes its core function in the three-dimensional space.

[0099] Exemplarily, for each facility in the basic attribute data set, its type identification field is extracted. According to the type identification field and the pre-established mapping relationship between the type and the core functional area, the core functional area of the facility in the three-dimensional space is determined.

[0100] Step S320: Determine the target grid cells in the multi-level space grid cell set that coincide with the spatial position of the core functional area.

[0101] The target grid cells refer to the grid cells in the multi-level space grid cell set that coincide with the spatial position of the core functional area of the facility.

[0102] Exemplarily, the spatial range description information of the core functional area is extracted. The spatial range description information includes the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate of the core functional area. Then, for each grid cell in the multi-level space grid cell set, the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate of each grid cell are extracted. Next, it is determined whether the minimum X coordinate of the current grid cell is not less than the minimum X coordinate of the core functional area and the maximum X coordinate is not greater than the maximum X coordinate of the core functional area; it is determined whether the minimum Y coordinate of the current grid cell is not less than the minimum Y coordinate of the core functional area and the maximum Y coordinate is not greater than the maximum Y coordinate of the core functional area; it is determined whether the minimum Z coordinate of the current grid cell is not less than the minimum Z coordinate of the core functional area and the maximum Z coordinate is not greater than the maximum Z coordinate of the core functional area. If all the above judgments are true, the current grid cell is marked as the target grid cell that coincides with the spatial position of the core functional area.

[0103] As an implementation manner, step S320 may specifically include the following steps S321 to S326: Step S321: Extract the spatial range description information of the core functional area. The spatial range description information includes the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate of the core functional area.

[0104] The spatial range description information of the core functional area is the information used to clarify the specific position and size of the core functional area in the three-dimensional space. These coordinate information can be obtained from the design documents of the facility, the three-dimensional model, or the actual measurement data. By extracting this information, it is convenient to compare with the spatial position of the grid cells later.

[0105] Step S322: Traverse each grid cell in the multi-level spatial grid cell set, and extract the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate of each grid cell.

[0106] Exemplarily, use a loop structure to traverse each grid cell in the multi-level spatial grid cell set. For each grid cell, extract the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate from its data structure.

[0107] For example, in a multi-level spatial grid cell set, use a for loop to traverse each grid cell in the set. For each grid cell, assume its data structure is an object containing attributes such as the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate. Extract these coordinate information by accessing the object's attributes.

[0108] Step S323: Determine whether the minimum X coordinate of the current grid cell is not less than the minimum X coordinate of the core functional area and the maximum X coordinate is not greater than the maximum X coordinate of the core functional area.

[0109] This judgment is to determine whether the current grid cell is completely contained within the core functional area in the X-axis direction.

[0110] Exemplarily, compare the minimum X coordinate of the current grid cell with the minimum X coordinate of the core functional area, and at the same time compare the maximum X coordinate of the current grid cell with the maximum X coordinate of the core functional area. If the minimum X coordinate of the current grid cell is not less than the minimum X coordinate of the core functional area and the maximum X coordinate is not greater than the maximum X coordinate of the core functional area, then this condition is true; otherwise it is false.

[0111] Step S324: Determine whether the minimum Y coordinate of the current grid cell is not less than the minimum Y coordinate of the core functional area and the maximum Y coordinate is not greater than the maximum Y coordinate of the core functional area.

[0112] This judgment is to determine whether the current grid cell is completely contained within the core functional area in the Y-axis direction.

[0113] Exemplarily, compare the minimum Y coordinate of the current grid cell with the minimum Y coordinate of the core functional area, and at the same time compare the maximum Y coordinate of the current grid cell with the maximum Y coordinate of the core functional area. If the minimum Y coordinate of the current grid cell is not less than the minimum Y coordinate of the core functional area and the maximum Y coordinate is not greater than the maximum Y coordinate of the core functional area, then this condition is true; otherwise it is false.

[0114] Step S325: Determine whether the minimum Z - coordinate of the current grid cell is not less than the minimum Z - coordinate of the core functional area and whether the maximum Z - coordinate is not greater than the maximum Z - coordinate of the core functional area.

[0115] This judgment is to clarify whether the current grid cell is completely within the range of the core functional area in the Z - axis direction. The judgment process is to compare the minimum Z - coordinate of the current grid cell with the minimum Z - coordinate of the core functional area, and at the same time compare the maximum Z - coordinate of the current grid cell with the maximum Z - coordinate of the core functional area. If the minimum Z - coordinate of the current grid cell is not less than the minimum Z - coordinate of the core functional area and the maximum Z - coordinate is not greater than the maximum Z - coordinate of the core functional area, then this judgment condition holds; otherwise, it does not hold.

[0116] Step S326: If all the above judgments are true, then mark the current grid cell as the target grid cell that coincides with the spatial position of the core functional area.

[0117] When the judgment results of steps S323, S324, and S325 are all true, it means that the current grid cell is completely within the spatial range of the core functional area in the X - axis, Y - axis, and Z - axis directions. At this time, the grid cell can be marked as the target grid cell. The marking method can be to add a marking field in the data structure of the grid cell and set its value to the identifier representing the target grid cell. For example, add a boolean field named "is_target" in the data object of the grid cell. If the grid cell is determined to be the target grid cell, then set the value of the "is_target" field to "true"; if it is not the target grid cell, then set it to "false". Through such marking, it is convenient to filter out the target grid cells from the multi - level spatial grid cell set subsequently.

[0118] Step S330: Extract the functional classification description fields of each facility in the basic attribute data set, and determine the functional association range of the facility according to the functional classification description fields.

[0119] The functional classification description field details the specific functions of the facility, and the functional association range refers to the surrounding spatial range related to the facility function. Exemplarily, first parse the functional keywords in the functional classification description field. The functional keywords include input interfaces, output interfaces, and intermediate processing links, etc. Then, based on the functional keywords, construct the spatial expansion rules of the functional association range. The spatial expansion rules include the extension length of the interface connection direction and the coverage radius of the processing link. Finally, expand outward from the boundary of the core functional area of the facility according to the spatial expansion rules to generate the functional association range including the interface connection path and the processing link coverage area.

[0120] For example, for a sewage treatment facility, the function classification description field is "receiving sewage input and outputting purified water after treatment processes such as filtration, sedimentation, and disinfection". Function keywords parsed from this description are "sewage input interface", "filtration treatment process", "sedimentation treatment process", "disinfection treatment process", and "purified water output interface". Based on these keywords, spatial expansion rules are constructed. For example, it is stipulated that the extension length of the connecting pipe of the sewage input interface is 5 meters, the coverage radius of each treatment process is 3 meters, and the extension length of the connecting pipe of the purified water output interface is 4 meters. Starting from the boundary of the core function area of the sewage treatment facility and expanding outward according to these rules, the range where the sewage input pipe extends outward by 5 meters, the circular area with a radius of 3 meters centered on the center point of each treatment process, and the range where the purified water output pipe extends outward by 4 meters together constitute the function-related range of the sewage treatment facility.

[0121] As an implementation manner, step S330 may specifically include the following steps S331 to S336: Step S331: Parse the function keywords in the function classification description field. The function keywords include input interfaces, output interfaces, and intermediate treatment processes.

[0122] Parsing the function classification description field means analyzing and processing the text content of this field to extract the words that can represent the key parts of the facility's functions. The input interface in the function keywords refers to the entrance where the facility receives external substances or energy, the output interface refers to the exit where the facility outputs the treatment results to the outside, and the intermediate treatment processes are various treatment operations carried out by the facility during the process from input to output.

[0123] The execution process of parsing the function keywords in the function classification description field can use natural language processing technology. First, perform word segmentation on the text of the function classification description field to split it into individual words. Then, by matching with a pre-defined function keyword dictionary, filter out the function keywords among them. For example, for the function classification description field "receiving raw materials through the feed inlet and outputting finished products from the discharge outlet after heating and stirring treatments", using a word segmentation algorithm to split it into words such as "through", "feed inlet", "receive", "raw materials", "after", "heating", "stirring", "treatment", "from", "discharge outlet", "output", "finished products". Then, match with the function keyword dictionary to filter out function keywords such as "feed inlet" (input interface), "heating" (intermediate treatment process), "stirring" (intermediate treatment process), "discharge outlet" (output interface), etc.

[0124] Step S332: Based on the function keywords, construct spatial expansion rules for the function-related range. The spatial expansion rules include the extension length of the connection direction of the interfaces and the coverage radius of the treatment processes.

[0125] The spatial expansion rule is a rule used to determine the specific spatial size and shape of the functional association range. According to different functional keywords, an extension length is set for the interface connection direction, and a coverage radius is set for the intermediate processing link.

[0126] Exemplarily, each functional keyword can be classified first to distinguish input interfaces, output interfaces, and intermediate processing links. Then, according to factors such as the type, scale of the facility, and industry experience, corresponding spatial expansion parameters are set for different types of functional keywords. For example, for industrial production equipment, the extension length of the connection pipes of the input interface and the output interface may be determined according to factors such as the material and flow rate of the pipes, generally ranging from several meters to dozens of meters; the coverage radius of the intermediate processing link is set according to the size of the processing equipment and the requirements of the processing technology, and may be about several meters.

[0127] Step S333: Expand outward from the boundary of the core functional area of the facility according to the spatial expansion rule to generate a functional association range that includes the interface connection path and the coverage area of the processing link.

[0128] Starting from the boundary of the core functional area of the facility, expand outward according to the extension length of the interface connection direction and the coverage radius of the processing link set in the spatial expansion rule, so as to determine the functional association range.

[0129] Exemplarily, for the input interface and the output interface, starting from the corresponding interface position on the boundary of the core functional area, extend the corresponding length along the interface connection direction to form an interface connection path. For the intermediate processing link, with the position of the processing link in the core functional area as the center, draw a circle with the set coverage radius to form the coverage area of the processing link. These interface connection paths and the coverage areas of the processing links together constitute the functional association range. For example, for a power generation equipment, its core functional area is the spatial range where the generator body is located. The input interface is the fuel input pipeline. Starting from the fuel input port on the boundary of the core functional area, extend outward 20 meters according to the spatial expansion rule to form the fuel input interface connection path; the intermediate power generation processing link takes the generator as the center, and the circular area with a coverage radius of 15 meters is the power generation processing link coverage area; the output interface is the power output cable. Starting from the power output port on the boundary of the core functional area, extend outward 18 meters to form the power output interface connection path. The combination of these paths and areas is the functional association range of this power generation equipment.

[0130] Step S334: Determine the associated grid cells that overlap with the spatial position of the functional association range in the multi-level spatial grid cell set.

[0131] An associated grid cell refers to a grid cell in a multi-level spatial grid cell set whose spatial location overlaps with the functional association range of the facility. Exemplarily, when determining the associated grid cell, first calculate the spatial intersection area between the functional association range and the spatial range of each grid cell. The spatial intersection area is the overlapping area of the two on the horizontal projection plane. Then, determine the grid cells with a spatial intersection area greater than the preset area threshold as the associated grid cells whose spatial locations overlap with the functional association range.

[0132] Step S335: Calculate the spatial intersection area between the functional association range and the spatial range of each grid cell. The spatial intersection area is the overlapping area of the two on the horizontal projection plane.

[0133] The method for calculating the spatial intersection area can adopt geometric calculation methods. First, project the functional association range and the spatial range of the grid cell onto the horizontal plane to obtain their projection graphics on the horizontal plane. For regular projection graphics, such as rectangles, circles, etc., corresponding geometric formulas can be used to calculate the intersection area. For example, if the projection of the functional association range on the horizontal plane is a rectangle with length a and width b, and the projection of the grid cell on the horizontal plane is also a rectangle with length c and width d, and the two rectangles have partial overlap. The intersection area can be calculated by determining the length and width of the overlapping part and then using the rectangle area formula. For irregular projection graphics, numerical calculation methods can be adopted, such as discretizing the projection graphics into small pixel points, counting the number of overlapping pixel points, and then converting the intersection area according to the area of the pixel points. For example, the projection of the functional association range on the horizontal plane is a circle with radius r, and the projection of the grid cell on the horizontal plane is a square with side length s. If there is an overlapping part between the circle and the square, the geometric shape of the overlapping part can be determined through mathematical calculation, and then the intersection area can be obtained by using integral or piecewise calculation methods.

[0134] Step S336: Determine the grid cells with a spatial intersection area greater than the preset area threshold as the associated grid cells whose spatial locations overlap with the functional association range.

[0135] The preset area threshold is a pre-set area standard used to judge whether the overlapping degree between the grid cell and the functional association range is large enough to determine whether the grid cell is an associated grid cell. When the calculated spatial intersection area between a certain grid cell and the functional association range is greater than the preset area threshold, that grid cell is determined as an associated grid cell.

[0136] Step S340: Determine the associated grid cells whose spatial locations overlap with the functional association range in the multi-level spatial grid cell set.

[0137] The specific execution process of this step is the same as that of steps S334 - S336, that is, first calculate the spatial intersection area between the functional association range and the spatial range of each grid cell, and then determine the grid cells with the spatial intersection area greater than the preset area threshold as the associated grid cells. Through this step, the grid cells related to the facility's functional association range can be accurately screened out from the multi-level spatial grid cell set, providing a basis for generating the fusion data set in the subsequent steps.

[0138] Step S350: Extract the installation environment characteristic fields of each facility in the basic attribute data set, and determine the environmental impact area of the facility according to the installation environment characteristic fields.

[0139] The installation environment characteristic fields describe the specific environmental conditions of the facility installation, such as temperature, humidity, ventilation conditions, surrounding equipment, etc. The environmental impact area refers to the surrounding spatial range that the facility may affect due to installation environment factors. Exemplarily, through a detailed analysis of the installation environment characteristic fields, the key factors related to environmental impact are identified, such as temperature impact, radiation impact, vibration impact, etc. Then, based on these key factors and the pre-established environmental impact model, the environmental impact area is determined. The environmental impact model can be established based on physical principles, empirical data, or experimental results, and is used to describe the impact range and degree of the facility on the surrounding environment under different installation environments.

[0140] Step S360: Determine the environmental grid cells whose spatial positions overlap with the environmental impact area in the multi-level spatial grid cell set.

[0141] The environmental grid cells refer to the grid cells in the multi-level spatial grid cell set that overlap with the facility's environmental impact area in terms of spatial position. The execution process of determining the environmental grid cells is similar to that of determining the associated grid cells, that is, calculate the spatial intersection area between the environmental impact area and the spatial range of each grid cell, and determine the grid cells with the spatial intersection area greater than the preset area threshold as the environmental grid cells.

[0142] Step S370: Bind the spatial coordinate information of the target grid cells, associated grid cells, and environmental grid cells with the type identification field, function classification description field, and installation environment characteristic field of the corresponding facility one by one to generate a fusion data set with the binding relationship between spatial position and attribute information.

[0143] The binding operation is to associate the spatial coordinate information (X coordinate, Y coordinate, Z coordinate) of the target grid cells, associated grid cells, and environmental grid cells with the type identification field, function classification description field, and installation environment characteristic field of the corresponding facility to form a new data set. Each data element in this data set contains spatial position information and attribute information.

[0144] For example, for a facility, the spatial coordinates of the target grid cell are (X1, Y1, Z1), the spatial coordinates of the associated grid cells are (X2, Y2, Z2), (X3, Y3, Z3), etc., and the spatial coordinates of the environmental grid cells are (X4, Y4, Z4), etc. Bind this spatial coordinate information to the facility type identification field "motor", the functional classification description field "convert electrical energy into mechanical energy", and the installation environment characteristic field "in a well-ventilated machine room". A new data structure can be created, such as a table or an object array with multiple fields, to store the spatial coordinate information and the attribute information in different fields respectively. For example, in a table, each row represents a grid cell and contains fields such as "X coordinate", "Y coordinate", "Z coordinate", "type identification", "functional classification description", "installation environment characteristic", etc. Fill in the corresponding information into the corresponding fields to generate a fused data set with the binding relationship between spatial position and attribute information.

[0145] Step S400: Call the pre-built KKS coding rule model to generate an encoding for the fused data set, obtaining a KKS coding sequence corresponding to the facility's spatial position and attribute information.

[0146] The KKS coding rule model is a pre-built model for converting the spatial position and attribute information of a facility into a specific encoding, and the KKS coding sequence is a series of encodings generated after encoding the fused data set according to this model. The execution process of calling the pre-built KKS coding rule model to generate an encoding for the fused data set is as follows: First, parse the spatial coding rule in the KKS coding rule model, extract the spatial coordinate information of the target grid cell in the fused data set, and generate a first coding segment reflecting the facility's spatial position based on the spatial coordinate information. Then, parse the attribute coding rule in the KKS coding rule model, extract the facility type identification field in the fused data set, and generate a second coding segment reflecting the facility type based on the type identification field. Next, parse the functional coding rule in the KKS coding rule model, extract the facility's functional classification description field in the fused data set, and generate a third coding segment reflecting the facility's function based on the functional classification description field. Then, parse the environmental coding rule in the KKS coding rule model, extract the facility's installation environment characteristic field in the fused data set, and generate a fourth coding segment reflecting the facility's environment based on the installation environment characteristic field. Finally, splice the first coding segment, the second coding segment, the third coding segment, and the fourth coding segment in a preset order to generate a KKS coding sequence corresponding to the facility's spatial position and attribute information.

[0147] For example, for a power transformer facility, its integrated data set includes the spatial coordinate information of the target grid cell, the type identification field "transformer", the functional classification description field "voltage conversion", and the installation environment feature field "outdoor substation". The pre-built KKS coding rule model stipulates that the spatial coding rule is to convert the spatial coordinate information into a string of digital codes according to a set algorithm; the attribute coding rule is to convert the facility type identification into a specific letter code; the functional coding rule is to convert the key functions in the functional classification description into another string of letter codes; the environmental coding rule is to convert the installation environment features into specific symbol codes. According to these rules, first generate the first coding segment according to the spatial coordinates of the target grid cell, such as "1234"; generate the second coding segment according to the type identification field "transformer", such as "TR"; generate the third coding segment according to the functional classification description field "voltage conversion", such as "VT"; generate the fourth coding segment according to the installation environment feature field "outdoor substation", such as "OS". Then, in a preset order, such as the spatial coding segment first, the type coding segment second, the functional coding segment after the type coding segment, and the environmental coding segment after the functional coding segment, splice these coding segments together to obtain the KKS coding sequence "1234 - TR - VT - OS".

[0148] As an implementation manner, step S400 may specifically include the following steps S410 to S450: Step S410: Analyze the spatial coding rule in the KKS coding rule model, extract the spatial coordinate information of the target grid cell in the integrated data set, and generate a first coding segment reflecting the spatial position of the facility according to the spatial coordinate information.

[0149] The spatial coding rule is the rule in the KKS coding rule model for converting spatial coordinate information into codes, and the first coding segment is the coding part for reflecting the spatial position of the facility. Exemplarily, extracting the spatial coordinate information of the target grid cell from the integrated data set usually includes the X coordinate, Y coordinate, and Z coordinate. Then process these coordinate information according to the spatial coding rule to generate the first coding segment.

[0150] Step S420: Analyze the attribute coding rule in the KKS coding rule model, extract the type identification field of the facility in the integrated data set, and generate a second coding segment reflecting the type of the facility according to the type identification field.

[0151] The attribute coding rule is a rule in the KKS coding rule model used to convert the type identification information of facilities into codes. The second coding segment is the coding part used to reflect the facility type. Exemplarily, analyze the attribute coding rule in the KKS coding rule model to clarify its coding correspondence. Extract the type identification field of the facility from the fusion data set, and then convert the type identification field into the corresponding code according to the attribute coding rule.

[0152] For example, the attribute coding rule stipulates the coding mapping table corresponding to different facility types. For example, "generator" corresponds to "GD", "transformer" corresponds to "TR", "motor" corresponds to "MD", etc. If the type identification field of the facility in the fusion data set is "transformer", the second coding segment generated according to the attribute coding rule is "TR".

[0153] Step S430: Analyze the function coding rule in the KKS coding rule model, extract the function classification description field of the facility from the fusion data set, and generate the third coding segment reflecting the facility function according to the function classification description field.

[0154] The function coding rule is a rule in the KKS coding rule model used to convert the function classification description information of facilities into codes. The third coding segment is the coding part used to reflect the facility function. Exemplarily, the function classification description field of the facility can be extracted from the fusion data set, analyzed and processed, and the key function information can be extracted from it. Then convert the key function information into the corresponding code according to the function coding rule. For example, the function coding rule stipulates that function keywords are converted into specific letter combinations. For a water pump facility, its function classification description field is "pump water from a low place to a high place", and the key function information extracted from it is "pump water". In the function coding rule, "pump water" corresponds to the code "WP", so the third coding segment generated according to this rule is "WP".

[0155] Step S440: Analyze the environment coding rule in the KKS coding rule model, extract the installation environment characteristic field of the facility from the fusion data set, and generate the fourth coding segment reflecting the facility environment according to the installation environment characteristic field.

[0156] The environment coding rule is a rule in the KKS coding rule model used to convert the installation environment characteristic information of facilities into codes. The fourth coding segment is the coding part used to reflect the facility installation environment. Exemplarily, extract the installation environment characteristic field of the facility from the fusion data set, analyze this field, and identify the key environment characteristic information in it. Then convert the key environment characteristic information into the corresponding code according to the environment coding rule.

[0157] For example, the environment coding rule stipulates the codes corresponding to different installation environment characteristics. If the installation environment characteristic field of the facility is "in a damp basement", the key environment characteristic information identified is "damp" and "basement". In the environment coding rule, "damp" corresponds to the code "MH", and "basement" corresponds to the code "BS", then these two codes are combined, and the generated fourth code segment is "MH - BS".

[0158] Step S450: Concatenate the first code segment, the second code segment, the third code segment, and the fourth code segment in a preset order to generate a KKS code sequence corresponding to the facility spatial location and attribute information.

[0159] The preset order is the concatenation order of each code segment predefined in the KKS coding rule model. By concatenating the first code segment, the second code segment, the third code segment, and the fourth code segment in this order, a complete KKS code sequence can be obtained. Exemplarily, determine the preset code concatenation order, such as the spatial code segment first, the type code segment second, the function code segment after the type code segment, and the environment code segment after the function code segment. Extract the character sequences of the first code segment, the second code segment, the third code segment, and the fourth code segment, and then connect these code segments in sequence according to the preset order, and use a preset delimiter to separate the code segments. Finally, perform format verification on the concatenated character sequence so that the character length of each code segment meets the requirements of the KKS coding rule model, and use the character sequence that passes the format verification as the KKS code sequence corresponding to the facility spatial location and attribute information.

[0160] For example, the preset code concatenation order is "first code segment - second code segment - third code segment - fourth code segment", and the delimiter is " - ". The first code segment is "532", the second code segment is "TR", the third code segment is "WP", and the fourth code segment is "MH - BS". Concatenating in order gives "532 - TR - WP - MH - BS". Perform format verification on this character sequence to check whether the character length of each code segment meets the provisions of the KKS coding rule model. If it meets the requirements, this sequence is the KKS code sequence corresponding to the facility spatial location and attribute information.

[0161] As an implementation manner, step S450 may specifically include the following steps S451 to S455: Step S451: Determine that the preset code concatenation order is the spatial code segment first, the type code segment second, the function code segment after the type code segment, and the environment code segment after the function code segment.

[0162] The preset encoding splicing order is predefined in the KKS encoding rule model. Such an order can clearly reflect the spatial location, type, function, and installation environment information of the facility in the encoding sequence in turn. The process of determining this order is usually completed in the design stage of the KKS encoding rule model and is determined according to factors such as the requirements of actual applications and the readability and maintainability of the encoding. When executing step S450, directly obtain this preset encoding splicing order from the KKS encoding rule model.

[0163] Step S452: Extract the character sequences of the first encoding segment, the second encoding segment, the third encoding segment, and the fourth encoding segment, and use them as the spatial encoding segment, the type encoding segment, the function encoding segment, and the environment encoding segment respectively.

[0164] In the previous steps, the first encoding segment, the second encoding segment, the third encoding segment, and the fourth encoding segment have been generated respectively, and these encoding segments may be stored in different data forms. In this step, they need to be converted into character sequences for subsequent splicing operations. The extraction method can be processed accordingly according to the storage form of the encoding segment. For example, if the encoding segment is stored in digital form, it can be converted to string type; if it is stored in the form of an object or an array, the key character information can be extracted.

[0165] For example, the first encoding segment is stored as "532" in integer form, and it is converted to the string "532" as the spatial encoding segment; the second encoding segment is stored as "TR" in string form, and it is directly used as the type encoding segment; the third encoding segment is stored in array form, which contains character elements "W" and "P", and these elements are combined into the string "WP" as the function encoding segment; the fourth encoding segment is stored in object form, which contains two attributes "MH" and "BS", and the values of these two attributes are combined into the string "MH - BS" as the environment encoding segment.

[0166] Step S453: Connect the spatial encoding segment, the type encoding segment, the function encoding segment, and the environment encoding segment in turn, and use a preset delimiter to separate them between the encoding segments.

[0167] According to the preset encoding splicing order determined in step S451, connect the spatial encoding segment, the type encoding segment, the function encoding segment, and the environment encoding segment in turn, and use a preset delimiter to separate them between adjacent encoding segments, which can make the encoding sequence clearer and more readable. The preset delimiter can be symbols such as " - ", " / ", etc., which are predefined in the KKS encoding rule model.

[0168] For example, if the spatial coding segment is "532", the type coding segment is "TR", the function coding segment is "WP", the environmental coding segment is "MH - BS", and the preset delimiter is " - ", then the concatenated character sequence is "532 - TR - WP - MH - BS".

[0169] Step S454: Perform format verification on the concatenated character sequence so that the character length of each coding segment meets the requirements of the KKS coding rule model.

[0170] The format verification is to ensure that the generated KKS coding sequence conforms to the specifications of the KKS coding rule model, and the character length of each coding segment needs to be within the specified range. Exemplarily, according to the regulations on the character length of each coding segment in the KKS coding rule model, the concatenated character sequence is split, and the character length of each coding segment is checked to see if it meets the requirements. If the character length of a certain coding segment does not meet the requirements, corresponding processing can be carried out, such as truncating the overly long coding segment or padding zeros after the overly short coding segment.

[0171] For example, the KKS coding rule model stipulates that the character length of the spatial coding segment is 3 - 5 digits, the character length of the type coding segment is 2 - 3 digits, the character length of the function coding segment is 2 - 4 digits, and the character length of the environmental coding segment is 3 - 6 digits. For the concatenated character sequence "532 - TR - WP - MH - BS", checking the character length of the spatial coding segment "532" is 3 digits, which meets the requirements; the character length of the type coding segment "TR" is 2 digits, which meets the requirements; the character length of the function coding segment "WP" is 2 digits, which meets the requirements; the character length of the environmental coding segment "MH - BS" is 5 digits, which meets the requirements. If a certain coding segment does not meet the requirements, such as the spatial coding segment being "53" with a length of 2 digits and not meeting the 3 - 5 digit requirement, zeros can be padded at the back to adjust it to "530".

[0172] Step S455: Use the character sequence that passes the format verification as the KKS coding sequence corresponding to the facility's spatial location and attribute information.

[0173] When the concatenated character sequence passes the format verification and the character length of each coding segment meets the requirements of the KKS coding rule model, this character sequence can be used as the final KKS coding sequence, which accurately reflects information such as the spatial location, type, function, and installation environment of the facility. Store or apply this character sequence to the corresponding system for facility identification and management.

[0174] For example, after format verification, the character sequence "532 - TR - WP - MH - BS" meets the requirements of the KKS coding rule model and is used as the KKS coding sequence corresponding to the spatial location and attribute information of the facility. This coding sequence can be used to uniquely identify the facility in the subsequent facility management system.

[0175] Step S500: Perform uniqueness verification on the KKS coding sequence, generate a verification result set containing the codes that pass the verification and the codes that fail the verification, and perform parameter adjustment processing on the fusion data set corresponding to the codes that fail the verification to regenerate valid KKS codes.

[0176] The uniqueness verification is to ensure that the generated KKS coding sequence is unique in the entire system and avoid the situation of duplicate coding. The verification result set contains the codes that pass the verification and the codes that fail the verification. For the codes that fail the verification, it is necessary to adjust the parameters of the corresponding fusion data set and then regenerate the codes until the generated codes pass the uniqueness verification. Exemplarily, first establish a coding uniqueness verification database, and compare each code in the KKS coding sequence with the existing codes in the coding uniqueness verification database one by one. Record the codes that are repeated with the existing codes in the comparison result as the codes that fail the verification, and record the non-repeated codes as the codes that pass the verification to generate a verification result set. For each code that fails the verification, extract the spatial coordinate information, type identification field, function classification description field, and installation environment feature field in the corresponding fusion data set. Analyze this information to determine whether there are coding duplication problems caused by reasons such as too high or too low grid resolution, too coarse or too fine type classification, general or redundant function description, and environmental feature coverage deviating from the preset range. Adjust the grid resolution level, type classification accuracy, function description depth, and environmental feature coverage range in the fusion data set according to the analysis results, re-call the KKS coding rule model to generate new KKS codes, and perform uniqueness verification processing again until valid KKS codes are generated.

[0177] As an implementation manner, step S500 may specifically include the following steps S510 to S580: Step S510: Establish a coding uniqueness verification database, and compare each code in the KKS coding sequence with the existing codes in the coding uniqueness verification database one by one.

[0178] The coding uniqueness verification database is a database used to store existing KKS codes. By comparing the newly generated KKS code sequence with the existing codes in the database, it can be determined whether the new code is duplicate. The process of establishing the coding uniqueness verification database can use a database management system such as MySQL, Oracle, etc. to create a special table to store coding information. The execution process of comparing each code in the KKS code sequence with the existing codes in the database is as follows: Use a database query statement to retrieve all existing codes from the database, and then sequentially compare each code in the KKS code sequence with these existing codes.

[0179] For example, use the MySQL database to create a table named "kks_codes" with a field named "code" to store KKS codes. For the newly generated KKS code sequence, write a program in Python to connect to the database through a database connection library (such as "pymysql"), and execute the query statement "SELECT code FROM kks_codes" to obtain all existing codes. Then iterate through each code in the KKS code sequence and compare it with the existing codes obtained from the query.

[0180] Step S520: Record the codes that are duplicate with the existing codes in the comparison result as the unverified codes, and record the non-duplicate codes as the verified codes to generate a verification result set.

[0181] During the comparison process, for the KKS codes that are duplicate with the existing codes, mark them as unverified codes; for the KKS codes that are not duplicate with the existing codes, mark them as verified codes. Two lists can be used to store the verified codes and the unverified codes respectively, so as to generate a verification result set.

[0182] Step S530: For each unverified code, extract the spatial coordinate information, type identification field, function classification description field, and installation environment characteristic field in its corresponding fusion data set.

[0183] For the unverified codes, it is necessary to further analyze the reasons for the code duplication, so it is necessary to extract the relevant information in its corresponding fusion data set. The process of extracting this information can be achieved by searching for the record corresponding to the unverified code in the fusion data set. The fusion data set can be a table or an array of objects containing multiple records, and each record contains spatial coordinate information, type identification field, function classification description field, installation environment characteristic field, etc.

[0184] For example, the fused data set is stored in the form of a list containing multiple dictionaries, and each dictionary represents the information of a grid cell, including key-value pairs such as "X coordinate", "Y coordinate", "Z coordinate", "type identifier", "function classification description", and "installation environment characteristics". For a coding that fails verification, by traversing the fused data set, find the record corresponding to the coding, and extract the spatial coordinate information (such as the values of "X coordinate", "Y coordinate", and "Z coordinate"), the value of the type identifier field, the value of the function classification description field, and the value of the installation environment characteristics field.

[0185] Step S540: Analyze the grid resolution level of the spatial coordinate information to determine whether there is a coding duplication problem caused by too high or too low grid resolution.

[0186] The grid resolution level affects the generation of spatial coding segments. If the grid resolution is too high, it may cause the spatial coding to be too detailed and prone to coding duplication; if the grid resolution is too low, it may cause the spatial coding to be too general and may also result in coding duplication. The execution process of analyzing the grid resolution level of the spatial coordinate information and determining whether there is a coding duplication problem is as follows: According to the spatial coordinate information in the fused data set and the relevant data of the grid resolution level, analyze the generation of spatial coding segments. If it is found that multiple different facilities are close in space, but due to too high grid resolution, their spatial coding segments are very similar or even the same, then it may be a coding duplication caused by too high grid resolution; if it is found that the spatial coding segments of facilities in multiple different regions are the same, it may be because the grid resolution is too low to distinguish the spatial positions of different regions.

[0187] Step S550: Analyze the classification accuracy of the type identifier field to determine whether there is a coding duplication problem caused by too coarse or too fine type classification.

[0188] The type classification accuracy affects the generation of type coding segments. If the type classification is too coarse, it may classify different types of facilities into the same category, resulting in duplicate type coding segments; if the type classification is too fine, there may be some unnecessary subdivisions, increasing the complexity of the coding and may also lead to coding duplication. Exemplarily, perform a statistical analysis on the type identifier field to check whether there are cases where the type identifier fields of multiple different facilities are the same but the actual types are different, or whether there are cases where the type identifier field is too detailed resulting in coding duplication.

[0189] Step S560: Analyze the description depth of the function classification description field to determine whether there is a coding duplication problem caused by general or redundant function descriptions.

[0190] The depth of functional description affects the generation of functional coding segments. If the functional description is too general, facilities with different functions may be described as having the same function, resulting in duplicate functional coding segments. If the functional description is too redundant, it may contain some unnecessary information, increasing the complexity of coding and potentially leading to duplicate coding. Exemplarily, perform semantic analysis on the functional classification description field to extract the key functional information therein. Check whether there are cases where the key functional information obtained after processing the functional description fields of multiple different facilities is the same but the actual functions are different, or whether there are cases where the functional description fields contain too much irrelevant information leading to duplicate coding.

[0191] Step S570: Analyze the coverage range of the installation environment feature field to determine whether there is a problem of duplicate coding caused by the coverage of environmental features deviating from the preset range.

[0192] The coverage range of the installation environment feature field affects the generation of environmental coding segments. If the coverage range of environmental features deviates from the preset range, it may lead to inaccurate environmental coding segments, thereby resulting in duplicate coding. Exemplarily, according to the installation environment feature field in the fusion data set and the preset environmental feature coverage range standard, check the generation of environmental coding segments. If it is found that the installation environments of multiple different facilities are actually different, but due to inaccurate coverage of environmental features, their environmental coding segments are the same, then it may be due to duplicate coding caused by the coverage of environmental features deviating from the preset range.

[0193] Step S580: Adjust the grid resolution level, type classification accuracy, depth of functional description, and coverage range of environmental features in the fusion data set according to the analysis results, re - call the KKS coding rule model to generate a new KKS coding, and perform the uniqueness verification process again until a valid KKS coding is generated.

[0194] According to the analysis results of steps S540 - S570, adjust the relevant parameters in the fusion data set. If duplicate coding is caused by too high grid resolution, reduce the grid resolution; if the type classification is too coarse, refine the type classification; if the functional description is too general, increase the depth of the functional description; if the coverage of environmental features deviates from the preset range, adjust the coverage of environmental features. Then re - call the KKS coding rule model to generate a new KKS coding based on the adjusted fusion data set. Compare the new coding with the existing codings in the coding uniqueness verification database again for uniqueness verification. If the new coding still has duplicate problems, continue to repeat the above analysis and adjustment process until the generated coding passes the uniqueness verification and becomes a valid KKS coding.

[0195] For example, after analysis, it is found that the encoding is repeated due to too high grid resolution. The grid resolution is reduced by one level. The KKS encoding rule model is called again, and new KKS encodings are generated based on the adjusted fusion data set. The new encodings are compared with the encoding uniqueness verification database. If the verification is passed, the encoding becomes a valid KKS encoding; if there are still duplicate problems, other parameters are continuously analyzed and adjusted until a valid encoding is generated.

[0196] It can be understood that in the above introductions of the embodiments of the present invention, various algorithms involved, such as statistical filtering algorithms, point cloud data processing algorithms, word segmentation algorithms, etc., can be learned from relevant content in the prior art. For the sake of saving space, they will not be elaborated in the embodiments of the present invention. In addition, those skilled in the art can make detailed supplements according to the common general knowledge in the art when implementing the solutions of the present invention. For example, according to the general knowledge in the art, normalization can be used to eliminate the dimension conflict before feature fusion, interpolation can be used to eliminate the dimension difference, historical data, experience or business scenario requirements can be combined to reasonably set the threshold, the model can be trained based on the general model training method, the number of layers in the model structure can be set based on actual needs, the activation function can be selected, etc. The present invention will no longer give redundant introductions to the overly detailed implementation process.

[0197] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a computer system provided by an embodiment of the present invention. The computer system at least includes a processor 101, a communication interface 102, and a memory 103. Among them, the processor 101, the communication interface 102, and the memory 103 can be connected through a bus or other means. Among them, the processor 101 (or central processing unit (CPU)) is the computing core and control core of the computer system, which can parse various instructions in the computer system and process various data in the computer system. The communication interface 102 can optionally include a standard wired interface, a wireless interface (such as WI-FI, mobile communication interface, etc.), and can be controlled by the processor 101 to be used for sending and receiving data; the communication interface 102 can also be used for the transmission and interaction of internal data in the computer system. The memory 103 (Memory) is the memory device in the computer system, used to store programs and data. It can be understood that the memory 103 here can include both the built-in memory of the computer system and, of course, the extended memory supported by the computer system. The memory 103 provides a storage space, and the operating system of the computer system is stored in this storage space, which is not limited in the present invention.

[0198] In one embodiment, the processor 101 executes the KKS coding generation method based on the fusion of spatial grid and three-dimensional data provided above in the embodiments of the present invention by running a computer program in the memory 103.

Claims

1. A method for generating KKS codes based on the fusion of spatial grids and three-dimensional data, characterized in that, The method comprises: Acquire the original three-dimensional spatial data set covering the target facility area and the basic attribute data set of the corresponding facilities; Gridding the original three-dimensional spatial data set to generate a multi-level spatial grid unit set matching the spatial range of the target facility area; Associatively mapping the basic attribute data set with the multi-level spatial grid unit set to generate a fused data set having a binding relationship between spatial position and attribute information; Calling a pre-built KKS encoding rule model to encode the fused data set to obtain a KKS encoding sequence corresponding to the facility spatial location and attribute information; The uniqueness of the KKS coding sequence is verified to generate a verification result set including codes that passed the verification and codes that failed the verification, and parameter adjustment processing is performed on the fused data set corresponding to the codes that failed the verification to regenerate a valid KKS coding.

2. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 1, wherein The obtaining of the original three-dimensional spatial data set covering the target facility area and the basic attribute data set of the corresponding facilities includes: The laser scanning device is used to collect spatial information of the target facility area to obtain a sequence of original point cloud data including X-coordinates, Y-coordinates, and Z-coordinates; Performing noise filtering on the original point cloud data sequence to remove discrete abnormal points generated by equipment errors, and generating a valid point cloud data set with continuous spatial distribution characteristics; Obtaining a basic attribute record file of a target facility, wherein the basic attribute record file includes a type identification field, a function classification field, and an installation environment feature field of the facility in the design phase; Performing field cleaning on the basic attribute record file, removing duplicate or missing field contents therein, and generating a basic attribute data set containing complete attribute information; The effective point cloud data set and the basic attribute data set are time stamp aligned to ensure consistency between the two in the data acquisition time dimension, thereby obtaining the original three-dimensional space data set and the basic attribute data set.

3. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 2, characterized in that, The laser scanning device is used to collect spatial information of the target facility area to obtain an original point cloud data sequence containing X coordinates, Y coordinates, and Z coordinates, including: Setting the scanning parameters of the laser scanning device to a preset scanning frequency and scanning angle range so as to cover the entire spatial range of the target facility area; Control the laser scanning device to start from the initial scanning point of the target facility area and perform point-by-point scanning processing according to the preset scanning path; Record the emission time, reception time and reflection intensity information of the laser signal during each scan; Calculate the distance parameter between the scanning point and the laser scanning device according to the time difference between the transmitting time and the receiving time; Calculate the X coordinate, Y coordinate and Z coordinate of the scanning point according to the scanning angle range and distance parameters; The X coordinate, Y coordinate, Z coordinate and reflection intensity information are stored in association to generate an original point cloud data sequence including space coordinates and reflection intensity.

4. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 1, wherein The gridding of the original three-dimensional spatial data set to generate a multi-level spatial grid unit set matching the spatial range of the target facility area includes: Extract the maximum and minimum values of the X, Y, and Z coordinates of all point clouds in the original three-dimensional space data set to determine the spatial boundary range of the target facility area; Select an initial grid resolution according to the spatial boundary range, and perform three-dimensional space division on the target facility area based on the initial grid resolution to generate a set of basic space grid cells; Increase the resolution of the grid cells containing the key components of the facility in the set of basic space grid cells, and reduce their grid size to a preset ratio of the initial grid resolution to generate a first-level grid set containing fine grid cells; Reduce the resolution of the grid cells containing only the support structure of the facility in the set of basic space grid cells, and expand their grid size to a preset multiple of the initial grid resolution to generate a second-level grid set containing rough grid cells; Associate the levels of the set of basic space grid cells, the first-level grid set, and the second-level grid set to generate a multi-level space grid cell set with different spatial resolutions.

5. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 4, wherein The step of selecting an initial grid resolution according to the spatial boundary range and performing three-dimensional space division on the target facility area based on the initial grid resolution to generate a set of basic space grid cells includes: Calculate the length in the X-axis direction, the width in the Y-axis direction, and the height in the Z-axis direction of the spatial boundary range; Select an initial grid resolution according to the length, width, and height, so that the size of each basic space grid cell in the X-axis, Y-axis, and Z-axis directions is the initial grid resolution; Starting from the minimum X coordinate, minimum Y coordinate, and minimum Z coordinate of the spatial boundary range, sequentially divide the grid lines in the X-axis, Y-axis, and Z-axis directions according to the initial grid resolution; The intersection points of the grid lines in the X-axis, Y-axis, and Z-axis directions form grid vertices in three-dimensional space, and adjacent grid vertices enclose basic space grid cells with a cube structure. Store all the basic space grid cells in a set to generate the set of basic space grid cells.

6. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 1, wherein, The step of associating and mapping the set of basic attribute data with the multi-level space grid cell set to generate a fused data set with a binding relationship between spatial position and attribute information includes: Extract the type identification field of each facility in the set of basic attribute data, and determine the core functional area of the facility in three-dimensional space according to the type identification field; Determine the target grid cells in the multi-level space grid cell set that coincide with the spatial position of the core functional area; Extract the functional classification description field of each facility in the set of basic attribute data, and determine the functional association range of the facility according to the functional classification description field; Determine the associated grid cells in the multi-level space grid cell set that overlap with the spatial position of the functional association range; Extract the installation environment feature field of each facility in the set of basic attribute data, and determine the environmental impact area of the facility according to the installation environment feature field; Determine the environmental grid cells in the multi-level space grid cell set that overlap with the spatial position of the environmental impact area; Bind the spatial coordinate information of the target grid cell, associated grid cells, and environmental grid cells to the type identification field, function classification description field, and installation environment characteristic field of the corresponding facilities one by one to generate the integrated data set with the binding relationship between spatial position and attribute information.

7. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 6, wherein, Determining the target grid cell that coincides with the spatial position of the core functional area in the multi-level spatial grid cell set includes: Extract the spatial range description information of the core functional area, where the spatial range description information includes the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate of the core functional area; Traverse each grid cell in the multi-level spatial grid cell set and extract the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate of each grid cell; Judge whether the minimum X coordinate of the current grid cell is not less than the minimum X coordinate of the core functional area and whether the maximum X coordinate is not greater than the maximum X coordinate of the core functional area; Judge whether the minimum Y coordinate of the current grid cell is not less than the minimum Y coordinate of the core functional area and whether the maximum Y coordinate is not greater than the maximum Y coordinate of the core functional area; Judge whether the minimum Z coordinate of the current grid cell is not less than the minimum Z coordinate of the core functional area and whether the maximum Z coordinate is not greater than the maximum Z coordinate of the core functional area; If all the above judgments are true, mark the current grid cell as the target grid cell that coincides with the spatial position of the core functional area.

8. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 1, wherein Invoking the pre-constructed KKS coding rule model to encode the integrated data set to obtain the KKS coding sequence corresponding to the facility spatial position and attribute information includes: Analyze the spatial coding rule in the KKS coding rule model, extract the spatial coordinate information of the target grid cell in the integrated data set, and generate the first coding segment reflecting the facility spatial position according to the spatial coordinate information; Analyze the attribute coding rule in the KKS coding rule model, extract the type identification field of the facility in the integrated data set, and generate the second coding segment reflecting the facility type according to the type identification field; Analyze the function coding rule in the KKS coding rule model, extract the function classification description field of the facility in the integrated data set, and generate the third coding segment reflecting the facility function according to the function classification description field; Analyze the environment coding rule in the KKS coding rule model, extract the installation environment characteristic field of the facility in the integrated data set, and generate the fourth coding segment reflecting the facility environment according to the installation environment characteristic field; Determine that the preset coding splicing order is that the spatial coding segment is in the front, the type coding segment is behind, the function coding segment is after the type coding segment, and the environment coding segment is after the function coding segment; Extract the character sequence of the first coding segment as the spatial coding segment, extract the character sequence of the second coding segment as the type coding segment, extract the character sequence of the third coding segment as the function coding segment, and extract the character sequence of the fourth coding segment as the environment coding segment; Connect the space coding segment, type coding segment, function coding segment, and environment coding segment in sequence, and separate the coding segments with a preset delimiter; Perform format verification on the concatenated character sequence to ensure that the character length of each coding segment meets the requirements of the KKS coding rule model; Use the character sequence that passes the format verification as the KKS coding sequence corresponding to the facility space position and attribute information.

9. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 1, wherein The uniqueness verification of the KKS coding sequence generates a verification result set including verified codes and unverified codes, and performs parameter adjustment processing on the fusion data set corresponding to the unverified codes to regenerate valid KKS codes, including: Establish a coding uniqueness verification database, and compare each code in the KKS coding sequence with the existing codes in the coding uniqueness verification database one by one; Record the codes that are repeated with the existing codes in the comparison result as unverified codes, and record the non-repeated codes as verified codes to generate the verification result set; For each unverified code, extract the space coordinate information, type identification field, function classification description field, and installation environment feature field in its corresponding fusion data set; Analyze the grid resolution level of the space coordinate information to determine whether there is a coding repetition problem caused by too high or too low grid resolution; Analyze the classification accuracy of the type identification field to determine whether there is a coding repetition problem caused by too coarse or too fine type classification; Analyze the description depth of the function classification description field to determine whether there is a coding repetition problem caused by general or redundant function descriptions; Analyze the coverage range of the installation environment feature field to determine whether there is a coding repetition caused by the environmental feature coverage deviating from the preset range; Adjust the grid resolution level, type classification accuracy, function description depth, and environmental feature coverage range in the fusion data set according to the analysis results, re-call the KKS coding rule model to generate new KKS codes, and perform uniqueness verification processing again until valid KKS codes are generated.

10. A computer system, characterized in that, Including: A memory that stores a computer program; A processor for loading the computer program to implement the KKS coding generation method based on the fusion of space grid and three-dimensional data as described in any one of claims 1-9.

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