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

Abstract

The present invention provides a KKS code generation method and system based on the fusion of spatial grids and three-dimensional data. The method obtains an original three-dimensional spatial data set covering a target facility area and a basic attribute data set of the corresponding facilities, grids the original three-dimensional spatial data set, generates a multi-level spatial grid unit set matching the spatial range of the target facility area, associates and maps the basic attribute data set with the multi-level spatial grid unit set, generates a fused data set with a binding relationship between spatial location and attribute information, calls a pre-built KKS encoding rule model to encode the fused data set, obtains a KKS code sequence corresponding to the facility spatial location and attribute information, performs uniqueness verification, generates a verification result set including verification pass codes and verification fail codes, and generates a valid KKS code after adjustment processing. The present invention significantly improves the quality and practicality of KKS codes.

Description

Technical Field

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

[0002] With the increasing demand for standardization in industrial facility management, KKS code generation technology has been widely adopted in complex industrial systems such as the power and chemical industries. Its core goal is to uniquely identify key information such as facility type, function, and spatial location through encoding, providing a unified identification basis for facility operation, maintenance, inspection, and management. Currently, common KKS code generation methods typically match encoding rules based on basic facility attribute information. Some methods incorporate two-dimensional coordinate information for auxiliary positioning, but these methods primarily rely on fixed-resolution grid divisions or simple spatial area annotations as the carrier of spatial information. However, due to their inability to fully utilize the three-dimensional spatial data characteristics of facilities, traditional methods suffer from two shortcomings: first, the grid division cannot adapt to the spatial complexity of different facility components, resulting in inaccurate representation of the encoded spatial location information; second, the association between attribute information and spatial information is relatively simple, and the code only reflects the single-dimensional characteristics of the facility, making it difficult to simultaneously carry the dual information of "what the facility is" and "where the facility is." This lack of information dimensionality can easily lead to code duplication and unclear identification. Summary of the Invention

[0003] The present invention provides a KKS code generation method and system based on the fusion of spatial grid and three-dimensional data.

[0004] In a first aspect, an embodiment of the present invention provides a KKS code generation method based on the fusion of spatial grid and three-dimensional data, the method comprising:

[0005] Obtaining the original three-dimensional spatial data set covering the target facility area and the basic attribute data set of the corresponding facilities;

[0006] Gridding the original three-dimensional spatial data set to generate a multi-level spatial grid unit set that matches the spatial range of the target facility area;

[0007] Associating and 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;

[0008] 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;

[0009] The uniqueness of the KKS code 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 fusion data set corresponding to the codes that failed the verification to regenerate a valid KKS code.

[0010] In a second aspect, an embodiment of the present invention provides a computer system, including:

[0011] a memory storing a computer program;

[0012] A processor is used to load the computer program to implement the KKS code generation method based on the fusion of spatial grid and three-dimensional data as described above.

[0013] 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 location 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; the original three-dimensional spatial data set is grid-divided to generate a multi-level spatial grid unit 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, making the grid division more in line with the actual spatial distribution of the facility; the basic attribute data set is combined with the multi-level spatial grid unit set. The collection is mapped and associated to generate a fused data collection, achieving a deep binding between facility attribute information and spatial location information, enabling the code to simultaneously carry the dual information of "what the facility is" and "where the facility is", enriching the information content of the code; the pre-built KKS coding rule model is called to generate the code for the fused data collection to ensure that the code meets the requirements of standardization rules; the generated KKS code sequence is uniquely verified and the parameters are adjusted for codes that fail the verification to regenerate valid codes, avoiding the problem of repeated coding caused by data feature defects in traditional coding methods, forming a dynamically optimized code generation chain, 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 location and attribute characteristics of the facility and meet the uniqueness requirements. It solves the problems of single information dimension, inaccurate grid division, and high code repetition rate in traditional coding methods, and significantly improves the quality and practicality of KKS codes. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This 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.

[0015] Figure 2 It is a schematic diagram of the composition of a computer system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] See also Figure 1 , Figure 1 A flowchart of a KKS code generation method based on the fusion of spatial grid and three-dimensional data provided in 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. The KKS code generation method based on the fusion of spatial grid and three-dimensional data may include the following steps:

[0018] Step S100: Acquire an original three-dimensional spatial data set covering a target facility area and a basic attribute data set of corresponding facilities.

[0019] The original three-dimensional spatial data set contains continuously collected point cloud information with spatial coordinate markers, and the basic attribute data set contains facility type identification, functional classification description and installation environment feature description.

[0020] The original three-dimensional spatial data set refers to the data set containing point cloud information obtained after spatial information collection of the target facility area. These point cloud information are characterized by continuous collection, and each point is marked with spatial coordinates, which can accurately reflect the three-dimensional spatial structure of the target facility area. Point cloud information is a set composed of a large number of points. Each point represents a specific location in the target facility area. Its spatial coordinate mark usually includes X coordinate, Y coordinate and Z coordinate, which is used to determine the specific position of the point in three-dimensional space. The basic attribute data set is a set of attribute information related to the target facility, in which the facility type identifier is used to clarify the specific type of facility, such as power equipment, 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.

[0021] The process of obtaining the original three-dimensional spatial data set covering the target facility area and the corresponding facility basic attribute data set, for example, includes: using a laser scanning device to collect spatial information of the target facility area, obtaining a raw point cloud data sequence containing X, Y, and Z coordinates. Then, the raw point cloud data sequence is filtered to remove discrete outliers caused by equipment errors, generating a valid point cloud data set with continuous spatial distribution characteristics. Next, a basic attribute record file for the target facility is obtained. This file contains fields for type identification, functional classification, and installation environment characteristics of the facility during the design phase. Field cleaning is then performed on the basic attribute record file to remove duplicate or missing fields, generating a basic attribute data set containing complete attribute information. Finally, the valid point cloud data set and the basic attribute data set are timestamp-aligned to ensure consistency in the data acquisition time dimension, thereby obtaining the original three-dimensional spatial data set and the basic attribute data set.

[0022] As an implementation manner, step S100 may specifically include the following steps S110 to S150:

[0023] Step S110: spatial information of the target facility area is collected by laser scanning equipment to obtain an original point cloud data sequence including X coordinates, Y coordinates, and Z coordinates.

[0024] The original point cloud data sequence is a collection of points collected by a laser scanning device. Each point contains X coordinates, Y coordinates, and Z coordinates, which are used to represent the position of the point in three-dimensional space.

[0025] Specifically, the scanning parameters of the laser scanning device can be set to a preset scanning frequency and scanning angle range so as to cover the entire spatial range of the target facility area. Then, the laser scanning device is controlled 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, the emission time, reception time and reflection intensity information of the laser signal are recorded. Next, the distance parameter between the scanning point and the laser scanning device is calculated based on the time difference between the emission time and the reception time. The X coordinate, Y coordinate and Z coordinate of the scanning point are then calculated based on the scanning angle range and the distance parameter. Finally, the X coordinate, Y coordinate, Z coordinate and reflection intensity information are associated and stored to generate a raw point cloud data sequence containing spatial coordinates and reflection intensity.

[0026] For example, when scanning the interior of a building, a laser scanner is placed in a suitable location, with a scanning frequency of 1000 scans per second and a scanning angle range of 360 degrees horizontally and 180 degrees vertically to ensure coverage of the entire building interior. Starting at a corner of the building, the system scans point by point along a pre-set spiral scanning path. During each scan, a high-precision clock records the laser signal's transmission and reception times, along with the reflection intensity information. Based on the time difference between the transmission and reception times, the speed of light formula is used to calculate the distance between the scanning point and the laser scanner. Based on the scanning angle range and distance parameters, trigonometric functions are used to calculate the X, Y, and Z coordinates of the scanning point. For example, horizontally, the X coordinate is calculated as d * cos(θ) and the Y coordinate is calculated as d * sin(θ), where θ is the horizontal angle of the scanning point relative to the laser scanner. Vertically, the Z coordinate is calculated based on the vertical scanning angle and distance parameters. Finally, the calculated X, Y, and Z coordinates, along with the reflection intensity information, are stored in a database to generate a raw point cloud data sequence.

[0027] As an embodiment, step S110 collects spatial information of the target facility area using a laser scanning device to obtain a sequence of original point cloud data including X coordinates, Y coordinates, and Z coordinates. Specifically, the following steps S111 to S116 may be included:

[0028] Step S111: 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.

[0029] Scanning parameters refer to the parameters used by the laser scanning device during scanning, including the scanning frequency and scanning angle range. The scanning frequency refers to the number of times the laser scanning device emits the laser beam per second, which determines the scanning speed and data density. 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 scanning coverage. The preset scanning frequency and scanning angle range are pre-set based on the size, shape, and complexity of the target facility area to ensure that the entire spatial range of the target facility area can be fully covered. The appropriate scanning frequency and scanning angle range can be determined based on the actual situation of the target facility area. Then, the scanning parameters are set to the preset values ​​through the laser scanning device's operating interface or programming interface.

[0030] 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 according to the preset scanning path.

[0031] The initial scanning point is the starting point for the laser scanner to begin scanning. It is predetermined based on the characteristics of the target facility area and the scanning requirements. The preset scanning path is the path that the laser scanner follows during the scanning process. It can be a straight line, spiral, grid line, or other different shapes to ensure a comprehensive and uniform scanning of the target facility area.

[0032] For example, the laser scanner is moved to the initial scanning point and calibrated and positioned to ensure accurate positioning and orientation. The laser scanner is then controlled to scan point by point in a sequence and at a specific speed, following a pre-defined scanning path. During the scanning process, a motor can be used to drive the laser scanner's rotation and movement to scan in different directions.

[0033] Step S113: Record the emission time, reception time and reflection intensity information of the laser signal during each scan.

[0034] The emission time of the laser signal is the moment when the laser scanning device emits the laser beam. The reception time refers to the moment when the laser beam is reflected back from the target object and received by the laser scanning device. The reflection intensity information refers to 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.

[0035] For example, in the hardware circuit of a laser scanning device, a high-precision clock chip is used to record the emission and reception times 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.

[0036] Step S114: 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.

[0037] The time difference between the emission time and the reception time reflects the time it takes for the laser beam to be emitted and received. The distance parameter between the scanning point and the laser scanning device can be calculated based on the speed of light formula.

[0038] Step S115: Calculate the X coordinate, Y coordinate, and Z coordinate of the scanning point according to the scanning angle range and distance parameters.

[0039] The scanning angle range includes the horizontal scanning angle and the vertical scanning angle. Using trigonometric functions, the X, Y, and Z coordinates of the scanning 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 repeated here.

[0040] Step S116: The X coordinate, Y coordinate, Z coordinate and reflection intensity information are stored in association with each other to generate an original point cloud data sequence including spatial coordinates and reflection intensity.

[0041] Associative storage refers to storing the X-coordinate, Y-coordinate, Z-coordinate, and reflection intensity information according to a set format and rules, so that there is a corresponding relationship between them. The original point cloud data sequence is a collection of these stored data.

[0042] Specifically, a data table can be created in the database, containing fields such as X coordinate, Y coordinate, Z coordinate and reflection intensity information, and the data obtained from each scan can be inserted into the data table. In the file system, text files or binary files can be used to store data, and the X coordinate, Y coordinate, Z coordinate and reflection intensity information can be written into the file in sequence according to the set format. For example, when using a database to store data, a data table named "point_cloud" is created, containing four fields: "x_coordinate", "y_coordinate", "z_coordinate" and "reflection_intensity". After each scan obtains data, 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", with each line representing the data of a scanning point. In this way, a sequence of original point cloud data containing spatial coordinates and reflection intensity can be generated.

[0043] Step S120: performing noise filtering on the original point cloud data sequence to remove discrete abnormal points caused by equipment errors, and generating a valid point cloud data set with continuous spatial distribution characteristics.

[0044] Discrete outliers refer to points that are far away from the surrounding points due to errors in the laser scanning equipment, 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 noise points.

[0045] For example, a statistical filtering algorithm can be used for noise filtering. First, the statistical characteristics of the neighborhood points of each point in the original point cloud data sequence are calculated, such as the average distance and standard deviation of the neighborhood points. Then, a threshold is set based on the statistical characteristics, and points whose distance from the neighborhood points exceeds the threshold are removed as discrete outliers.

[0046] Step S130: Obtain a basic attribute record file of the target facility. The basic attribute record file includes a type identification field, a function classification field, and an installation environment feature field of the facility during the design phase.

[0047] 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 clarify 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.

[0048] 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.

[0049] Step S140: Clean the fields of the basic attribute record file to remove duplicate or missing field contents, and generate a basic attribute data set containing complete attribute information.

[0050] 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.

[0051] Specifically, each field in the basic attribute record file can be traversed to check whether there are duplicate field contents. If there are duplicate field contents, only one of them is retained. Then, each field is checked for missing content. If there is missing content, the field is supplemented or deleted according to the actual situation.

[0052] Step S150: aligning the valid point cloud data set and the basic attribute data set with their timestamps to ensure consistency between the two in the data acquisition time dimension, thereby obtaining the original three-dimensional spatial data set and the basic attribute data set.

[0053] Timestamp alignment involves matching and aligning the data in the valid point cloud dataset and the basic attribute dataset based on their acquisition time, ensuring consistency between the two. For example, the acquisition time of each data point is recorded in both the valid point cloud dataset and the basic attribute dataset. By comparing the acquisition times, data points with similar acquisition times are associated.

[0054] For example, in a valid point cloud dataset, each point cloud data point has an acquisition timestamp, and in a basic attribute dataset, each attribute data point also has an acquisition timestamp. By writing a program, we can traverse the valid point cloud dataset and the basic attribute dataset and associate data points whose acquisition timestamps differ within a set range (e.g., 1 second). This allows us to align the timestamps of the valid point cloud dataset and the basic attribute dataset, resulting in the original 3D spatial dataset and the basic attribute dataset.

[0055] Step S200: gridding the original three-dimensional spatial data set to generate a multi-level spatial grid unit set that matches the spatial range of the target facility area. The multi-level spatial grid unit set includes grid structures with different spatial resolutions.

[0056] Grid division is the process of dividing the three-dimensional space represented by the original three-dimensional spatial data set into several small grid units. The multi-level spatial grid unit set is a set of grid units of different levels and different spatial resolutions. Spatial resolution refers to the size of the grid unit. Different spatial resolutions can better adapt to the different structures and details of the target facility area.

[0057] Exemplarily, the maximum and minimum values ​​of the X-coordinates, Y-coordinates, and Z-coordinates of all point clouds in the original three-dimensional spatial data set are extracted to determine the spatial boundary range of the target facility area. Then, the initial grid resolution is selected according to the spatial boundary range, and the target facility area is divided into three dimensions based on the initial grid resolution to generate a basic spatial grid unit set. Next, the resolution of the grid cells containing the key components of the facility in the basic spatial grid unit set is increased, and the grid size is reduced to a preset ratio of the initial grid resolution to generate a first-level grid set containing fine grid cells. At the same time, the resolution of the grid cells containing only the facility support structures in the basic spatial grid unit set is reduced, and the grid size is expanded to a preset multiple of the initial grid resolution to generate a second-level grid set containing coarse grid cells. Finally, the basic spatial grid unit set, the first-level grid set, and the second-level grid set are hierarchically associated to generate a multi-level spatial grid unit set with different spatial resolutions.

[0058] As an implementation manner, step S200 may specifically include the following steps S210 to S250:

[0059] Step S210: extracting the maximum and minimum values ​​of the X coordinates, Y coordinates, and Z coordinates of all point clouds in the original three-dimensional spatial data set to determine the spatial boundary range of the target facility area.

[0060] The spatial boundary range of the target facility area is the minimum three-dimensional spatial range that can completely contain the target facility area. The range can be determined by extracting the maximum and minimum values ​​of the X coordinates, Y coordinates, and Z coordinates of all point clouds in the original three-dimensional spatial data set.

[0061] For example, all point cloud data in the original 3D spatial data set is traversed, and the X, Y, and Z coordinates of each point cloud are recorded. During the traversal process, the coordinate values ​​are compared 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 of the target facility area.

[0062] Step S220: Select an initial grid resolution according to the spatial boundary range, divide the target facility area into three dimensions based on the initial grid resolution, and generate a basic spatial grid unit set.

[0063] The initial grid resolution is the size of the grid unit first selected when performing grid division. The basic space grid unit set is a set of grid units divided according to the initial grid resolution.

[0064] For example, the length of the spatial boundary range in the X-axis direction, the width in the Y-axis direction, and the height in the Z-axis direction are calculated. Then, the initial grid resolution is selected based on the length, width, and height, so that the size of each basic spatial grid unit in the X-axis, Y-axis, and Z-axis directions is the initial grid resolution. Next, starting from the minimum X coordinate, the minimum Y coordinate, and the minimum Z coordinate of the spatial boundary range, the grid lines in the X-axis, Y-axis, and Z-axis directions are divided in sequence according to the initial grid resolution. Finally, the grid vertices in the three-dimensional space are formed by the intersection of the grid lines in the X-axis, Y-axis, and Z-axis directions, and the basic spatial grid units with a cubic structure are surrounded by adjacent grid vertices. All basic spatial grid units are stored in a collection to generate a basic spatial grid unit collection.

[0065] As an implementation manner, step S220 may specifically include the following steps S221 to S224:

[0066] Step S221: Calculate the length of the space boundary range in the X-axis direction, the width in the Y-axis direction, and the height in the Z-axis direction.

[0067] The length of the spatial boundary range in the X-axis direction refers to the difference between the maximum and minimum coordinate values ​​of the target facility area in the X-axis direction. The width in the Y-axis direction refers to the difference between the maximum and minimum coordinate values ​​of the target facility area in the Y-axis direction. The height in the Z-axis direction refers to the difference between the maximum and minimum coordinate values ​​of the target facility area in the Z-axis direction.

[0068] Exemplarily, the maximum and minimum values ​​of the X coordinate, Y coordinate, and Z coordinate of the spatial boundary range are obtained, and the differences therebetween are calculated respectively.

[0069] Step S222: Selecting an initial grid resolution based on the length, width, and height so that the dimensions of each basic spatial grid unit in the X-axis, Y-axis, and Z-axis directions are all the same as the initial grid resolution.

[0070] For example, an appropriate initial grid resolution can be selected based on 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 requires more details, a smaller initial grid resolution can be selected.

[0071] Step S223: Starting from the minimum X coordinate, the minimum Y coordinate, and the minimum Z coordinate of the spatial boundary range, grid lines are divided in the X-axis, Y-axis, and Z-axis directions in sequence according to the initial grid resolution.

[0072] Grid lines are virtual lines used to divide three-dimensional space. Grid lines are drawn in the X, Y, and Z axes according to the initial grid resolution, dividing the target facility area into several small grid cells. For example, starting from the minimum X coordinate of the spatial boundary range, a grid line is drawn in the X-axis direction at intervals equal to the initial grid resolution. Starting from the minimum Y coordinate of the spatial boundary range, a grid line is drawn in the Y-axis direction at intervals equal to the initial grid resolution. Starting from the minimum Z coordinate of the spatial boundary range, a grid line is drawn in the Z-axis direction at intervals equal to the initial grid resolution.

[0073] Step S224: The intersections of the grid lines in the X-axis, Y-axis, and Z-axis directions form grid vertices in the three-dimensional space, and the adjacent grid vertices form basic spatial grid units with a cubic structure. All basic spatial grid units are stored in a collection to generate a basic spatial grid unit collection.

[0074] A grid vertex is the intersection of grid lines in the X-axis, Y-axis, and Z-axis directions. A basic space grid unit is a small space with a cubic structure surrounded by adjacent grid vertices. A basic space grid unit set is a set consisting of all these basic space grid units.

[0075] For example, the grid vertices in three-dimensional space can be obtained by calculating the coordinates of the intersection points of the grid lines. For each grid vertex, the adjacent grid vertices are found. These adjacent grid vertices form a basic spatial grid unit with a cubic structure. All basic spatial grid units are stored in a data structure, such as an array or a linked list, to generate a basic spatial grid unit set.

[0076] Step S230: The resolution of the grid cells containing the key components of the facility in the basic spatial grid cell set is increased, and the grid size is reduced to a preset ratio of the initial grid resolution to generate a first-level grid set containing fine grid cells.

[0077] Key components of a facility are those that play a key role in the function and performance of the target facility. Resolution enhancement is to reduce the grid size of the grid cells containing 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 enhancement. The first-level grid set is a set composed of these fine grid cells.

[0078] For example, the structural complexity characteristics of the facility components within each grid cell in the basic spatial grid cell set are extracted. The structural complexity characteristics are determined based on the density of concave and convex features on the component surface, the number of connection interfaces, and the depth of the internal cavity. Then, a mapping relationship table is established between structural complexity and resolution adjustment ratio. In the mapping relationship table, the higher the structural complexity, the smaller the corresponding resolution reduction ratio. Next, the mapping relationship table is queried based on the structural complexity characteristics to obtain the corresponding adjustment ratio, and the resolution of the grid cells containing the key components of the facility is improved to generate a first-level grid set containing fine grid cells.

[0079] As an implementation manner, step S230 may specifically include the following steps S231 to S237:

[0080] Step S231: extracting the structural complexity characteristics of the facility components in each grid unit in the basic spatial grid unit set. The structural complexity characteristics are determined according to the density of concave-convex features on the component surface, the number of connection interfaces, and the depth of the internal cavity.

[0081] The structural complexity feature is a feature used to describe the structural complexity of facility components. The density of concave-convex features on the component surface refers to the ratio of the number of concave-convex features on the component surface to the surface area of ​​the component. The number of connection interfaces refers to the number of interfaces connecting the component with other components. The internal cavity depth refers to the depth of the internal cavity of the component.

[0082] Exemplarily, for each grid cell in the basic spatial grid cell set, the surface concave-convex feature density, the number of connection interfaces, and the internal cavity depth of the facility components within the grid cell are calculated by three-dimensional modeling or point cloud data analysis methods. These features are then combined to form structural complexity characteristics. For example, a mechanical part within a grid cell is scanned using a three-dimensional laser scanner to obtain its point cloud data. The number of concave-convex features and the surface area on the part surface are calculated using a point cloud data analysis algorithm to obtain the surface concave-convex feature density. At the same time, the number of connection interfaces and the internal cavity depth of the part are determined by analyzing the point cloud data or consulting the design drawings of the part. These features are combined to form the structural complexity characteristics of the part.

[0083] Step S232: establishing a mapping relationship table between structural complexity and resolution adjustment ratio. In the mapping relationship table, the higher the structural complexity, the smaller the corresponding resolution reduction ratio.

[0084] The mapping relationship table is a table for storing 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 unit.

[0085] For example, based on experience or experimental data, the resolution adjustment ratios corresponding to different structural complexities are determined. For example, structural complexity is divided 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; and for low structural complexity, the corresponding resolution adjustment ratio is 0.5. These corresponding relationships are stored in a table to form a mapping relationship table.

[0086] Step S233: query the mapping relationship table according to the structural complexity characteristics to obtain the corresponding adjustment ratio, improve the resolution of the grid cells containing the key components of the facility, and generate a first-level grid set containing fine grid cells.

[0087] Querying the mapping relationship table refers to searching for the corresponding resolution adjustment ratio in the mapping relationship table according to the structural complexity characteristics, and improving the resolution refers to reducing the grid size of the grid cells containing the key components of the facility.

[0088] For example, for a grid cell in the basic spatial grid cell set containing a key facility component, the corresponding adjustment ratio is searched in the mapping table based on its structural complexity characteristics. The grid size of this grid cell is reduced to the initial grid resolution multiplied by the adjustment ratio. The same process is repeated for all grid cells containing key facility components to generate a first-level grid set containing fine grid cells.

[0089] Step S234: reducing the resolution of the grid cells that only contain the facility support structure in the basic spatial grid cell set, expanding the grid size to a preset multiple of the initial grid resolution, and generating a second-level grid set containing coarse grid cells.

[0090] The facility support structure refers to the structure used to support the target facility, such as steel beams and columns. Resolution reduction refers to increasing the grid size of the grid cells that only contain the facility support structure. The coarse grid cell refers to the grid cell obtained after resolution reduction. The second-level grid set is a set composed of these coarse grid cells.

[0091] For example, the functional redundancy characteristics of the facility support structures within each grid cell in the basic spatial grid cell set are extracted. These functional redundancy characteristics are determined based on the uniqueness of the support structure's load-bearing direction and the uniformity of its materials. A mapping table is then established between functional redundancy and resolution adjustment factors. In this mapping table, higher functional redundancy corresponds to a greater resolution magnification factor. Next, the mapping table is queried based on the functional redundancy characteristics to obtain the corresponding adjustment factor. The resolution of the grid cells containing only the facility support structures is reduced to generate a second-level grid set containing coarse grid cells.

[0092] Step S235: extracting the functional redundancy characteristics of the facility support structure in each grid unit in the basic spatial grid unit set, wherein the functional redundancy characteristics are determined based on the uniqueness of the load-bearing direction and the uniformity of the material of the support structure.

[0093] Functional redundancy characteristics are used to describe the degree of functional redundancy of the facility support structure. Load-bearing direction uniqueness refers to whether the direction of the force borne by the support structure during the load-bearing process is unique. Material uniformity refers to whether the material of the support structure is evenly distributed throughout the structure.

[0094] For example, for each grid cell in the basic spatial grid cell set, structural mechanics analysis or material testing methods are used to determine the uniqueness of the load-bearing direction and material uniformity of the facility support structure within the grid cell. These features are then combined to form a functional redundancy feature.

[0095] Step S236: establishing a mapping relationship table between functional redundancy and resolution adjustment multiples. In the mapping relationship table, a higher functional redundancy corresponds to a greater resolution magnification multiple.

[0096] The mapping relationship table is a table for storing 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 unit is enlarged.

[0097] For example, the resolution adjustment factors corresponding to different functional redundancies can be determined based on experience or experimental data. For example, functional redundancy can be divided into three levels: high, medium, and low. For high functional redundancy, the corresponding resolution adjustment factor is 5; for medium functional redundancy, the corresponding resolution adjustment factor is 3; and for low functional redundancy, the corresponding resolution adjustment factor is 2. These correspondences are stored in a table to form a mapping relationship table.

[0098] Step S237: query the mapping relationship table according to the functional redundancy feature to obtain the corresponding adjustment multiple, reduce the resolution of the grid cells containing only the facility support structure, and generate a second-level grid set containing coarse grid cells.

[0099] Querying the mapping relationship table refers to searching for the corresponding resolution adjustment factor in the mapping relationship table according to the functional redundancy characteristics, and reducing the resolution refers to enlarging the grid size of the grid cells that only contain the facility support structure.

[0100] For example, for a grid cell in the basic spatial grid cell set that contains only facility support structures, the corresponding adjustment factor can be searched in the mapping relationship table based on its functional redundancy characteristics. The grid size of this grid cell is expanded to the initial grid resolution multiplied by the adjustment factor. The same process is repeated for all grid cells that contain only facility support structures to generate a second-level grid set containing coarse grid cells.

[0101] Step S240: reducing the resolution of the grid cells that only contain the facility support structure in the basic spatial grid cell set, expanding the grid size to a preset multiple of the initial grid resolution, and generating a second-level grid set containing coarse grid cells.

[0102] The specific execution process of this step is similar to steps S234-S237 and will not be repeated here.

[0103] Step S250: hierarchically associating the basic spatial grid unit set, the first-level grid set, and the second-level grid set to generate a multi-level spatial grid unit set with different spatial resolutions.

[0104] Hierarchical association refers to associating the basic spatial grid unit set, the first-level grid set, and the second-level grid set according to preset rules, so that a multi-level structure with different spatial resolutions is formed between them. Exemplarily, a data structure can be used to store these grid unit sets and establish an association relationship between them. For example, a tree data structure can be used, with the basic spatial grid unit set as the root node, the first-level grid set and the second-level grid set as child nodes, and an association relationship is established through pointers or indexes between the nodes.

[0105] 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, which can be used to quickly find and access the corresponding grid cell. 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.

[0106] Step S300: Associatively mapping the basic attribute data set with the multi-level spatial grid unit set to generate a fused data set with a binding relationship between spatial position and attribute information.

[0107] Association mapping refers to the corresponding association between the attribute information in the basic attribute data set and the grid cells in the multi-level spatial grid cell set, and the fused data set refers to the data set that binds the spatial location information and attribute information together.

[0108] Exemplarily, the type identification field of each facility in the basic attribute data set is extracted, and the facility's core functional area in three-dimensional space is determined based on the type identification field. Then, a target grid cell that coincides with the core functional area is identified within the multi-level spatial grid cell set. Next, the function classification description field of each facility in the basic attribute data set is extracted, and the facility's functional association range is determined based on the function classification description field. Associated grid cells that overlap with the functional association range are identified within the multi-level spatial grid cell set. Finally, the installation environment characteristic field of each facility in the basic attribute data set is extracted, and the facility's environmental impact area is determined based on the installation environment characteristic field. Environmental grid cells that overlap with the environmental impact area are identified within the multi-level spatial grid cell set. Finally, the spatial coordinate information of the target grid cell, associated grid cells, and environmental grid cells is bound to the type identification field, function classification description field, and installation environment characteristic field of the corresponding facility, generating a fused data set with a binding relationship between spatial location and attribute information.

[0109] For example, within the power facilities area of ​​a factory, the basic attribute data set contains attribute information for a transformer, including the type identification field "transformer," the functional classification description field "voltage conversion," and the installation environment characteristic field "indoor dry environment." Based on the type identification field, the core functional area of ​​the transformer in three-dimensional space is determined to be the spatial extent of the transformer itself. Within the multi-level spatial grid cell set, a target grid cell is identified that coincides with the core functional area. Based on the functional classification description field, the functional association scope of the transformer is determined to be the spatial extent of the cables, switches, and other equipment connected to the transformer. Within the multi-level spatial grid cell set, associated grid cells that overlap with the functional association scope are identified. Based on the installation environment characteristic field, the environmental impact area of ​​the transformer is determined to be the space within a certain range around the transformer. Within the multi-level spatial grid cell set, environmental grid cells that overlap with the environmental impact area are identified. Finally, the spatial coordinate information of the target grid cell, associated grid cells, and environmental grid cells is bound to the transformer's type identification field, functional classification description field, and installation environment characteristic field, generating a fused data set with spatial location and attribute information bounded.

[0110] As an implementation manner, step S300 may specifically include the following steps S310 to S370:

[0111] 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.

[0112] The type identification field is used to clarify the specific type of the facility, and the core functional area refers to the area where the facility realizes its core functions in three-dimensional space.

[0113] For example, each facility in the basic attribute data set is traversed to extract its type identification field, and the core functional area of ​​the facility in the three-dimensional space is determined based on the type identification field and a pre-established mapping relationship between the type and the core functional area.

[0114] Step S320: determining a target grid cell that coincides with the spatial position of the core functional area in the multi-level spatial grid cell set.

[0115] The target grid cell refers to the grid cell in the multi-level spatial grid cell set that coincides with the spatial location of the core functional area of ​​the facility.

[0116] Exemplarily, the spatial extent description information of the core functional area is extracted, and the spatial extent 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, each grid cell in the multi-level spatial grid cell set is traversed to 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. Next, a determination is made as to 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; 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; and 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 of the above determinations are true, the current grid cell is marked as the target grid cell that coincides with the spatial position of the core functional area.

[0117] As an implementation manner, step S320 may specifically include the following steps S321 to S326:

[0118] Step S321: extracting 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.

[0119] The spatial extent description information of the core functional area is used to clarify the specific location and size of the core functional area in three-dimensional space. These coordinate information can be obtained from the design documents, three-dimensional models or actual measurement data of the facility. By extracting this information, it is convenient to compare it with the spatial position of the grid unit later.

[0120] 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.

[0121] Exemplarily, a loop structure is used to traverse each grid cell in the multi-level spatial grid cell set. For each grid cell, the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate are extracted from its data structure.

[0122] For example, in a multi-level spatial grid cell collection, a for loop is used to iterate over each grid cell in the collection. For each grid cell, assume its data structure is an object containing properties such as minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate. This coordinate information can be extracted by accessing the object's properties.

[0123] Step S323: Determine whether the minimum X coordinate of the current grid unit 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.

[0124] This judgment is to determine whether the current grid unit is completely contained in the core functional area in the X-axis direction.

[0125] For example, the minimum X coordinate of the current grid cell can be compared with the minimum X coordinate of the core functional area, and the maximum X coordinate of the current grid cell can be compared 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 the condition is true; otherwise, it is false.

[0126] Step S324: Determine whether the minimum Y coordinate of the current grid unit 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.

[0127] This judgment is to determine whether the current grid unit is completely contained in the core functional area in the Y-axis direction.

[0128] For example, the minimum Y coordinate of the current grid cell is compared with the minimum Y coordinate of the core functional area, and the maximum Y coordinate of the current grid cell is compared 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 the condition is true; otherwise, it is false.

[0129] Step S325: Determine whether the minimum Z coordinate of the current grid unit 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.

[0130] This judgment is to determine whether the current grid cell is completely within 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 the judgment condition is met; otherwise, it is not met.

[0131] Step S326: If the above judgments are all true, the current grid unit is marked as a target grid unit that coincides with the spatial position of the core functional area.

[0132] 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 a 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, a Boolean field named "is_target" is added to the data object of the grid cell. If the grid cell is determined to be a target grid cell, the value of the "is_target" field is set to "true"; if it is not a target grid cell, it is set to "false". Through such marking, the target grid cell can be easily filtered out from the multi-level spatial grid cell set later.

[0133] Step S330: extract the function classification description field of each facility in the basic attribute data set, and determine the function association range of the facility according to the function classification description field.

[0134] The functional classification description field details the specific functions of the facility, while the functional association scope refers to the surrounding spatial range related to the facility's functions. For example, the functional keywords in the functional classification description field can be parsed first. Functional keywords include input interfaces, output interfaces, and intermediate processing links. Then, based on the functional keywords, spatial extension rules for the functional association scope are constructed. Spatial extension rules include the extension length of the interface connection direction and the coverage radius of the processing link. Finally, based on the spatial extension rules, starting from the boundary of the core functional area of ​​the facility and expanding outward, a functional association scope is generated, including the interface connection path and the coverage area of ​​the processing link.

[0135] For example, for a sewage treatment facility, its functional classification description field is "receive sewage input, and output purified water after filtration, sedimentation, disinfection and other treatment links." The functional keywords parsed from this description are "sewage input interface," "filtration treatment link," "sedimentation treatment link," "disinfection treatment link," and "purified water output interface." Based on these keywords, spatial expansion rules are constructed. For example, it is stipulated that the connecting pipe of the sewage input interface extends for 5 meters, the coverage radius of each treatment link is 3 meters, and the connecting pipe of the purified water output interface extends for 4 meters. Starting from the boundary of the core functional area of ​​the sewage treatment facility, expanding outward according to these rules, the range of 5 meters extending outward from the sewage input pipe, the circular area of ​​each treatment link with a radius of 3 meters with its center point as the center point, and the range of 4 meters extending outward from the purified water output pipe together constitute the functional association range of the sewage treatment facility.

[0136] As an implementation manner, step S330 may specifically include the following steps S331 to S336:

[0137] Step S331: parse the function keywords in the function classification description field, where the function keywords include input interface, output interface and intermediate processing links.

[0138] Parsing the functional classification description field involves analyzing and processing the text content within that field to extract key terms that represent the facility's functions. The input interface in the functional keywords refers to the facility's entry point for receiving external materials or energy, the output interface refers to the facility's exit point for outputting processing results, and the intermediate processing steps are the various processing operations performed by the facility from input to output.

[0139] Natural language processing techniques can be used to parse functional keywords within functional classification description fields. First, the text within the functional classification description field is segmented into individual words. Then, the keywords are filtered out by matching them against a predefined dictionary of functional keywords. For example, for the functional classification description field "Raw materials are received through the feed port, and the finished product is output from the discharge port after heating and stirring," the word segmentation algorithm breaks it down into words such as "through," "feed port," "receive," "raw materials," "pass," "heat," "stir," "process," "after," "from," "discharge port," "output," and "finished product." This is then matched against the dictionary of functional keywords to filter out functional keywords such as "feed port" (input interface), "heating" (intermediate processing step), "stirring" (intermediate processing step), and "discharge port" (output interface).

[0140] Step S332: constructing a spatial extension rule of the function association range based on the function keyword, the spatial extension rule includes the extension length of the interface connection direction and the coverage radius of the processing link.

[0141] Spatial extension rules determine the specific size and shape of the functional scope. Based on different functional keywords, they set extension lengths for interface connections and coverage radiuses for intermediate processing steps.

[0142] For example, each functional keyword can be categorized first, distinguishing between input interfaces, output interfaces, and intermediate processing links. Then, based on factors such as the type and scale of the facility, and industry experience, corresponding spatial expansion parameters can be set for different types of functional keywords. For example, for industrial production equipment, the extension length of the connecting pipes between the input and output interfaces may be determined based on factors such as the pipe material and flow rate, generally ranging from several meters to tens of meters. The coverage radius of the intermediate processing link is set based on the size of the processing equipment and the requirements of the processing process, and may be around several meters.

[0143] 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 processing link coverage area.

[0144] Starting from the boundary of the core functional area of ​​the facility, expand outward according to the interface connection direction extension length and processing link coverage radius set in the spatial expansion rules to determine the functional association range.

[0145] For example, for input and output interfaces, an interface connection path is formed by extending the corresponding length along the interface connection direction, starting from the corresponding interface location on the boundary of the core functional area. For intermediate processing links, a circle with a set coverage radius, centered around the processing link location within the core functional area, is used to form the processing link's coverage area. These interface connection paths and processing link coverage areas together constitute the functional association scope. For example, for a power generation device, the core functional area is the spatial area within the generator body. The input interface is the fuel input pipeline. Starting from the fuel input port on the boundary of the core functional area, it extends outward 20 meters according to the spatial expansion rule to form the fuel input interface connection path. The intermediate power generation processing link, centered around the generator, covers a circular area with a radius of 15 meters, forming 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, it extends outward 18 meters to form the power output interface connection path. These paths and areas together constitute the functional association scope of the power generation device.

[0146] Step S334: Determine, in the multi-level spatial grid unit set, associated grid units that overlap with the spatial position of the function association range.

[0147] Associated grid cells refer to those grid cells within a multi-level spatial grid cell set that spatially overlap with the facility's functional association range. For example, to determine associated grid cells, the spatial intersection area between the functional association range and the spatial range of each grid cell is first calculated. The spatial intersection area is the area of ​​overlap between the two on a horizontal projection plane. Grid cells whose spatial intersection area exceeds a preset area threshold are then determined to overlap with the functional association range.

[0148] Step S335: Calculate the spatial intersection area between the functional association range and the spatial range of each grid unit, where the spatial intersection area is the overlapping area of ​​the two on the horizontal projection plane.

[0149] The area of ​​spatial intersection can be calculated using geometric calculations. First, project the functional association range and the spatial range of the grid cell onto a horizontal plane to obtain their projections. For regular projections, such as rectangles and circles, the intersection area can be calculated using corresponding geometric formulas. For example, if the projection of the functional association range onto the horizontal plane is a rectangle with length a and width b, and the projection of the grid cell onto the horizontal plane is also a rectangle with length c and width d, and the two rectangles partially overlap, the intersection area can be calculated by determining the length and width of the overlapping portion and then using the rectangular area formula. For irregular projections, numerical calculation methods can be used, such as discretizing the projection into small pixels, counting the number of overlapping pixels, and then calculating the intersection area based on the pixel areas. For example, if the projection of the functional association range onto the horizontal plane is a circle with radius r, and the projection of the grid cell onto the horizontal plane is a square with side length s, if the circle and square overlap, the geometric shape of the overlapping portion can be determined mathematically, and then the intersection area can be calculated using integration or piecewise calculation.

[0150] Step S336: Determine the grid cells whose spatial intersection area is greater than a preset area threshold as associated grid cells that overlap with the spatial position of the function association range.

[0151] The preset area threshold is a pre-set area standard used to determine whether a grid cell's overlap with the functional association range is sufficient to determine whether the grid cell is an associated grid cell. When the calculated spatial intersection area of ​​a grid cell and the functional association range is greater than the preset area threshold, the grid cell is determined to be an associated grid cell.

[0152] Step S340: determining, in the multi-level spatial grid unit set, associated grid units that overlap with the spatial position of the function association range.

[0153] The specific execution process of this step is consistent with steps S334-S336. First, the spatial intersection area between the functional association range and the spatial range of each grid cell is calculated. Then, grid cells whose spatial intersection area exceeds a preset area threshold are identified as associated grid cells. This step accurately selects grid cells related to the facility functional association range from the multi-level spatial grid cell set, providing a foundation for the subsequent generation of a fused data set.

[0154] Step S350: extracting the installation environment characteristic field of each facility in the basic attribute data set, and determining the environmental impact area of ​​the facility according to the installation environment characteristic field.

[0155] The installation environment characteristic field describes the specific environmental conditions of the facility installation, such as temperature, humidity, ventilation conditions, and surrounding equipment. The environmental impact zone refers to the surrounding spatial range that may be affected by the facility's installation environment factors. For example, a detailed analysis of the installation environment characteristic field is performed to identify key factors related to environmental impacts, such as temperature, radiation, and vibration. The environmental impact zone is then determined based on these key factors and a pre-established environmental impact model. The environmental impact model can be based on physical principles, empirical data, or experimental results, and is used to describe the scope and extent of the facility's impact on the surrounding environment under different installation environments.

[0156] Step S360: Determine, in the multi-level spatial grid unit set, an environmental grid unit that overlaps with the spatial position of the environmental impact area.

[0157] Environmental grid cells are those in a multi-level spatial grid cell set that overlap with a facility's environmental impact area. The process for identifying environmental grid cells is similar to that for identifying associated grid cells. Specifically, the spatial intersection area between the environmental impact area and the spatial extent of each grid cell is calculated. Grid cells with an intersection area greater than a preset area threshold are identified as environmental grid cells.

[0158] Step S370: Bind the spatial coordinate information of the target grid unit, associated grid unit and environmental grid unit to the type identification field, function classification description field and installation environment characteristic field of the corresponding facility one by one to generate a fused data set with a binding relationship between spatial position and attribute information.

[0159] The binding operation is to associate the spatial coordinate information (X coordinate, Y coordinate, Z coordinate) of the target grid cell, associated grid cell and environmental grid cell with the type identification field, functional classification description field and installation environment characteristic field of the corresponding facility to form a new data set. Each data element in this set contains spatial location information and attribute information.

[0160] For example, for a facility, the spatial coordinates of its target grid cell are (X1, Y1, Z1), the spatial coordinates of its associated grid cells are (X2, Y2, Z2), (X3, Y3, Z3), and so on, and the spatial coordinates of its environmental grid cells are (X4, Y4, Z4), and so on. This spatial coordinate information is bound to the facility's type identification field ("motor"), the functional classification description field ("converting electrical energy into mechanical energy"), and the installation environment characteristic field ("in a well-ventilated machine room"). This can be accomplished by creating a new data structure, such as a table containing multiple fields or an array of objects, to store spatial coordinate information and attribute information in separate fields. 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," and "installation environment characteristics." The corresponding information is then filled into the corresponding fields, generating a fused data set with bound spatial location and attribute information.

[0161] Step S400: Call the 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.

[0162] The KKS encoding rule model is a pre-built model for converting the spatial location and attribute information of facilities into specific codes. The KKS encoding sequence is a series of codes generated by encoding the fused dataset according to this model. The process of using the pre-built KKS encoding rule model to encode the fused dataset is as follows: First, the spatial encoding rules in the KKS encoding rule model are parsed to extract the spatial coordinate information of the target grid cell in the fused dataset. Based on this spatial coordinate information, a first encoding segment is generated that reflects the spatial location of the facility. Next, the attribute encoding rules in the KKS encoding rule model are parsed to extract the type identification field of the facility in the fused dataset. Based on this field, a second encoding segment is generated that reflects the facility type. Next, the function encoding rules in the KKS encoding rule model are parsed to extract the function classification description field of the facility in the fused dataset. Based on this field, a third encoding segment is generated that reflects the facility function. Finally, the environment encoding rules in the KKS encoding rule model are parsed to extract the installation environment feature field of the facility in the fused dataset. Based on this field, a fourth encoding segment is generated that reflects the facility environment. Finally, the first coding segment, the second coding segment, the third coding segment and the fourth coding segment are spliced ​​in a preset order to generate a KKS coding sequence corresponding to the facility spatial location and attribute information.

[0163] For example, for a power transformer facility, its fused data set includes the spatial coordinate information of the target grid cell, the type identifier field "transformer," the functional classification description field "voltage conversion," and the installation environment characteristic field "outdoor substation." The pre-built KKS encoding rule model stipulates that the spatial encoding rule converts the spatial coordinate information into a string of numeric codes according to a pre-set algorithm; the attribute encoding rule converts the facility type identifier into a specific alphabetic code; the function encoding rule converts the key functions in the functional classification description into another string of alphabetic codes; and the environment encoding rule converts the installation environment characteristics into a specific symbolic code. Based on these rules, the first encoding segment (e.g., "1234") is generated based on the spatial coordinates of the target grid cell; the second encoding segment (e.g., "TR") is generated based on the type identifier field "transformer"; the third encoding segment (e.g., "VT") is generated based on the functional classification description field "voltage conversion"; and the fourth encoding segment (e.g., "OS") is generated based on the installation environment characteristic field "outdoor substation." Then, according to the preset order, such as the space coding segment first, the type coding segment second, the function coding segment after the type coding segment, and the environment coding segment after the function coding segment, these coding segments are spliced ​​together to obtain the KKS coding sequence "1234-TR-VT-OS".

[0164] As an implementation manner, step S400 may specifically include the following steps S410 to S450:

[0165] Step S410: Parse the spatial coding rules in the KKS coding rule model, extract the spatial coordinate information of the target grid unit in the fused data set, and generate a first coding segment reflecting the spatial position of the facility based on the spatial coordinate information.

[0166] The spatial encoding rule is the rule used in the KKS encoding rule model to convert spatial coordinate information into a code. The first code segment is the code portion that reflects the spatial location of the facility. For example, the spatial coordinate information of the target grid cell, typically including the X, Y, and Z coordinates, is extracted from the fused data set. This coordinate information is then processed according to the spatial encoding rule to generate the first code segment.

[0167] Step S420: Parse the attribute encoding rules in the KKS encoding rule model, extract the type identification field of the facility in the fused data set, and generate a second encoding segment reflecting the facility type according to the type identification field.

[0168] The attribute encoding rules in the KKS encoding rule model are used to convert facility type identification information into codes. The second encoding segment is the code portion that reflects the facility type. For example, the attribute encoding rules in the KKS encoding rule model are analyzed to clarify the corresponding codes. The facility type identification field is extracted from the fused data set, and then converted into the corresponding code according to the attribute encoding rules.

[0169] For example, the attribute coding rules specify a mapping table of codes corresponding to different facility types, such as "generator" corresponding to "GD," "transformer" corresponding to "TR," and "motor" corresponding to "MD." If the type identification field of the facility in the fused data set is "transformer," the second code segment generated according to the attribute coding rules is "TR."

[0170] Step S430: parse the function coding rules in the KKS coding rule model, extract the function classification description fields of the facilities in the fusion data set, and generate a third coding segment reflecting the function of the facility according to the function classification description fields.

[0171] The function coding rule is a rule in the KKS coding rule model used to convert the function classification description information of the facility into a code, and the third coding segment is a coding part used to reflect the function of the facility. For example, the function classification description field of the facility can be extracted from the fused data set, and the field can be analyzed and processed to extract the key function information therein. The key function information is then converted into the corresponding code according to the function coding rule. For example, the function coding rule stipulates that the function keywords are converted into a specific letter combination. For a water pump facility, its function classification description field is "pumping water from a low place to a high place", and the key function information extracted from it is "pumping water". In the function coding rule, "pumping water" corresponds to the code "WP", and the third coding segment generated according to the rule is "WP".

[0172] Step S440: parse the environment coding rules in the KKS coding rule model, extract the installation environment feature field of the facility in the fusion data set, and generate a fourth coding segment reflecting the facility environment based on the installation environment feature field.

[0173] The environmental coding rules are the rules within the KKS coding rule model used to convert a facility's installation environment characteristics into codes. The fourth coding segment is the coding portion used to reflect the facility's installation environment. For example, the facility's installation environment characteristic field is extracted from the fused data set, analyzed, and key environmental characteristic information is identified. This key environmental characteristic information is then converted into the corresponding codes according to the environmental coding rules.

[0174] For example, the environmental coding rules specify the codes corresponding to different installation environment characteristics. If the facility's installation environment characteristic field is "In a damp basement," the key environmental characteristics identified are "humid" and "basement." The environmental coding rules assign the code "MH" to "humid" and "BS" to "basement." Combining these two codes creates the fourth code segment "MH - BS."

[0175] Step S450: splicing 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 spatial location and attribute information.

[0176] The preset order is the concatenation order of the various coding segments predefined in the KKS coding rule model. By concatenating the first, second, third, and fourth coding segments in this order, a complete KKS coding sequence can be obtained. For example, a preset coding concatenation order is determined, such that the spatial coding segment comes first, the type coding segment comes second, the function coding segment comes after the type coding segment, and the environment coding segment comes after the function coding segment. The character sequences of the first, second, third, and fourth coding segments are extracted, and then these coding segments are sequentially concatenated in the preset order. The coding segments are separated by a preset delimiter. Finally, the concatenated character sequence is format-checked to ensure that the character length of each coding segment meets the requirements of the KKS coding rule model. The character sequence that passes the format check is used as the KKS coding sequence corresponding to the facility's spatial location and attribute information.

[0177] For example, the preset code concatenation sequence is "first code segment - second code segment - third code segment - fourth code segment," with the delimiter "-." 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." The resulting sequence is "532 - TR - WP - MH - BS." This character sequence is format-checked to see if the length of each code segment complies with the KKS encoding rule model. If so, the sequence is the KKS code sequence corresponding to the facility's spatial location and attribute information.

[0178] As an implementation manner, step S450 may specifically include the following steps S451 to S455:

[0179] Step S451: Determine the preset code splicing order as follows: the space code segment is in front, the type code segment is in the back, the function code segment is after the type code segment, and the environment code segment is after the function code segment.

[0180] The preset code splicing order is predefined in the KKS code rule model. This order clearly reflects the facility's spatial location, type, function, and installation environment information in the code sequence. This order is typically determined during the design phase of the KKS code rule model, based on actual application requirements and factors such as code readability and maintainability. During step S450, this preset code splicing order is directly retrieved from the KKS code rule model.

[0181] Step S452: extracting character sequences of the first coding segment, the second coding segment, the third coding segment, and the fourth coding segment as a space coding segment, a type coding segment, a function coding segment, and an environment coding segment, respectively.

[0182] In the previous steps, the first, second, third, and fourth coding segments have been generated, respectively. These coding segments may be stored in different data formats. In this step, they need to be converted into character sequences for subsequent splicing operations. The extraction method can be processed accordingly based on the storage format of the coding segments. For example, if the coding segments are stored in digital form, they can be converted into a string type; if they are stored in object or array form, the key character information can be extracted.

[0183] For example, the first code segment is stored in integer form as "532", which is converted into the string "532" as a space code segment; the second code segment is stored in string form as "TR", which is directly used as a type code segment; the third code segment is stored in array form, which contains character elements "W" and "P", and these elements are combined into the string "WP" as a function code segment; the fourth code 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 an environment code segment.

[0184] Step S453: Connect the space coding segment, type coding segment, function coding segment and environment coding segment in sequence, and use a preset delimiter to separate the coding segments.

[0185] According to the preset code splicing order determined in step S451, the spatial code segment, type code segment, function code segment, and environment code segment are sequentially connected, and adjacent code segments are separated by a preset delimiter. This makes the code sequence clearer and easier to read. The preset delimiter can be a symbol such as "-", " / ", etc., which is pre-defined in the KKS coding rule model.

[0186] For example, the space coding segment is "532", the type coding segment is "TR", the function coding segment is "WP", the environment coding segment is "MH - BS", and the preset separator is "-", then the connected character sequence is "532 - TR - WP - MH - BS".

[0187] Step S454: Perform format check on the connected character sequence so that the character length of each encoding segment meets the requirements of the KKS encoding rule model.

[0188] Format checking ensures that the generated KKS encoding sequence conforms to the KKS encoding rule model. The character length of each encoding segment must be within the specified range. For example, the concatenated character sequence is split according to the character length requirements of the KKS encoding rule model, and each encoding segment is checked for compliance. If a segment does not meet the required length, appropriate action can be taken, such as truncating any excessively long segments or padding any excessively short segments with zeros.

[0189] For example, the KKS encoding rule model stipulates that the character length of the spatial code segment is 3 to 5 bits, the character length of the type code segment is 2 to 3 bits, the character length of the function code segment is 2 to 4 bits, and the character length of the environment code segment is 3 to 6 bits. For the concatenated character sequence "532 - TR - WP - MH - BS", check that the character length of the spatial code segment "532" is 3 bits, which meets the requirement; the character length of the type code segment "TR" is 2 bits, which meets the requirement; the character length of the function code segment "WP" is 2 bits, which meets the requirement; and the character length of the environment code segment "MH - BS" is 5 bits, which meets the requirement. If a code segment does not meet the requirements, such as the spatial code segment "53", which is 2 bits long and does not meet the 3 to 5 bit requirement, it can be padded with zeros to adjust it to "530".

[0190] Step S455: The character sequence that passes the format check is used as the KKS encoding sequence corresponding to the facility spatial location and attribute information.

[0191] When the concatenated character sequence passes format verification and the character length of each encoding segment meets the requirements of the KKS encoding rule model, the character sequence can be used as the final KKS encoding sequence, which accurately reflects the spatial location, type, function, and installation environment of the facility. This character sequence is stored or applied to the corresponding system for facility identification and management.

[0192] For example, after format verification, the character sequence "532 - TR - WP - MH - BS" meets the requirements of the KKS encoding rule model and is used as the KKS encoding sequence corresponding to the spatial location and attribute information of the facility. This encoding sequence can be used to uniquely identify the facility in the subsequent facility management system.

[0193] Step S500: Perform uniqueness verification on the KKS code sequence, generate a verification result set including codes that passed verification and codes that failed verification, and perform parameter adjustment processing on the fusion data set corresponding to the codes that failed verification to regenerate a valid KKS code.

[0194] Uniqueness verification ensures that the generated KKS code sequence is unique across the entire system, preventing code duplication. The verification result set includes both verified and unverified codes. For codes that failed verification, the corresponding fused data set must undergo parameter adjustments and then be regenerated until the generated code passes uniqueness verification. For example, a code uniqueness verification database is first established, and each code in the KKS code sequence is individually compared with existing codes in the code uniqueness verification database. Codes that duplicate existing codes in the comparison results are recorded as failed verification codes, while codes that do not duplicate are recorded as passed verification codes, generating a verification result set. For each unverified code, the spatial coordinate information, type identification field, functional classification description field, and installation environment feature field are extracted from the corresponding fused data set. This information is analyzed to determine whether code duplication is caused by factors such as excessively high or low grid resolution, overly coarse or detailed type classification, vague or redundant functional descriptions, or environmental feature coverage outside the preset range. According to the analysis results, the grid resolution level, type classification accuracy, functional description depth and environmental feature coverage in the fused data set are adjusted, the KKS encoding rule model is re-called to generate a new KKS code, and the uniqueness verification process is performed again until a valid KKS code is generated.

[0195] As an implementation manner, step S500 may specifically include the following steps S510 to S580:

[0196] Step S510: Establish a code uniqueness verification database, and compare each code in the KKS code sequence with the existing codes in the code uniqueness verification database one by one.

[0197] The code uniqueness verification database is used to store existing KKS codes. By comparing a newly generated KKS code sequence with existing codes in the database, it can be determined whether the new code is a duplicate. The code uniqueness verification database can be established using a database management system such as MySQL or Oracle, creating a dedicated table to store code information. The process for comparing each code in the KKS code sequence with existing codes in the database is as follows: Using a database query statement, all existing codes are retrieved from the database, and then each code in the KKS code sequence is compared with these existing codes one by one.

[0198] For example, create a table named "kks_codes" in a MySQL database with a field named "code" to store KKS codes. For a newly generated KKS code sequence, write a Python program that connects to the database using a database connection library (such as "pymysql") and executes the query "SELECT code FROM kks_codes" to retrieve all existing codes. Then, iterate over each code in the KKS code sequence and compare it with the existing codes obtained from the query.

[0199] Step S520: Record the codes that are repeated with the existing codes in the comparison results as the codes that failed the verification, and record the codes that are not repeated as the codes that passed the verification, and generate a verification result set.

[0200] During the comparison process, any KKS code that is duplicated with an existing code is marked as a failed verification code; any KKS code that is not duplicated with an existing code is marked as a passed verification code. Two lists can be used to store the passed and failed verification codes, respectively, to generate a verification result set.

[0201] Step S530: For each code that fails verification, extract the spatial coordinate information, type identification field, function classification description field, and installation environment feature field in the corresponding fusion data set.

[0202] For codes that failed verification, further analysis is needed to determine the cause of the duplicate code. This requires extracting relevant information from the corresponding fused dataset. This information can be extracted by searching the fused dataset for records corresponding to the failed verification code. The fused dataset can be a table or object array containing multiple records, each of which contains spatial coordinate information, a type identifier field, a functional classification description field, and installation environment characteristics fields.

[0203] For example, the fused data set is stored as a list containing multiple dictionaries. Each dictionary represents the information of a grid cell and contains key-value pairs such as "X coordinate," "Y coordinate," "Z coordinate," "type identifier," "functional classification description," and "installation environment characteristics." For a code that fails verification, the fused data set is traversed to find the record corresponding to the code. The spatial coordinate information (such as the values ​​of "X coordinate," "Y coordinate," and "Z coordinate"), as well as the values ​​of the type identifier field, functional classification description field, and installation environment characteristics field are extracted.

[0204] Step S540: Analyze the grid resolution level of the spatial coordinate information to determine whether there is a coding duplication problem caused by the grid resolution being too high or too low.

[0205] The grid resolution level affects the generation of spatial code segments. If the grid resolution is too high, the spatial code may be too detailed, which may easily lead to code duplication. If the grid resolution is too low, the spatial code may be too general, which may also lead to code duplication. The execution process of analyzing the grid resolution level of spatial coordinate information and determining whether there is a code duplication problem is as follows: Based on the spatial coordinate information and relevant data of the grid resolution level in the fused data set, the generation of spatial code segments is analyzed. If multiple different facilities are found to be close in space, but their spatial code segments are very similar or even identical due to the high grid resolution, then the code duplication may be caused by the high grid resolution. If the spatial code segments of facilities in multiple different areas are the same, it may be because the grid resolution is too low, and the spatial locations of different areas cannot be distinguished.

[0206] Step S550: Analyze the classification accuracy of the type identification field to determine whether there is a coding duplication problem caused by the type classification being too coarse or too detailed.

[0207] The accuracy of type classification affects the generation of type code segments. If the type classification is too coarse, different types of facilities may be classified into the same category, resulting in repeated type code segments. If the type classification is too detailed, some unnecessary subdivisions may appear, increasing the complexity of the coding and possibly leading to repeated coding. For example, a statistical analysis of the type identification field is performed to check whether multiple different facilities have the same type identification field but different actual types, or whether the type identification field is too detailed, resulting in repeated coding.

[0208] Step S560: Analyze the description depth of the function classification description field to determine whether there is a coding duplication problem caused by the vague or redundant function description.

[0209] The depth of the functional description will affect the generation of the functional coding segment. If the functional description is too general, facilities with different functions may be described as having the same function, resulting in repeated functional coding segments. If the functional description is too redundant, it may contain some unnecessary information, increasing the complexity of the coding and also causing repeated coding. For example, semantic analysis is performed on the functional classification description field to extract the key functional information. Check whether the functional description fields of multiple different facilities have the same key functional information after processing, but the actual functions are different, or whether the functional description field contains too much irrelevant information, resulting in repeated coding.

[0210] Step S570: Analyze the coverage of the installation environment feature field to determine whether there is a coding duplication problem caused by the environment feature coverage being out of the preset range.

[0211] The coverage of the installation environment feature field affects the generation of the environment code segment. If the environment feature coverage deviates from the preset range, the environment code segment may be inaccurate, resulting in code duplication. For example, the generation of the environment code segment is checked based on the installation environment feature field in the fused data set and the preset environment feature coverage standard. If it is found that the installation environments of multiple different facilities are actually different, but their environment code segments are the same due to inaccurate environment feature coverage, then the code duplication may be caused by the environment feature coverage deviating from the preset range.

[0212] Step S580: According to the analysis results, the grid resolution level, type classification accuracy, functional description depth and environmental feature coverage in the fused data set are adjusted, the KKS encoding rule model is re-called to generate a new KKS code, and the uniqueness verification process is performed again until a valid KKS code is generated.

[0213] Based on the analysis results from steps S540-S570, the relevant parameters in the fused dataset are adjusted. If code duplication is caused by excessively high grid resolution, the grid resolution is reduced; if the type classification is too coarse, the type classification is refined; if the function description is too general, the function description is increased in depth; if the environmental feature coverage is outside the preset range, the environmental feature coverage is adjusted. The KKS encoding rule model is then re-invoked to generate a new KKS code based on the adjusted fused dataset. The new code is again compared with existing codes in the code uniqueness verification database for uniqueness verification. If duplication still occurs in the new code, the above analysis and adjustment process is repeated until the generated code passes uniqueness verification and becomes a valid KKS code.

[0214] For example, if analysis reveals that code duplication is caused by excessively high grid resolution, the grid resolution is reduced by one level. The KKS encoding rule model is re-invoked, and a new KKS code is generated based on the adjusted fused data set. The new code is then compared with the code uniqueness verification database. If verification is successful, the code becomes a valid KKS code. If duplication persists, further analysis and adjustment of other parameters are performed until a valid code is generated.

[0215] It is understandable that the various algorithms involved in the above-mentioned introductions of the embodiments of the present invention, such as statistical filtering algorithms, point cloud data processing algorithms, word segmentation algorithms, etc., can all be learned from the relevant content in the prior art. In order to save space, they will not be expanded too much in the embodiments of the present invention. In addition, when implementing the scheme of the present invention, those skilled in the art can supplement the details according to the common knowledge in this field. For example, according to the common knowledge in this field, normalization can be used to eliminate dimensional conflicts before feature fusion, interpolation can be used to eliminate dimensional differences, and thresholds can be reasonably set based on historical data, experience or business scenario requirements. The model can be trained based on a general model training method, and 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 provide redundant introductions to the overly detailed implementation process.

[0216] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a computer system provided in an embodiment of the present invention. The computer system includes at least a processor 101, a communication interface 102, and a memory 103. The processor 101, communication interface 102, and memory 103 may be connected via a bus or other means. The processor 101 (also known as the Central Processing Unit (CPU)) is the computing and control core of the computer system, capable of parsing various instructions within the computer system and processing various data within the computer system. The communication interface 102 may optionally include a standard wired interface or a wireless interface (such as Wi-Fi, a mobile communication interface, etc.), which can be used to send and receive data under the control of the processor 101. The communication interface 102 may also be used for data transmission and interaction within the computer system. The memory 103 is a storage device in the computer system for storing programs and data. It is understood that the memory 103 herein may 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 storage space, which stores the computer system's operating system, but this is not limited to this in the present invention.

[0217] In one embodiment, the processor 101 executes the KKS code generation method based on the fusion of spatial grid and three-dimensional data provided in the above embodiment of the present invention by running the computer program in the memory 103.

Claims

1. A KKS code generation method based on the fusion of spatial grid and three-dimensional data, characterized in that: The method comprises: Obtaining 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 that matches the spatial range of the target facility area; Associating and 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; Performing uniqueness verification on the KKS code sequence to generate a verification result set including codes that passed verification and codes that failed verification, and performing parameter adjustment processing on the fused data set corresponding to the codes that failed verification to regenerate a valid KKS code; The calling of the 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 includes: parsing the spatial coding rule in the KKS coding rule model, extracting spatial coordinate information of the target grid cell in the fused data set, and generating a first coding segment reflecting the spatial location of the facility according to the spatial coordinate information; Parsing the attribute encoding rule in the KKS encoding rule model, extracting the type identification field of the facility in the fused data set, and generating a second encoding segment reflecting the type of the facility according to the type identification field; Parsing the function coding rule in the KKS coding rule model, extracting the function classification description field of the facility in the fused data set, and generating a third coding segment reflecting the function of the facility according to the function classification description field; Parsing the environment coding rule in the KKS coding rule model, extracting the installation environment feature field of the facility in the fused data set, and generating a fourth coding segment reflecting the facility environment according to the installation environment feature field; Determine the preset code splicing order as follows: the space code segment is in front, the type code segment is in the back, the function code segment is after the type code segment, and the environment code segment is after the function code segment; Extracting a character sequence of the first coding segment as a space coding segment, extracting a character sequence of the second coding segment as a type coding segment, extracting a character sequence of the third coding segment as a function coding segment, and extracting a character sequence of the fourth coding segment as an environment coding segment; Connect the space code segment, type code segment, function code segment and environment code segment in sequence, and separate the code segments using a preset delimiter; Perform format check on the concatenated character sequence so that the character length of each encoding segment meets the requirements of the KKS encoding rule model; The character sequence that passes the format check is used as the KKS encoding sequence corresponding to the facility spatial location and attribute information.

2. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 1 is characterized in that: 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: Use laser scanning equipment to collect spatial information of the target facility area and obtain a sequence of original point cloud data containing X coordinates, Y coordinates, and Z coordinates; Noise filtering is performed on the original point cloud data sequence to remove discrete abnormal points caused by equipment errors, thereby 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 characteristic field of the facility during the design phase; Perform field cleaning on the basic attribute record file to remove duplicate or missing field contents, and generate 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 is characterized in that: The laser scanning device is used to collect spatial information of the target facility area to obtain a sequence of original point cloud data 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 associated and stored to generate an original point cloud data sequence including spatial 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 is characterized in that: 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: Extracting the maximum and minimum values ​​of the X coordinates, Y coordinates, and Z coordinates of all point clouds in the original three-dimensional spatial data set to determine the spatial boundary range of the target facility area; Selecting an initial grid resolution based on the spatial boundary range, dividing the target facility area into three dimensions based on the initial grid resolution to generate a basic spatial grid unit set; Improving the resolution of the grid cells containing key components of the facility in the basic spatial grid cell set, reducing the grid size to a preset ratio of the initial grid resolution, and generating a first-level grid set containing fine grid cells; Reducing the resolution of the grid cells that only contain the facility support structure in the basic spatial grid cell set, and expanding the grid size thereof to a preset multiple of the initial grid resolution, thereby generating a second-level grid set that contains coarse grid cells; The basic spatial grid unit set, the first-level grid set, and the second-level grid set are hierarchically associated to generate a multi-level spatial grid unit 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 is characterized in that: The step of selecting an initial grid resolution based on the spatial boundary range, dividing the target facility area into three dimensions based on the initial grid resolution, and generating a basic spatial grid unit set includes: Calculating the length of the space boundary range in the X-axis direction, the width in the Y-axis direction, and the height in the Z-axis direction; Selecting an initial grid resolution based on the length, width, and height so that the dimensions of each basic spatial grid unit in the X-axis, Y-axis, and Z-axis directions are all the initial grid resolution; Starting from the minimum X coordinate, the minimum Y coordinate, and the minimum Z coordinate of the spatial boundary range, grid lines are divided in the X-axis, Y-axis, and Z-axis directions in sequence according to the initial grid resolution; The intersections of the grid lines in the X-axis, Y-axis, and Z-axis directions form grid vertices in the three-dimensional space, and adjacent grid vertices form basic spatial grid units with a cubic structure. All basic spatial grid units are collectively stored to generate the basic spatial grid unit set.

6. The KKS code generation method based on the fusion of spatial grid and three-dimensional data according to claim 1 is characterized in that: The associating and 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 includes: Extracting a type identification field of each facility in the basic attribute data set, and determining a core functional area of ​​the facility in three-dimensional space according to the type identification field; Determining a target grid cell in the multi-level spatial grid cell set that coincides with the spatial position of the core functional area; Extracting a function classification description field of each facility in the basic attribute data set, and determining a function association range of the facility according to the function classification description field; Determining, in the multi-level spatial grid unit set, an associated grid unit that overlaps with the spatial position of the function association range; Extracting the installation environment characteristic field of each facility in the basic attribute data set, and determining the environmental impact area of ​​the facility according to the installation environment characteristic field; Determining, from the multi-level spatial grid unit set, an environmental grid unit that overlaps with the spatial position of the environmental impact area; The spatial coordinate information of the target grid unit, associated grid unit and environmental grid unit is bound one by one with the type identification field, function classification description field and installation environment characteristic field of the corresponding facility to generate the fused 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 is characterized in that: The step of determining a target grid unit that coincides with the spatial position of the core functional area in the multi-level spatial grid unit set includes: Extracting spatial range description information of the core functional area, the spatial range description information including 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; Traversing each grid cell in the multi-level spatial grid cell set, extracting the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate of each grid cell; 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 whether the maximum X coordinate is not greater than the maximum X coordinate of the core functional area; Determine whether the minimum Y coordinate of the current grid unit 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; Determine whether the minimum Z coordinate of the current grid unit 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 the above judgments are all true, the current grid unit is marked as a target grid unit 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 is characterized in that: The uniqueness verification of the KKS code sequence is performed to generate a verification result set including verification-passed codes and verification-failed codes, and parameter adjustment processing is performed on the fusion data set corresponding to the verification-failed codes to regenerate a valid KKS code, including: Establishing a code uniqueness verification database, and comparing each code in the KKS code sequence with the existing codes in the code uniqueness verification database one by one; Recording the codes that are repeated with the existing codes in the comparison results as codes that failed the verification, and recording the codes that are not repeated as codes that passed the verification, to generate the verification result set; For each code that fails verification, extract the spatial coordinate information, type identification field, function classification description field, and installation environment feature field from the corresponding fusion data set; Analyze the grid resolution level of the spatial coordinate information to determine whether there is a coding duplication problem caused by the grid resolution being too high or too low; Analyze the classification accuracy of the type identification field to determine whether there is a coding duplication problem caused by overly coarse or overly detailed type classification; Analyze the description depth of the function classification description field to determine whether there is a coding duplication problem caused by vague or redundant function descriptions; Analyze the coverage of the installation environment feature field to determine whether there is code duplication caused by the environment feature coverage being out of a preset range; According to the analysis results, the grid resolution level, type classification accuracy, functional description depth and environmental feature coverage in the fused data set are adjusted, the KKS encoding rule model is re-called to generate a new KKS code, and the uniqueness verification process is performed again until a valid KKS code is generated.

9. A computer system, characterized in that: include: a memory, wherein the computer program is stored in the memory; A processor, configured to load the computer program to implement the KKS code generation method based on the fusion of spatial grid and three-dimensional data as described in any one of claims 1 to 8.

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