Business district boundary identification method and system based on spatial data

Abstract

The invention discloses a business district boundary recognition method and system based on spatial data, and belongs to the technical field of spatial data analysis, and the method comprises the steps: collecting business district data, dividing POI data through a spatial grid, constructing a feature matrix, adjusting a feature weight through a time dimension correction factor, and recognizing the initial boundary range of a business district; screening core nodes based on the feature matrix, constructing a basic skeleton structure of the business district, filling detail boundaries in the skeleton structure by adopting a density clustering algorithm, and repairing and optimizing the boundaries; and setting boundary elastic adjustment, dynamically correcting the boundary of the business district according to the change trend of the human flow line and the updating condition of the business facilities, establishing an abnormal early warning mechanism, and correspondingly adopting a boundary adjustment strategy. According to the method, the initial boundary range of the business district is identified, so that the boundary of the business district is more accurate, commercial decision and market analysis are facilitated, boundary breakage caused by data missing is effectively repaired, the integrity and connectivity of the boundary of the business district are ensured, and the actual application value of the business district is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of spatial data analysis, and in particular relates to a method and system for identifying business district boundaries based on spatial data. Background Art

[0002] In the context of rapid urbanization today, accurate identification of business district boundaries has become increasingly important for business planning, market analysis, and resource allocation. Traditional methods for defining business districts rely on empirical judgment or simple spatial analysis methods, which often face many limitations. With the rapid development of big data technology, geographic information systems (GIS), and spatial analysis methods, business district boundary identification technology based on spatial data has gradually emerged, forming a quantitative analysis of business districts and their activities. However, most existing technologies are still insufficient in terms of dynamics and flexibility. Traditional methods for identifying business district boundaries rely on static data and cannot reflect changes in traffic, consumer behavior, and business environment in real time, resulting in information lags in business decisions and affecting sensitivity and responsiveness to market dynamics.

[0003] Existing business district boundary recognition technologies generally have unsatisfactory aspects in terms of data sources, feature extraction, and boundary adjustment. First, many methods rely on a single data source, such as using only historical sales data or geographic location information, and cannot fully capture the impact of multi-dimensional information on business district boundaries. At the same time, existing technologies often lack sufficient detail in the construction of feature matrices and cannot fully reflect the interactive relationship between various commercial facilities in the business district and their spatial layout characteristics. In addition, the dynamic adjustment ability of existing business district boundaries is weak, and it fails to fully consider real-time changing factors such as traffic trends, seasonal changes, and the update status of surrounding commercial facilities, making it difficult to achieve flexible adaptation of business district boundaries. This leads to insufficient stability and reliability of business district identification results, which in turn affects the effectiveness of commercial operations and the optimization of strategic layout. Summary of the invention

[0004] In view of the above existing problems, the technical problem solved by the present invention is to solve the core technical problems such as insufficient data integration, inaccurate boundary determination and insensitivity to dynamic changes in the process of identifying the business district boundary. Specifically, the method collects multi-dimensional business district data, including POI data and basic data, uses spatial grid division and feature matrix construction, dynamically adjusts weights, identifies core commercial agglomeration areas and realizes flexible adjustment of boundaries and abnormal warning, thereby improving the accuracy and adaptability of business district boundary identification and ensuring that business district management can respond to market changes in a timely manner.

[0005] In order to solve the above technical problems, a method for identifying business district boundaries based on spatial data is proposed, including: Collect business district data, divide POI data through spatial grids, construct a feature matrix, adjust feature weights through time dimension correction factors, and identify the initial boundary range of the business district; screen core nodes based on the feature matrix, build the basic skeleton structure of the business district, use density clustering algorithm to fill in the detailed boundaries in the skeleton structure, and perform boundary repair and optimization; set boundary elasticity adjustment, dynamically correct the business district boundary according to the changing trend of pedestrian flow lines and the update of commercial facilities, establish an abnormal warning mechanism, and take corresponding boundary adjustment strategies.

[0006] As a preferred solution of the method for identifying business district boundaries based on spatial data described in the present invention, wherein: the business district data includes POI data and basic data; The POI data is point of interest data of commercial facilities; the basic data includes: the category attributes of the POI include business type and scale level, and the spatial location includes longitude and latitude, business hours, passenger flow, surrounding road network density and historical data; The construction of the feature matrix includes dividing the collected POI data into spatial grids, generating a feature vector for each grid unit, including business density, functional complementarity and spatial accessibility, and combining the feature vectors into a matrix as input data for business district identification.

[0007] As a preferred solution of the method for identifying the boundary of a business district based on spatial data described in the present invention, the identification of the initial boundary range of the business district includes calculating the business type association of each grid unit according to the historical data of passenger flow, scoring the functional integrity through the diversity of POI categories, evaluating the completeness of the regional commercial functions, and dynamically adjusting the weight of each grid unit; The weights of all grid cells are combined into a dynamic weight matrix and the time series of historical passenger flow data are extracted to analyze the passenger flow change patterns in different time periods; According to the changing rules of passenger flow, a time correction factor is assigned to each grid unit, and the weight value in the feature matrix is ​​adjusted; Feed the corrected weight values ​​back to the dynamic weight matrix for dynamic optimization in the time dimension, continuously optimize, and identify the initial boundary range of the core commercial agglomeration area; The weight value in the time correction factor adjustment feature matrix is ​​adjusted using a dual-dimensional mechanism to set a time limit, with the first dimension being the time attenuation factor and the second dimension being the time enhancement factor, and the two being linearly superimposed to generate a final correction value; A time decay factor is used for historical data, that is, an exponential decay model is used to process the collected historical data. The weight decay amplitude is adjusted for each additional time period of historical data. A time enhancement factor is used for the collected non-historical data, that is, a multiple enhancement model is used for processing. If the current time is within the time limit, a first weight is dynamically assigned; if the current time is a statutory holiday, a second weight is dynamically assigned.

[0008] As a preferred solution of the method for identifying business district boundaries based on spatial data described in the present invention, the basic skeleton structure of building the business district includes screening out grid cells with weight values ​​higher than a threshold as core nodes based on a dynamic weight matrix, specifically: An improved hierarchical clustering algorithm is used to cluster core nodes according to spatial proximity and weight similarity to form the basic skeleton structure of the business district, which is composed of the core business district area and the main connection paths. Each time a hierarchical clustering is completed, the silhouette coefficient of the current clustering result is calculated. When the coefficient is less than the coefficient threshold, the number of clusters is automatically increased by the specified amplitude and recalculated; Under the constraints of the skeleton structure, the radiation distance of the core nodes is set. When it is detected that the distance between two core nodes exceeds the radiation distance, an independent sub-business district is automatically generated with the middle point of the two core nodes as the dividing line, triggering the construction of the secondary skeleton; Based on the secondary skeleton construction, the secondary commercial agglomeration area is identified through the density clustering algorithm to form a detailed boundary, traverse all grid cells, calculate the core point attributes of each cell, further identify the core nodes, merge the identified core points and neighboring cells to form a detailed boundary area; At the same time, for the connection paths connecting the core nodes, the actual travel time is obtained by obtaining GIS. When the actual time is greater than twice the theoretical time, a virtual node is inserted to refine the detail boundary; Integrate the core business district area formed by the basic skeleton structure and the secondary business area formed by the secondary skeleton into an initial business district scope, and further repair and optimize the boundaries.

[0009] As a preferred solution of the method for identifying business district boundaries based on spatial data described in the present invention, the boundary repair and optimization includes repairing boundary breaks caused by data loss through spatial adjacency connectivity constraints; The adjacency connectivity constraint is that each boundary area must be directly adjacent to at least two core nodes; When the boundary area is connected to only one core node, the connection path is completed through the path search algorithm; Detect broken boundaries, mark all isolated areas that do not meet the adjacent connectivity constraints, generate a minimum spanning tree based on the road network data, and complete the missing connections between core nodes; A buffer zone is generated for the patched boundary, and a patch record database is established to record the parameters and effect evaluation indicators of each patching operation.

[0010] As a preferred solution of the method for identifying business district boundaries based on spatial data described in the present invention, the elastic adjustment of the boundaries includes real-time monitoring of the changing trend of pedestrian flow lines and the updating of commercial facilities, making elastic adjustments to the boundaries, and calculating the amplitude of the boundary adjustment. ; Dynamically modify the business district boundary according to the adjustment range to ensure that the boundary shape is consistent with the actual business activities. Specifically: when the first modification threshold < When the value is less than or equal to the second correction threshold, only the boundary point coordinates are adjusted, and the maximum offset distance does not exceed the extended threshold range of the original boundary; When the second correction threshold < When the third correction threshold is reached, press Direction adjustment boundary, amplitude ; when >When the third correction threshold is reached, the early warning mode is triggered, the automatic correction is frozen, the manual review is triggered and the regional reconstruction is started.

[0011] As a preferred solution of the method for identifying business district boundaries based on spatial data described in the present invention, the abnormal warning mechanism includes establishing an abnormal warning mechanism. When the third correction threshold is less than When the value is less than or equal to the fourth correction threshold, the region is reconstructed, the skeleton structure is kept unchanged, the density clustering algorithm is re-executed to identify the secondary commercial agglomeration area, the detailed boundary is formed, and manual review and correction are performed; when >When the fourth correction threshold is reached, the original skeleton structure is cancelled, triggering the re-identification of the initial boundary range of the core commercial cluster area.

[0012] Another object of the present invention is to provide a business district boundary identification system based on spatial data. The present invention accurately identifies and maintains the boundaries of business districts through efficient data processing and dynamic adjustment to support business decisions and urban development planning. The system aims to achieve the following key goals: first, the data adjustment module is used to collect and process multi-dimensional POIs and basic data, and an accurate feature matrix is ​​constructed to lay a solid foundation for the identification of business district boundaries; second, through the boundary generation module, core nodes are screened out based on the feature matrix, the skeleton structure of the business district is established, and density clustering is used to optimize the detailed boundaries to ensure the integrity and accuracy of the boundaries; finally, the self-correction module can monitor the changes in pedestrian flow lines and commercial facilities in real time, dynamically adjust the boundaries, and trigger warnings in abnormal situations to ensure that the boundaries of the business district are always adapted to actual business activities.

[0013] As a preferred solution of the business district boundary recognition system based on spatial data described in the present invention, it includes a data adjustment module, a boundary generation module, and a self-correction module; The data adjustment module collects and processes raw data and adjusts feature weights according to the time dimension correction factor; The boundary generation module constructs the basic skeleton structure of the business district based on the feature matrix and uses a density clustering algorithm to fill and repair the boundary; The self-correction module dynamically corrects and optimizes the business district boundary provided by the boundary generation module, performs boundary elastic adjustment, establishes an abnormal warning mechanism, and triggers manual review and area reconstruction.

[0014] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a method for identifying business district boundaries based on spatial data when executing the computer program.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for identifying business district boundaries based on spatial data.

[0016] Beneficial effects of the present invention: The present invention collects business district data and spatial grid division POI data, constructs a feature matrix, and realizes the identification of the initial boundary range of the business district. It is of great significance for the functional division of the business district and the identification of the gathering area, making the boundary of the business district more accurate, which is conducive to business decision-making and market analysis.

[0017] By selecting core nodes and filling boundaries with density clustering algorithms, we can effectively repair boundary breaks caused by missing data, ensure the integrity and connectivity of the business district boundaries, and enhance the actual application value of the business district. In addition, through a dynamic boundary elastic adjustment mechanism, the business district boundaries can be corrected in real time according to the changing trends of pedestrian flow lines and the update of commercial facilities, ensuring that the boundary shape is highly consistent with commercial activities, thereby adapting to market demand and enhancing commercial vitality.

[0018] Establish an abnormal early warning mechanism, quickly take regional reconstruction measures under certain threshold conditions, effectively respond to sudden changes, and improve the flexibility and response speed of business district management. These steps jointly promote the accuracy and practicality of business district boundary identification, and provide data support and decision-making basis for urban commercial development. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which: Figure 1An overall flow chart of a method for identifying business district boundaries based on spatial data provided by an embodiment of the present invention.

[0020] Figure 2 A system solution module diagram of a business district boundary recognition system based on spatial data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0024] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0025] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0026] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a method for identifying business district boundaries based on spatial data, including: S1: Collect business district data, divide POI data into spatial grids, construct a feature matrix, adjust feature weights through time dimension correction factors, and identify the initial boundary range of the business district.

[0028] It should be noted that the business district data includes POI data and basic data; The POI data is point of interest data of commercial facilities, including but not limited to shopping malls, restaurants, office buildings, transportation hubs, etc.; The basic data includes: the category attributes of POI include business type, scale level, spatial location includes longitude and latitude, business hours, passenger flow, surrounding road network density, and historical data.

[0029] Clean the collected data, call the GIS data of the city's geographical scope for the POI data, and delete isolated POI points that exceed the actual geographical scope of the city; The historical passenger flow data is smoothed using the sliding window method with a window size of 7 days to eliminate abnormal fluctuations in a single day; A POI status monitoring table is established to automatically mark POIs with zero passenger flow for 30 consecutive days as "invalid" and set their business density value to zero in the feature matrix.

[0030] The constructing of the feature matrix includes dividing the collected POI data into spatial grids, and generating a feature vector for each grid unit, including business density, functional complementarity, and spatial accessibility; The business density is the percentage of any type of POI; The functional complementarity is the spatial proximity of POIs of different business formats; The spatial accessibility is the shortest path distance based on the road network, that is, walking distance rather than straight-line distance; The feature vectors are combined into a matrix as input data for business district identification.

[0031] Furthermore, the business type correlation of each grid unit is calculated based on the historical passenger flow data to reflect the synergy between different business types; the functional completeness score is calculated through the diversity of POI categories to evaluate the completeness of regional commercial functions.

[0032] Dynamically adjust the weight of each grid unit to ensure that the weight of the core commercial agglomeration area increases: ; in, For the The weight value of the grid cell, and are adjustment factors, respectively controlling the impact of business density and functional complementarity. For the The number of POIs of a certain type in a grid cell, is the total number of POIs in the area, For the The business type correlation of each grid unit, For the The functional integrity score of each grid cell; The weights of all grid units are combined into a dynamic weight matrix and the time series of historical passenger flow data are extracted. The passenger flow change patterns in different time periods are analyzed. According to the passenger flow change patterns, a time correction factor is assigned to each grid unit, and the weight value in the feature matrix is ​​adjusted. The corrected weight value is fed back to the dynamic weight matrix for dynamic optimization in the time dimension. The initial boundary range of the core commercial agglomeration area is identified through continuous optimization. The weight value in the time correction factor adjustment feature matrix is ​​adjusted using a dual-dimensional mechanism, with the time limit set to 1 month. Dimension one is the time attenuation factor, and dimension two is the time enhancement factor. The two are linearly superimposed to generate the final correction value. Specifically, a time decay factor is used for historical data, that is, an exponential decay model is used to process the collected historical data. For each additional time period of historical data, the weight decay is adjusted by 15%; A time enhancement factor is used for the collected non-historical data, that is, a multiple enhancement model is used for processing. If the current time is within the time limit, a 1.3-fold weight is dynamically assigned; if the current time is a statutory holiday, a 2-fold weight is dynamically assigned.

[0033] S2: Based on the feature matrix, core nodes are selected to build the basic skeleton structure of the business district. The density clustering algorithm is used to fill in the detailed boundaries in the skeleton structure, and the boundaries are repaired and optimized.

[0034] Furthermore, based on the dynamic weight matrix, grid cells with weight values ​​higher than the threshold are selected as core nodes, specifically: An improved hierarchical clustering algorithm is used to cluster core nodes according to spatial proximity and weight similarity to form the basic skeleton structure of the business district, which is composed of the core business district area and the main connection paths. Each time a hierarchical clustering is completed, the silhouette coefficient of the current clustering result is calculated. When the coefficient is less than the coefficient threshold, the number of clusters is automatically increased by 20% and recalculated; Under the constraints of the skeleton structure, the secondary commercial agglomeration area is identified through the density clustering algorithm, the detailed boundary is formed, all grid cells are traversed, and the core point attributes of each cell are calculated: ; in, To determine the grid unit Is it a core point? for The set of neighborhood grid cells of is the weight value of the neighborhood grid cell, for and The spatial distance is the distance attenuation coefficient, is the core point determination threshold, , is the mean of the dynamic weight matrix, is the standard deviation of the dynamic weight matrix; The identified core points and neighborhood units are merged to form detailed boundary areas.

[0035] At the same time, under the constraints of the skeleton structure, the radiation distance of the core nodes is set. When it is detected that the distance between two core nodes exceeds the radiation distance, an independent sub-business district is automatically generated with the middle point of the two core nodes as the dividing line, triggering the construction of the secondary skeleton; Based on the secondary skeleton construction, the secondary commercial agglomeration area is identified through the density clustering algorithm to form a detailed boundary, traverse all grid cells, calculate the core point attributes of each cell, further identify the core nodes, merge the identified core points and neighboring cells to form a detailed boundary area; At the same time, for the connection paths connecting the core nodes, the actual travel time is obtained by obtaining GIS. When the actual time is greater than twice the theoretical time, a virtual node is inserted to refine the detail boundary; Integrate the core business district area formed by the basic skeleton structure and the secondary business area formed by the secondary skeleton into an initial business district scope, and further repair and optimize the boundaries.

[0036] It should be noted that the boundary breaks caused by missing data are repaired through spatial adjacency connectivity constraints; The adjacency connectivity constraint is that each boundary area must be directly adjacent to at least two core nodes (share edges or corners); when the boundary area is only connected to one core node, the connection path is completed through the path search algorithm; Detect broken boundaries, mark all isolated areas that do not meet the adjacent connectivity constraints, generate a minimum spanning tree based on the road network data, and complete the missing connections between core nodes; A 50-meter buffer zone is generated for the repaired boundary, and areas in the buffer zone with POI density 80% higher than the original boundary are included in the formal boundary. A repair record database is established to record the parameters and effect evaluation indicators of each repair operation.

[0037] S3: Set flexible boundary adjustments, dynamically revise the business district boundaries according to the changing trends of pedestrian flow lines and the updates of commercial facilities, establish an abnormal warning mechanism, and adopt corresponding boundary adjustment strategies.

[0038] Furthermore, we monitor the changing trend of pedestrian flow and the update of commercial facilities in real time, make elastic adjustments to the boundaries, and calculate the extent of boundary adjustments: ; in, is the boundary adjustment amplitude, is the elastic coefficient, is the change of human flow line, For commercial facility renewal, and They are the historical maximum values ​​for pedestrian flow lines and commercial facilities respectively; Dynamically modify the boundaries of the business district according to the adjustment range to ensure that the boundary shape is consistent with actual business activities. Specifically: when When , only the coordinates of the boundary points are adjusted, and the maximum offset distance does not exceed 5% of the original boundary; when When Direction adjustment boundary, amplitude ; when When the warning mode is triggered, the automatic correction is frozen, the manual review is triggered and the regional reconstruction is started; Establish an abnormal early warning mechanism. When the area is reconstructed, the skeleton structure is kept unchanged, and the density clustering algorithm is re-executed to identify the secondary commercial agglomeration area and form the detailed boundary; when When the original skeleton structure is cancelled, the initial boundary range of the core commercial cluster area is re-identified.

[0039] Among them, the first correction threshold is 0, the second correction threshold is 0.1, the third correction threshold is 0.3, and the fourth correction threshold is 0.6.

[0040] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that: If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0041] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0042] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0043] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination of the two: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0044] Example 3, reference Figure 2 , which is the third embodiment of the present invention, and which provides a business district boundary recognition system based on spatial data, including a data adjustment module, a boundary generation module, and a self-correction module; The data adjustment module is responsible for collecting and processing raw data, including POI data and basic data. The collected POI data is divided into spatial grids, and each grid unit generates a feature vector, constructs a feature matrix as input data for business district identification, and adjusts the feature weight according to the time dimension correction factor; The business type association of each grid unit is calculated based on the historical data of passenger flow, and the functional integrity score is scored through the diversity of POI categories to evaluate the completeness of regional commercial functions. The weight of each grid unit is dynamically adjusted. The time correction factor is used to adjust the weight value in the feature matrix using a two-dimensional mechanism. The time limit is set. Dimension one is the time attenuation factor, and dimension two is the time enhancement factor. The two are linearly superimposed to generate the final correction value, providing the processed feature matrix and initial boundary range for the boundary generation module, laying a data foundation for subsequent boundary identification and optimization; The boundary generation module receives the feature matrix provided by the data adjustment module, and based on the dynamic weight matrix, selects the grid cells with weight values ​​higher than the threshold as core nodes to build the basic skeleton structure of the business district. Under the constraints of the skeleton structure, the radiation distance of the core nodes is set. When it is detected that the distance between two core nodes exceeds the radiation distance, an independent sub-business district is automatically generated with the middle point of the two core nodes as the dividing line, triggering the secondary skeleton construction; according to the secondary skeleton construction, the secondary commercial agglomeration area is identified through the density clustering algorithm to form a detailed boundary, traverse all grid cells, calculate the core point attributes of each cell, further identify the core node, merge the identified core points and neighboring cells to form a detailed boundary area, and perform boundary repair and optimization at the same time, and pass the results to the self-correction module for dynamic adjustment; The self-correction module repairs the boundary breaks caused by data missing based on the business district boundary provided by the boundary generation module through spatial adjacency connectivity constraints, and performs dynamic correction and optimization to ensure the accuracy and timeliness of the business district boundary. The self-correction module is responsible for real-time monitoring of the changing trends of pedestrian flow lines and the updating of commercial facilities, making flexible adjustments to the boundaries, establishing an abnormal early warning mechanism, and triggering manual review and regional reconstruction when necessary. At the same time, the adjustment results are fed back to the boundary generation module to achieve closed-loop operation of the entire system.

[0045] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for identifying business district boundaries based on spatial data, characterized by: include, Collect business district data, divide POI data into spatial grids, construct feature matrix, adjust feature weights through time dimension correction factors, and identify the initial boundary range of the business district; Based on the feature matrix, core nodes are selected to build the basic skeleton structure of the business district. The density clustering algorithm is used to fill in the detailed boundaries in the skeleton structure, and the boundaries are repaired and optimized. Set flexible boundary adjustments, dynamically revise business district boundaries based on changing trends in pedestrian flow and updates on commercial facilities, establish an abnormal warning mechanism, and adopt corresponding boundary adjustment strategies.

2. The method for identifying business district boundaries based on spatial data according to claim 1, characterized in that: The business district data includes POI data and basic data; The POI data is point of interest data of commercial facilities; the basic data includes: the category attributes of the POI include business type and scale level, and the spatial location includes longitude and latitude, business hours, passenger flow, surrounding road network density and historical data; The construction of the feature matrix includes dividing the collected POI data into spatial grids, generating a feature vector for each grid unit, including business density, functional complementarity and spatial accessibility, and combining the feature vectors into a matrix as input data for business district identification.

3. The method for identifying business district boundaries based on spatial data according to claim 2, characterized in that: The identification of the initial boundary range of the business district includes calculating the business type association of each grid unit based on the historical data of passenger flow, scoring the functional completeness through the diversity of POI categories, evaluating the completeness of the regional commercial functions, and dynamically adjusting the weight of each grid unit; The weights of all grid cells are combined into a dynamic weight matrix and the time series of historical passenger flow data are extracted to analyze the passenger flow change patterns in different time periods; According to the changing rules of passenger flow, a time correction factor is assigned to each grid unit, and the weight value in the feature matrix is ​​adjusted; Feed the corrected weight values ​​back to the dynamic weight matrix for dynamic optimization in the time dimension, continuously optimize, and identify the initial boundary range of the core commercial agglomeration area; The weight value in the time correction factor adjustment feature matrix is ​​adjusted using a dual-dimensional mechanism to set a time limit, with the first dimension being the time attenuation factor and the second dimension being the time enhancement factor, and the two being linearly superimposed to generate a final correction value; A time decay factor is used for historical data, that is, an exponential decay model is used to process the collected historical data. The weight decay amplitude is adjusted for each additional time period of historical data. A time enhancement factor is used for the collected non-historical data, that is, a multiple enhancement model is used for processing. If the current time is within the time limit, a first weight is dynamically assigned; if the current time is a statutory holiday, a second weight is dynamically assigned.

4. The method for identifying business district boundaries based on spatial data according to claim 3, characterized in that: The basic skeleton structure of building a business district includes selecting grid cells with weight values ​​higher than a threshold as core nodes based on a dynamic weight matrix, specifically: An improved hierarchical clustering algorithm is used to cluster core nodes according to spatial proximity and weight similarity to form the basic skeleton structure of the business district, which is composed of the core business district area and the main connection paths. Each time a hierarchical clustering is completed, the silhouette coefficient of the current clustering result is calculated. When the coefficient is less than the coefficient threshold, the number of clusters is automatically increased by the specified amplitude and recalculated; Under the constraints of the skeleton structure, the radiation distance of the core nodes is set. When it is detected that the distance between two core nodes exceeds the radiation distance, an independent sub-business district is automatically generated with the middle point of the two core nodes as the dividing line, triggering the construction of the secondary skeleton; Based on the secondary skeleton construction, the secondary commercial agglomeration area is identified through the density clustering algorithm to form a detailed boundary, traverse all grid cells, calculate the core point attributes of each cell, further identify the core nodes, merge the identified core points and neighboring cells to form a detailed boundary area; At the same time, for the connection paths connecting the core nodes, the actual travel time is obtained by obtaining GIS. When the actual time is greater than twice the theoretical time, a virtual node is inserted to refine the detail boundary; Integrate the core business district area formed by the basic skeleton structure and the secondary business area formed by the secondary skeleton into an initial business district scope, and further repair and optimize the boundaries.

5. The method for identifying business district boundaries based on spatial data according to claim 4, characterized in that: The boundary repair and optimization includes repairing boundary breaks caused by missing data through spatial adjacency connectivity constraints; The adjacency connectivity constraint is that each boundary area must be directly adjacent to at least two core nodes; When the boundary area is connected to only one core node, the connection path is completed through the path search algorithm; Detect broken boundaries, mark all isolated areas that do not meet the adjacent connectivity constraints, generate a minimum spanning tree based on the road network data, and complete the missing connections between core nodes; A buffer zone is set for the patched boundary generation, and a patch record database is established to record the parameters and effect evaluation indicators of each patching operation.

6. The method for identifying business district boundaries based on spatial data according to claim 5, characterized in that: The boundary elastic adjustment includes real-time monitoring of the changing trend of the flow of people and the updating of commercial facilities, making boundary elastic adjustments, and calculating the boundary adjustment range. ; Dynamically modify the business district boundary according to the adjustment range. When the first modification threshold is less than When the value is less than or equal to the second correction threshold, only the boundary point coordinates are adjusted, and the maximum offset distance does not exceed the extended threshold range of the original boundary; When the second correction threshold < ≤ the third correction threshold, press Direction adjustment boundary, amplitude ; when >When the third correction threshold is reached, the early warning mode is triggered, the automatic correction is frozen, the manual review is triggered and the regional reconstruction is started.

7. The method for identifying business district boundaries based on spatial data according to claim 6, characterized in that: The abnormal warning mechanism includes establishing an abnormal warning mechanism, when the third correction threshold is less than When the value is less than or equal to the fourth correction threshold, the region is reconstructed, the skeleton structure is kept unchanged, the density clustering algorithm is re-executed to identify the secondary commercial agglomeration area, the detailed boundary is formed, and manual review and correction are performed; when >When the fourth correction threshold is reached, the original skeleton structure is cancelled, triggering the re-identification of the initial boundary range of the core commercial cluster area.

8. A system using the method for identifying business district boundaries based on spatial data as claimed in any one of claims 1 to 7, characterized in that: It includes data adjustment module, boundary generation module and self-correction module; The data adjustment module collects and processes raw data and adjusts feature weights according to the time dimension correction factor; The boundary generation module constructs the basic skeleton structure of the business district based on the feature matrix and uses a density clustering algorithm to fill and repair the boundary; The self-correction module dynamically corrects and optimizes the business district boundary provided by the boundary generation module, performs boundary elastic adjustment, establishes an abnormal warning mechanism, and triggers manual review and area reconstruction.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for identifying business district boundaries based on spatial data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for identifying business district boundaries based on spatial data as described in any one of claims 1 to 7 are implemented.

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