Method and device for constructing multi-dimensional fine-grained portrait model of land use unit and medium

Abstract

The invention discloses a method and device for constructing a multi-dimensional fine-grained portrait model of a land use unit and a medium, and relates to the field of portrait model construction, and the method comprises the steps: constructing road network data according to multi-source data of a research region; obtaining a shortest path set between each public service facility and each residential cell in the research area according to the road network data; obtaining a target reachable range of each public service facility according to the shortest path set; according to the target reachable range of each public service facility, judging whether the research area is an efficient land use unit or not; and constructing a multi-dimensional fine-grained portrait model of the efficient land use unit. According to the method, the multi-dimensional fine-grained features of the efficient land use unit can be excavated, so that the multi-dimensional fine-grained portrait model is established, and the model is further used for guiding land space planning to carry out reasonable layout.

Description

Technical Field

[0001] The present application relates to the field of land use unit portrait construction, and in particular to a method, device and medium for constructing a multi-dimensional fine-grained portrait model of a land use unit. Background Art

[0002] The rational layout of national land space planning involves interactive multimodal complex information such as surrounding population distribution and activities, facility status, and traffic conditions. These complex information have different impacts on the planning layout, and it is necessary to extract features and model rules for these information. Therefore, how to analyze and mine the regular features of land use units from these irregular and complex information is a key scientific issue in current research. Summary of the invention

[0003] The purpose of this application is to provide a method, device and medium for constructing a multidimensional fine-grained portrait model of land use units, which can mine the multidimensional fine-grained characteristics of efficient land use units, thereby establishing a multidimensional fine-grained portrait model, which is then used to guide the rational layout of national land space planning.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In a first aspect, the present application provides a method for constructing a multi-dimensional fine-grained portrait model of a land use unit, comprising the following steps.

[0006] The road network data is constructed based on the multi-source data of the study area; the multi-source data includes the geographic entity data, road network data and POI data in the study area.

[0007] A set of shortest paths between each public service facility and each residential community in the study area is obtained based on the road network data.

[0008] The target reachable range of each public service facility is obtained based on the shortest path set; the target reachable range refers to the area range that takes the public service facility as the starting point and meets a preset travel time or a preset travel distance.

[0009] Whether the study area is an efficient land use unit is determined based on the target reachable range of each public service facility.

[0010] Construct a multi-dimensional and fine-grained portrait model of efficient land use units.

[0011] In the second aspect, the present application provides a device for constructing a multi-dimensional fine-grained portrait model of a land use unit, comprising the following modules.

[0012] The road network data construction module is used to construct road network data based on multi-source data of the study area; the multi-source data includes geographic entity data, road network data and POI data in the study area.

[0013] The shortest path planning module is used to obtain a set of shortest paths between each public service facility and each residential community in the study area based on the road network data.

[0014] The reachable range determination module is used to derive the target reachable range of each public service facility based on the shortest path set; the target reachable range refers to the area range that takes the public service facility as the starting point and meets the preset travel time or preset travel distance.

[0015] The high-efficiency land use unit judgment module is used to judge whether the study area is a high-efficiency land use unit according to the target reachable range of each public service facility.

[0016] The portrait construction module is used to construct a multi-dimensional fine-grained portrait model of efficient land use units.

[0017] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for constructing a multi-dimensional fine-grained portrait model of land use units.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for constructing a multi-dimensional fine-grained portrait model of land use units.

[0019] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0020] The present application provides a method, device and medium for constructing a multidimensional fine-grained portrait model of a land unit, constructing road network data based on multi-source data of the study area; obtaining a set of shortest paths between each public service facility and each residential area in the study area based on the road network data; obtaining a target reachable range of each public service facility based on the shortest path set; judging whether the study area is an efficient land unit based on the target reachable range of each public service facility; and constructing a multidimensional fine-grained portrait model of efficient land units. The present invention can accurately screen out efficient land units, and further mine the multidimensional fine-grained features of efficient land units, thereby establishing a multidimensional fine-grained portrait model, providing a learning basis for intelligent optimization and decision-making of planning layout, and is of great significance for realizing the reasonable layout of national land space planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 This is an application environment diagram of a method for constructing a multi-dimensional fine-grained portrait model of a land use unit in one embodiment of the present application.

[0023] Figure 2 A flowchart of a method for constructing a multi-dimensional fine-grained portrait model of a land use unit provided in one embodiment of the present application.

[0024] Figure 3 A schematic diagram of the technical concept of a method for constructing a multi-dimensional fine-grained portrait model of land use units provided in one embodiment of the present application.

[0025] Figure 4 A schematic diagram of the functional modules of a device for constructing a multi-dimensional fine-grained portrait model of a land use unit provided in one embodiment of the present application.

[0026] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0029] The method for constructing a multi-dimensional fine-grained land use unit portrait model provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal can send multi-source data (including geographic entity data, road network data and POI data in the study area) to the server. After the server receives the multi-source data, the server constructs road network data based on the multi-source data of the study area; the shortest path set between each public service facility and each residential community in the study area is obtained based on the road network data; the target reachable range of each public service facility is obtained based on the shortest path set; whether the study area is an efficient land use unit is determined based on the target reachable range of each public service facility; and a multi-dimensional fine-grained portrait model of the efficient land use unit is constructed. The server can feed back the obtained multi-dimensional fine-grained portrait model of the efficient land use unit to the terminal. In addition, in some embodiments, the method for constructing a multidimensional fine-grained portrait model of a land use unit can also be implemented independently by a server or a terminal. For example, the terminal can directly construct a multidimensional fine-grained portrait model of an efficient land use unit based on multi-source data, or the server can obtain multi-source data from a data storage system to construct a multidimensional fine-grained portrait model of an efficient land use unit.

[0030] The terminal may be, but is not limited to, various desktop computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The server may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0031] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a method for constructing a multi-dimensional fine-grained portrait model of a land use unit is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server in is taken as an example to illustrate, including the following steps 101 to 105.

[0032] Step 101, constructing road network data based on multi-source data of a study area; the multi-source data includes geographic entity data, road network data and POI data in the study area.

[0033] The study area can be a community, or the area size can be customized. By integrating geographic entities, road networks, POIs and other data, semantic associations are made based on the spatial relationships between various geographic entities (such as houses, gates, and courtyards), and the association fusion between different entities is realized; the spatial topological relationships of broken roads, data redundancy, and repeated roads in the road data of the study area are identified and the corresponding spatial relationships are processed to form road network data that conforms to the actual situation; based on the spatial relationships and interactions between road attributes and the internal roads, overpasses, gates and other geographic entities in the study area in pedestrian and pedestrian traffic scenes, the relevant attributes of the road data (such as road one-way restrictions, turn settings, etc.) are supplemented, and the road data is topologically checked to create a refined road network dataset containing road attributes, covering urban trunk roads to community gates and courtyards.

[0034] Step 102: derive a set of shortest paths between each public service facility and each residential community in the study area based on the road network data.

[0035] As an example, taking public service facilities as the starting point and residential areas as the end point, the travel mode, age, travel time and travel distance are set as impedance conditions, and the route planning algorithm is used to perform hierarchical compression of road network data, find the capture point closest to the specified location, and generate the shortest path set.

[0036] Step 103, deriving a target reachable range of each public service facility according to the shortest path set; the target reachable range refers to an area range starting from the public service facility that meets a preset travel time or a preset travel distance.

[0037] Based on the generated shortest path set, the end nodes of the reachable paths are calculated according to the set impedance conditions such as distance or time, and then the travel isochronous surface / equidistant surface of the public service facilities (that is, the reachable range of the public service facilities) are calculated.

[0038] Step 104 , judging whether the study area is an efficient land use unit according to the target reachable range of each public service facility.

[0039] Step 105, construct a multi-dimensional fine-grained portrait model of the efficient land use unit.

[0040] By implementing the above-mentioned steps 101 to 105, the present invention will comprehensively consider the role of urban road network data and courtyards in residents' walking travel, integrate basic geographic entity data, road network data and POI data, construct a refined measurement model of the reachable range of the real road network, evaluate and analyze the spatial distribution characteristics of public service facilities and the coverage index of public service facilities, screen out high-efficiency land use units based on the evaluation and analysis results, characterize the land use unit portrait from multiple dimensions such as population distribution, social function, and spatial form, thereby realizing the regular feature learning of land use units, which has important practical significance and application prospects.

[0041] In another exemplary embodiment of the present application, for step 104, with reference to the community life circle livable index setting standards in the "National Space Planning Urban Physical Examination and Evaluation Charter", public service facilities are divided into 8 dimensions (medical services, education services, transportation services, cultural and sports entertainment, leisure services, elderly care services, government services, and life services), and the coverage index of each type of public service facilities is calculated based on the spatial relationship between the target reachable range of public service facilities and the scope of the study area. On the basis of the coverage index of a single type of public service facility, the coverage index of the eight dimensions of public service facilities in the study area is weighted to obtain the total coverage index of public service facilities in the study area.

[0042] Therefore, it is concluded that step 104, judging whether the study area is an efficient land use unit according to the target reachable range of each public service facility, specifically includes the following steps.

[0043] (1) Calculate the total reach of public service facilities of the same type based on the target reach of each public service facility.

[0044] (2) Calculate the coverage index of each type of public service facility based on the total reach of each type of public service facility.

[0045] Among them, the calculation expression of the coverage index of each type of public service facilities is as follows.

[0046] .

[0047] In the formula, It represents the coverage index of a certain type of public service facilities in the study area i; It is the area of ​​the intersection between the total reach of a certain type of public service facilities in the study area i and the residential area; is the total area of ​​residential areas in study area i.

[0048] (3) Calculate the total coverage index of various types of public service facilities in the study area based on the coverage index of each type of public service facility.

[0049] Among them, the calculation expression of the total coverage index is as follows.

[0050] .

[0051] In the formula, is the total coverage index of various public service facilities in the study area, For the The weight of public service facilities, For the The coverage index of public service facilities, It is the number of type dimensions of public service facilities.

[0052] (4) Based on the total coverage index and the distribution of various public service facilities in the study area, determine whether the study area is an efficient land use unit.

[0053] Communities with all kinds of public service facilities that meet the standards of the "15-minute living circle" and a total coverage index greater than 70% are selected as efficient land use units. The public service facilities that meet the standards are defined as: all kinds of basic public service facilities are distributed within the reach of the "15-minute living circle" of urban residents, which means they meet the standards, and if there are missing types of facilities, they do not meet the standards. The specific rules are shown in Table 1.

[0054] Table 1 Definition of various facilities that meet the standards of the “15-minute living circle”

[0055] In another exemplary embodiment of the present application, for step 105, multiple types of geographic big data such as buildings, road networks, mobile phone signaling, real estate registration, housing price information, POI, etc. are obtained according to the land use unit as the spatial scope as the data source for depicting the portraits of different types of land use units. Among them, the mobile phone signaling data comes from China Unicom mobile operator, which is used to depict the actual number of service population in the surrounding area, population activities and commuting information of the land use unit; building data, road network data, and real estate registration data are all derived from the natural resources planning departments of each city, which are used to calculate and describe the building height, road network density, number of real estate households and other status information around the land use unit; housing price information data comes from Lianjia second-hand housing transaction official website, which is used to describe the surrounding market value information; POI data comes from Gaode map, which is used to describe the distribution of facilities around the land use unit. Indicators for depicting the portrait of the land use unit are calculated from multi-dimensional perspectives such as population distribution, spatial form, and social function, and a basic portrait of the land use unit is constructed.

[0056] Therefore, it is concluded that in step 105, a multi-dimensional fine-grained portrait model of the efficient land use unit is constructed, which specifically includes the following steps.

[0057] (1) Calculate characteristic indicators that depict the portrait of efficient land use units based on multi-dimensional information; the multi-dimensional information includes dimensional information on population distribution, spatial form, social function, medical services, educational services, cultural and sports entertainment, leisure services, elderly care services, and life services. The characteristic indicators of land use units are shown in Table 2.

[0058] Table 2 Land use unit characteristic index table

[0059]

[0060] (2) Construct the multi-dimensional fine-grained portrait model according to the characteristic indicators. The portrait model is shown in the following formula.

[0061] .

[0062] in, represents the actual number of people served, Indicates the commuting conditions within and around the unit. represents the average height of the building, represents the average distance between residential areas and public transportation stations. represents the road network density, represents the average house price, Indicates the coverage rate of community health service facilities within 15 minutes’ walk. Indicates the coverage rate of community primary schools within 10 minutes’ walk. Indicates the coverage rate of community middle schools within 15 minutes’ walk (%), Indicates the coverage rate of parks, green spaces and squares within 5 minutes of walking (%), Indicates the coverage rate of community elderly care facilities within 5 minutes’ walk (%), It indicates the coverage rate (%) of vegetable markets (fresh food supermarkets) within 10 minutes’ walk.

[0063] In this embodiment, multi-source big data such as geographic entity data, road network data, POI data, etc. are integrated to construct refined road network data, and a route planning algorithm is used to generate a set of shortest paths with public service facilities as the starting point and residential communities as the end point. The travel reachable range of public service facilities in the study area is calculated based on the shortest path set and the set impedance conditions; then the public service facilities are divided into eight dimensions, and the coverage index of each type of public service facilities and the total coverage index of various types of public service facilities are calculated; finally, based on the total coverage index and the distribution of various types of public service facilities in the study area, a judgment is made as to whether the study area is an efficient land use unit, and multiple types of geographic big data such as buildings, mobile phone signaling, real estate registration, and housing price information are obtained according to the land use unit as the spatial scope, and indicators for describing the land use unit portrait are calculated from multi-dimensional perspectives such as population distribution, spatial form, and social function, to construct a multi-dimensional fine-grained portrait model of efficient land use units.

[0064] The present application also provides an application scenario, which applies the above-mentioned method for constructing a multi-dimensional fine-grained portrait model of land use units. Specifically: The method for constructing a multi-dimensional fine-grained portrait model of land use units provided in this embodiment can be applied to the intelligent decision-making model scenario of land use layout in national space planning. The scenario includes three links: learning the characteristic rules of planned land layout, building a "characteristic spectrum" of planned land, and intelligent optimization decision-making of planning layout. The method of this embodiment belongs to the first link. By characterizing the multi-dimensional fine-grained portrait model of planned land units, it breaks through the learning of reasonable scale and structural rules for efficient planning land unit layout, provides node attribute information of graph structure for the second link, and provides decision-making strategy rules for the third link.

[0065] Based on the same inventive concept, the embodiment of the present application also provides a device for constructing a multi-dimensional fine-grained portrait model of a land unit for implementing the method for constructing a multi-dimensional fine-grained portrait model of a land unit involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the device for constructing a multi-dimensional fine-grained portrait model of a land unit provided below can be referred to the limitations of the method for constructing a multi-dimensional fine-grained portrait model of a land unit above, and will not be repeated here.

[0066] In an exemplary embodiment, Figure 4 As shown, a device for constructing a multi-dimensional fine-grained portrait model of a land use unit is provided, comprising the following modules.

[0067] The road network data construction module M1 is used to construct road network data based on multi-source data of the study area; the multi-source data includes geographic entity data, road network data and POI data in the study area.

[0068] The shortest path planning module M2 is used to obtain a set of shortest paths between each public service facility and each residential community in the study area based on the road network data.

[0069] The reachable range determination module M3 is used to obtain the target reachable range of each public service facility based on the shortest path set; the target reachable range refers to the area range starting from the public service facility that meets the preset travel time or preset travel distance.

[0070] The high-efficiency land use unit determination module M4 is used to determine whether the study area is a high-efficiency land use unit according to the target reachable range of each public service facility.

[0071] The portrait construction module M5 is used to construct a multi-dimensional fine-grained portrait model of efficient land use units.

[0072] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store a multi-dimensional fine-grained portrait model of an efficient land unit. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for constructing a multi-dimensional fine-grained portrait model of a land unit is implemented.

[0073] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0074] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0075] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0076] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0077] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0078] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for constructing a multi-dimensional fine-grained portrait model of a land use unit, characterized in that: The method for constructing a multi-dimensional fine-grained portrait model of a land use unit includes: Constructing road network data based on multi-source data of the study area; the multi-source data includes geographic entity data, road network data and POI data in the study area; Determine the shortest path set between each public service facility and each residential community in the study area according to the road network data; A target reachable range of each public service facility is obtained according to the shortest path set; the target reachable range refers to an area range that takes the public service facility as a starting point and satisfies a preset travel time or a preset travel distance; Determine whether the study area is an efficient land use unit according to the target reachable range of each public service facility; Construct a multi-dimensional and fine-grained portrait model of efficient land use units.

2. The method for constructing a multi-dimensional fine-grained portrait model of a land use unit according to claim 1, characterized in that: Whether the study area is an efficient land use unit is determined based on the target reach of each public service facility, including: Calculate the total reach of public service facilities of the same category based on the target reach of each public service facility; Calculate the coverage index of each type of public service facility based on the total reach of each type of public service facility; Calculate the total coverage index of various types of public service facilities in the study area based on the coverage index of each type of public service facilities; Based on the total coverage index and the distribution of various public service facilities in the study area, it is judged whether the study area is an efficient land use unit.

3. The method for constructing a multi-dimensional fine-grained portrait model of a land use unit according to claim 2, characterized in that: The calculation expression of the coverage index of each type of public service facilities is: ； In the formula, It represents the coverage index of a certain type of public service facilities in the study area i; It is the area of ​​the intersection between the total reach of a certain type of public service facilities in the study area i and the residential area; is the total area of ​​residential areas in study area i.

4. The method for constructing a multi-dimensional fine-grained portrait model of a land use unit according to claim 2, characterized in that: The expression of the total coverage index is: ； In the formula, is the total coverage index of various public service facilities in the study area, For the The weight of public service facilities, is the coverage index of the mth type of public service facilities, It is the number of type dimensions of public service facilities.

5. The method for constructing a multi-dimensional fine-grained portrait model of a land use unit according to claim 1, characterized in that: Construct a multi-dimensional fine-grained portrait model of efficient land use units, including: Calculate characteristic indicators that depict the portrait of efficient land use units based on multi-dimensional information; the multi-dimensional information includes dimensional information on population distribution, spatial form, social functions, medical services, educational services, cultural and sports entertainment, leisure services, elderly care services, and life services; The multi-dimensional fine-grained portrait model is constructed according to the characteristic indicators.

6. The method for constructing a multi-dimensional fine-grained portrait model of a land use unit according to claim 5, characterized in that: The characteristic indicators include: the actual number of people served, commuting conditions within and around the unit within a preset distance, the average height of buildings, the average distance between residential areas and various public transportation stops, road network density, average housing prices, coverage rate of the preset walking time for health service facilities, coverage rate of the preset walking time for primary schools, coverage rate of the preset walking time for middle schools, coverage rate of the preset walking time for parks, green spaces and squares, and coverage rate of the preset walking time for elderly care facilities.

7. A device for constructing a multi-dimensional fine-grained portrait model of a land use unit, characterized in that: The land use unit multi-dimensional fine-grained portrait model construction device comprises: A road network data construction module is used to construct road network data based on multi-source data of the study area; the multi-source data includes geographic entity data, road network data and POI data in the study area; A shortest path planning module, used to obtain a set of shortest paths between each public service facility and each residential community in the study area according to the road network data; A reachable range determination module, used to obtain a target reachable range of each public service facility according to the shortest path set; the target reachable range refers to an area range that takes the public service facility as a starting point and meets a preset travel time or a preset travel distance; An efficient land use unit determination module, used to determine whether the study area is an efficient land use unit according to the target reachable range of each public service facility; The portrait construction module is used to construct a multi-dimensional fine-grained portrait model of efficient land use units.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for constructing a multi-dimensional fine-grained portrait model of a land use unit as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method for constructing a multi-dimensional fine-grained portrait model of a land use unit as described in any one of claims 1-6.

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