Urban low-efficiency land identification method, device and equipment based on multi-source data and storage medium

Abstract

The invention discloses an urban inefficient land identification method and device based on multi-source data, equipment and a storage medium. The method comprises the following steps: acquiring mobile phone signaling data in a target area within a preset duration; carrying out clustering analysis on the mobile phone signaling data to obtain urban low-efficiency land use candidate areas; acquiring urban land use management data and urban planned land use data corresponding to the urban low-efficiency land use candidate areas; based on the urban low-efficiency land use candidate areas, the urban land use management data and the urban planning land use data, obtaining urban low-efficiency land use map spots; performing cutting operation and filling operation on the urban low-efficiency land map spots to obtain low-efficiency land use areas of different land use types; and generating an urban low-efficiency land identification result based on the obtained low-efficiency land areas of different land types. According to the invention, efficient and accurate identification of urban inefficient land can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of land resource management, and particularly relates to a method, device, equipment and storage medium for identifying urban inefficient land based on multi-source data. Background Art

[0002] In the process of urban development, inefficient land, as the "abandoned" area formed in urban construction, widely exists in every corner of the city. These areas not only affect the overall image of the city, but also seriously restrict the efficient use of land resources. At present, large-scale urban renewal work in the old city area is carried out, and it is urgent to conduct a comprehensive and accurate investigation and analysis of low-utilization areas.

[0003] For the investigation and analysis of inefficient land, the traditional investigation method is the manual investigation method, such as the community unit actively reporting, the management department conducting on-site inspection and verification, etc. This method has many drawbacks. On the one hand, it consumes a large amount of human, material and time costs, and the efficiency is low; on the other hand, due to human negligence or geographical restrictions, it is difficult to achieve full coverage, and it is easy to miss important inefficient land information, making it difficult to meet the needs of urban refined management.

[0004] With the rapid development of information technology, it has become more convenient to obtain multi-source data such as mobile phone signaling data, urban land management data, and planned land data. How to effectively utilize these data to achieve efficient and accurate identification of urban inefficient land has become an urgent problem to be solved. Summary of the Invention

[0005] The present application aims to provide a method, device, equipment and storage medium for identifying urban inefficient land based on multi-source data, overcome the defects of traditional investigation methods, improve the refinement degree of land management, and provide a scientific basis for urban renewal and rational utilization of land resources.

[0006] The first object of the present application is to provide a method for identifying urban inefficient land based on multi-source data.

[0007] The above object of the present application is achieved through the following technical solutions:

[0008] A method for identifying urban inefficient land based on multi-source data, the method includes the following steps:

[0009] S1, obtaining mobile phone signaling data within a target area within a preset time period, wherein the mobile phone signaling data at least includes user location information and a timestamp;

[0010] S2, performing cluster analysis on the mobile phone signaling data to obtain candidate areas of urban inefficient land;

[0011] S3. Obtain the urban land management data and urban planned land data corresponding to the candidate areas of urban inefficient land use.

[0012] S4. Obtain urban inefficient land use patches based on the candidate areas of urban inefficient land use, urban land management data, and urban planned land data.

[0013] S5. Perform cutting and filling operations on the urban inefficient land use patches to obtain inefficient land use areas of different land use types.

[0014] S6. Generate an identification result of urban inefficient land use based on the obtained inefficient land use areas of different land use types.

[0015] Preferably, in step S2, the clustering analysis of the mobile phone signaling data to obtain candidate areas of urban inefficient land use includes:

[0016] S21. Import the mobile phone signaling data into a preset data analysis software.

[0017] S22. Use a preset clustering algorithm to perform clustering analysis on the mobile phone signaling data imported into the data analysis software to obtain clustering areas with different signaling densities.

[0018] S23. Screen out the clustering areas with signaling density lower than a preset density threshold in the clustering areas with different signaling densities as the candidate areas of urban inefficient land use.

[0019] Preferably, in step S22, the use of a preset clustering algorithm to perform clustering analysis on the mobile phone signaling data imported into the data analysis software to obtain clustering areas with different signaling densities includes:

[0020] Use the DBSCAN density clustering algorithm to perform clustering analysis on the mobile phone signaling data imported into the data analysis software according to the set neighborhood radius and minimum number of samples, and divide the density-connected points into the same clustering area to obtain clustering areas with different signaling densities.

[0021] Preferably, in step S4, the obtaining of urban inefficient land use patches based on the candidate areas of urban inefficient land use, urban land management data, and urban planned land data includes:

[0022] Overlay the candidate areas of urban inefficient land use, urban land management data, and urban planned land data in a geographic information system software to obtain urban inefficient land use patches.

[0023] Preferably, in step S5, the performing of cutting and filling operations on the urban inefficient land use patches to obtain inefficient land use areas of different land use types includes:

[0024] S51. According to the boundary lines of the areas of each land use type in the urban land use management data, use the cutting tool in the geographic information system software to cut the urban low - efficiency map patches to obtain low - efficiency map patches of different land use types;

[0025] S52. Reasonably fill the patches with data - missing areas among the patches of the low - efficiency map patches of different land use types obtained by cutting to obtain the low - efficiency areas of different land use types.

[0026] Preferably, in step S52, the reasonably filling the patches with data - missing areas among the patches of the low - efficiency map patches of different land use types obtained by cutting includes:

[0027] For the patches with data - missing areas among the patches of the low - efficiency map patches of different land use types obtained by cutting, refer to the surrounding data characteristics of the data - missing areas of the corresponding patches, and combine the urban planned land use data to reasonably fill the data - missing areas by using spatial interpolation method or manual drawing.

[0028] Preferably, in step S6, the generating the urban low - efficiency land identification result based on the obtained low - efficiency areas of different land use types includes:

[0029] Generate a thematic map containing various types of low - efficiency land in the geographic information system software based on the obtained low - efficiency areas of different land use types, and export a low - efficiency land data report, where

[0030] In the thematic map, different types of low - efficiency land are marked with different colors or symbols, and the low - efficiency land data report includes the position coordinates, area, and land use type of the low - efficiency areas.

[0031] The second object of the present application is to provide an identification device for urban low - efficiency land based on multi - source data.

[0032] The above - mentioned second application object of the present application is achieved by the following technical solutions:

[0033] An identification device for urban low - efficiency land based on multi - source data includes:

[0034] A signaling data acquisition module, configured to acquire mobile phone signaling data within a target area within a preset time period, where the mobile phone signaling data at least includes user location information and time stamps;

[0035] A data clustering and analysis module, configured to perform clustering analysis on the mobile phone signaling data to obtain candidate areas of urban low - efficiency land;

[0036] The land use data acquisition module is used to acquire the urban land use management data and urban planned land use data corresponding to the candidate areas of urban inefficient land use;

[0037] The patch construction module is used to obtain urban inefficient land use patches based on the candidate areas of urban inefficient land use, urban land use management data and urban planned land use data;

[0038] The patch processing module is used to perform cutting operations and filling operations on the urban inefficient land use patches to obtain inefficient land use areas of different land use types;

[0039] The recognition result generation module is used to generate the recognition result of urban inefficient land use based on the obtained inefficient land use areas of different land use types.

[0040] Preferably, the data clustering analysis module includes:

[0041] The data import unit is used to import the mobile phone signaling data into a preset data analysis software;

[0042] The data analysis unit is used to perform clustering analysis on the mobile phone signaling data imported into the data analysis software by using a preset clustering algorithm to obtain clustering regions with different signaling densities;

[0043] The region screening unit is used to screen out the clustering regions with signaling density lower than the preset density threshold in the clustering regions with different signaling densities as the candidate areas of urban inefficient land use.

[0044] Preferably, when the data analysis unit performs the clustering analysis on the mobile phone signaling data imported into the data analysis software by using a preset clustering algorithm to obtain clustering regions with different signaling densities, it specifically uses:

[0045] Using the DBSCAN density clustering algorithm, clustering analysis is performed on the mobile phone signaling data imported into the data analysis software according to the set neighborhood radius and minimum number of samples, and the points with density connection are divided into the same clustering region to obtain clustering regions with different signaling densities.

[0046] Preferably, when the patch construction module obtains urban inefficient land use patches based on the candidate areas of urban inefficient land use, urban land use management data and urban planned land use data, it specifically uses:

[0047] Overlay the candidate areas of urban inefficient land use, urban land use management data and urban planned land use data in the geographic information system software to obtain urban inefficient land use patches.

[0048] Preferably, the patch processing module includes:

[0049] The patch cutting unit cuts the urban low - utility map patches according to the boundary lines of the regions of each land use type in the urban land management data, using the cutting tool in the geographic information system software to obtain low - utility map patches of different land use types;

[0050] The patch filling unit reasonably fills the patches with data - missing regions among the cut low - utility map patches of different land use types to obtain the low - utility regions of different land use types.

[0051] Preferably, when the patch filling unit performs the reasonable filling of the patches with data - missing regions among the cut low - utility map patches of different land use types, it is specifically used for:

[0052] For the patches with data - missing regions among the cut low - utility map patches of different land use types, referring to the surrounding data characteristics of the data - missing regions of the corresponding patches, and combining the urban planned land data, the data - missing regions are reasonably filled by using the spatial interpolation method or manual drawing.

[0053] Preferably, when the recognition result generation module generates the urban inefficient land recognition result based on the obtained low - utility regions of different land use types, it is specifically used for:

[0054] Generating a thematic map containing various types of inefficient land in the geographic information system software based on the obtained low - utility regions of different land use types, and exporting an inefficient land data report, where

[0055] In the thematic map, different types of inefficient land are marked with different colors or symbols, and the inefficient land data report includes the position coordinates, area, and land use type of the low - utility regions.

[0056] The third object of the present application is to provide an electronic device.

[0057] The above - mentioned third application object of the present application is achieved by the following technical solution:

[0058] An electronic device includes:

[0059] A memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for identifying urban inefficient land based on multi - source data described in any one of the first objects of the present application.

[0060] The fourth object of the present application is to provide a computer - readable storage medium.

[0061] The fourth above-mentioned application objective of this application is achieved through the following technical solutions:

[0062] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the method for identifying urban inefficient land based on multi-source data described in any one of the first objectives of this application above.

[0063] This application has the following beneficial effects compared with the prior art:

[0064] 1. Improve the identification efficiency: Compared with the traditional manual investigation method, this application uses multi-source data and automated algorithms for processing, greatly shortening the investigation time, improving work efficiency, and being able to quickly obtain the distribution information of urban inefficient land;

[0065] 2. Enhance the identification accuracy: Through the fusion and fine processing of multi-source data, it can comprehensively and accurately identify inefficient land in various types of land, avoiding omissions and errors in manual investigations, and improving the refinement level of land management;

[0066] 3. Provide a scientific basis for decision-making: The accurate identification results of inefficient land provide reliable data support for decision-making such as urban renewal and land resource integration, helping to optimize the urban spatial layout, achieve the efficient use of land resources, and the sustainable development of the city. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0068] Figure 1 It is a schematic flowchart of a method for identifying urban inefficient land based on multi-source data in an embodiment of this application;

[0069] Figure 2 It is a schematic structural diagram of an apparatus for identifying urban inefficient land based on multi-source data in an embodiment of this application;

[0070] Figure 3 It is a schematic structural diagram of an electronic device in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0072] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. The system embodiments described below are only illustrative. For example, the division of units and modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or modules can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces, indirect coupling or communication connection of devices or modules, and can be electrical, mechanical, or other forms.

[0073] In addition, each functional unit in the embodiments of this application can be all integrated in a processor, or each unit can be separately used as a device alone, or two or more units can be integrated in a device; each functional unit in the embodiments of this application can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0074] Those of ordinary skill in the art can understand that all or part of the steps of implementing the following method embodiments can be completed through program instructions and related hardware. The foregoing program instructions can be stored in a computer-readable storage medium. When the program instructions are executed, the steps of the following method embodiments are executed; and the foregoing storage medium includes: various media that can store program codes such as mobile storage devices, read-only memories (ROMs), magnetic disks, or optical discs.

[0075] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include one or more of such features. In the description of this application, the meaning of "multiple" and "several" is two or more, unless otherwise clearly and specifically defined.

[0076] As Figure 1 shown, the embodiments of this application provide a method for identifying urban inefficient land based on multi-source data. The method may include the following steps:

[0077] S1. Obtain mobile phone signaling data within a preset time period in the target area, where the mobile phone signaling data at least includes user location information and timestamps.

[0078] To overcome the deficiencies of traditional survey methods for inefficient land use, the method for identifying urban inefficient land based on multi-source data in the embodiments of the present application uses multi-source data such as mobile phone signaling data, urban land management data, and urban planned land data to automatically identify urban inefficient land, so as to improve the efficiency and accuracy of urban inefficient land identification, thereby improving the refinement level of land management and providing a scientific basis for urban renewal and rational utilization of land resources.

[0079] When using the method of the present application to identify inefficient land in the target city, it is first necessary to obtain mobile phone signaling data within a preset time period in the target area.

[0080] Specifically, it is possible to cooperate with a communication operator to obtain the required mobile phone signaling data from the communication operator. The target city can be one city or multiple cities, and the target area can be the entire area of the target city or a part of the target city. The preset time period can be set as needed, for example, set to 1 month.

[0081] Specifically, the obtained mobile phone signaling data at least includes user location information and timestamps, and the user location information and timestamps can represent the specific locations where the user has been at a certain time or time period. Since there is generally little human activity in inefficient land, and now mobile phones are extremely common and an indispensable personal item in people's lives, it is possible to conveniently and accurately determine whether some areas are inefficient land through the situation of mobile phone signaling data within a preset time period in the target area.

[0082] To ensure user privacy and security, after obtaining the user's mobile phone signaling data, the mobile phone signaling data can be first desensitized.

[0083] S2. Perform cluster analysis on the mobile phone signaling data to obtain candidate areas for urban inefficient land.

[0084] After obtaining the mobile phone signaling data within a preset time period in the target area, further perform cluster analysis on the mobile phone signaling data, so as to obtain candidate areas for urban inefficient land.

[0085] S3. Obtain the urban land management data and urban planned land data corresponding to the candidate areas for urban inefficient land.

[0086] To further refine the land use types of the candidate areas for urban inefficient land obtained by cluster analysis, further obtain the urban land management data and urban planned land data corresponding to the candidate areas for urban inefficient land.

[0087] Specifically, the latest urban land management data and urban planning land data can be obtained from the urban land management department and the urban planning department respectively. The urban land management data details the actual usage of various types of land at present, while the urban planning land data reflects the future land use planning layout of the city.

[0088] To facilitate subsequent data analysis, the obtained urban land management data and urban planning land data can be converted into a data format compatible with the mobile signaling data processing software, such as the data format of common GIS system software (.shp, etc.).

[0089] S4. Obtain urban low - efficiency land patches based on the candidate areas of urban low - efficiency land, urban land management data, and urban planning land data;

[0090] Then, effectively combine the candidate areas of urban low - efficiency land obtained through cluster analysis with the obtained urban land management data and urban planning land data, so as to obtain the urban low - efficiency land patches corresponding to the candidate areas of urban low - efficiency land.

[0091] S5. Perform cutting operations and filling operations on the urban low - efficiency land patches to obtain low - efficiency land areas of different land use types;

[0092] Then, by cutting the urban low - efficiency land patches, accurately divide the low - efficiency land areas of different land use types in the urban low - efficiency land patches. Through filling processing of the divided patches, ensure the integrity and accuracy of the patches.

[0093] S6. Generate the identification results of urban low - efficiency land based on the obtained low - efficiency land areas of different land use types.

[0094] Finally, analyze and process the low - efficiency land areas of different land use types according to specific needs, so as to output the corresponding identification results of low - efficiency land.

[0095] In summary, the method for identifying urban low - efficiency land based on multi - source data in the above - mentioned embodiments first obtains mobile signaling data within a preset time period in the target area, then performs cluster analysis on the mobile signaling data to obtain candidate areas of urban low - efficiency land, then obtains the urban land management data and urban planning land data corresponding to the candidate areas of urban low - efficiency land. Then, obtain urban low - efficiency land patches based on the candidate areas of urban low - efficiency land, urban land management data, and urban planning land data. Next, perform cutting operations and filling operations on the urban low - efficiency land patches to obtain low - efficiency land areas of different land use types. Finally, generate the identification results of urban low - efficiency land based on the obtained low - efficiency land areas of different land use types.

[0096] Compared with the traditional manual investigation method, the embodiments of the present application utilize multi-source data and automated algorithms for processing, greatly shortening the investigation time, improving work efficiency, and enabling rapid acquisition of the distribution information of urban inefficient land; through the fusion and refined processing of multi-source data, it is possible to comprehensively and accurately identify inefficient land in various types of land, avoiding omissions and errors in manual investigations, and improving the refinement level of land management; the accurate identification results of inefficient land provide reliable data support for decisions such as urban renewal and land resource integration, contributing to optimizing the urban spatial layout, achieving the efficient utilization of land resources, and the sustainable development of the city.

[0097] In one embodiment, in step S2, the clustering analysis of mobile phone signaling data to obtain candidate areas of urban inefficient land includes:

[0098] S21, import the mobile phone signaling data into a preset data analysis software;

[0099] Specifically, the data analysis software can adopt relevant data analysis libraries of Python (such as TransBigData data analysis library, pandas data analysis library, python-phonenumbers data analysis library, etc.) or professional GIS analysis software.

[0100] S22, use a preset clustering algorithm to perform clustering analysis on the mobile phone signaling data imported into the data analysis software to obtain clustering areas with different signaling densities;

[0101] In this embodiment, the specific method for performing clustering analysis on the mobile phone signaling data imported into the data analysis software using a preset clustering algorithm is as follows:

[0102] Adopt the DBSCAN density clustering algorithm to perform clustering analysis on the mobile phone signaling data imported into the data analysis software according to the set neighborhood radius and minimum number of samples, and divide the density-connected points into the same clustering area to obtain clustering areas with different signaling densities.

[0103] Specifically, the neighborhood radius and minimum number of samples can be set according to the actual scale and population distribution of the city.

[0104] S23, screen out the clustering areas with signaling density lower than the preset density threshold in the clustering areas with different signaling densities as candidate areas of urban inefficient land.

[0105] Since the clustering areas with signaling density lower than the preset density threshold are very likely to be low-activity areas in the city and have a high correlation with inefficient land, therefore, in this embodiment, the clustering areas with signaling density lower than the preset density threshold are screened out in the clustering areas with different signaling densities as candidate areas of urban inefficient land.

[0106] Specifically, the preset density threshold is set according to needs, and the optimal density threshold can be selected through multiple automatic analyses in cooperation with on-site manual inspections.

[0107] In one embodiment, in step S4, obtaining the urban low-utility land patches based on the candidate areas of urban inefficient land, urban land management data, and urban planned land data includes:

[0108] Overlay the candidate areas of urban inefficient land, urban land management data, and urban planned land data in geographic information system software to obtain urban low-utility land patches.

[0109] In one embodiment, in step S5, performing cutting operations and filling operations on the urban low-utility land patches to obtain low-utility areas of different land use types includes:

[0110] S51, according to the boundary lines of the areas of each land use type in the urban land management data, use the cutting tool in the geographic information system software to cut the urban low-utility land patches to obtain low-utility land patches of different land use types;

[0111] Since some or all of the patches in the urban low-utility land patches may span lands of multiple land use types, therefore, through the above patch cutting method, the land use types i included in each patch can be accurately cut, improving the refinement degree of land management.

[0112] S52, reasonably fill the patches with data missing areas in each of the low-utility land patches of different land use types obtained by cutting to obtain low-utility areas of different land use types.

[0113] In one embodiment, in step S52, reasonably filling the patches with data missing areas in each of the low-utility land patches of different land use types obtained by cutting includes:

[0114] For the patches with data missing areas in each of the low-utility land patches of different land use types obtained by cutting, refer to the surrounding data characteristics of the data missing areas of the corresponding patches, combine with the urban planned land data, and use spatial interpolation method or manual drawing method to reasonably fill the data missing areas.

[0115] For example, if there is a data missing area in a patch, and it is known from the urban land management data that this area is a park green space, then the shape, area, etc. of the green spaces around this patch can be referred to, combined with the planning of this park green space in the planned land data, and the spatial interpolation algorithm or manual drawing method can be used to reasonably fill the missing area to ensure the integrity and accuracy of the patch.

[0116] In one embodiment, in step S6, generating the urban inefficient land identification result based on the obtained low-utility areas of different land use types includes:

[0117] Generating a thematic map containing various types of inefficient land in geographic information system software based on the obtained low-utility areas of different land use types, and exporting an inefficient land data report, where

[0118] In the thematic map, different types of inefficient land are marked with different colors or symbols, and the inefficient land data report includes the location coordinates, area, and land use type of the low-utility areas.

[0119] It should be noted that after obtaining the urban inefficient land identification result through the above method, in order to ensure the reliability of the identification result, some of the identified inefficient land can be selected for on-site verification, comparing the on-site situation with the identification result, checking for misjudgment or missed judgment, and fine-tuning and optimizing the parameter settings (such as density threshold, neighborhood radius, minimum sample number, etc.) in the above identification method of the present application according to the verification result.

[0120] As Figure 2 shown, an embodiment of the present application provides an identification device for urban inefficient land based on multi-source data, and the device may include:

[0121] A signaling data acquisition module 201, configured to acquire mobile phone signaling data within a target area during a preset time period, where the mobile phone signaling data at least includes user location information and a timestamp;

[0122] A data clustering and analysis module 202, configured to perform clustering analysis on the mobile phone signaling data to obtain candidate areas for urban inefficient land;

[0123] A land use data acquisition module 203, configured to acquire urban land management data and urban planned land use data corresponding to the candidate areas for urban inefficient land;

[0124] A map patch construction module 204, configured to obtain urban low-utility map patches based on the candidate areas for urban inefficient land, urban land management data, and urban planned land use data;

[0125] A map patch processing module 205, configured to perform cutting and filling operations on the urban low-utility map patches to obtain low-utility areas of different land use types;

[0126] An identification result generation module 206, configured to generate an urban inefficient land identification result based on the obtained low-utility areas of different land use types.

[0127] In one embodiment, the data clustering and analysis module 202 includes:

[0128] A data import unit for importing mobile signaling data into a preset data analysis software;

[0129] A data analysis unit for performing clustering analysis on the mobile signaling data imported into the data analysis software using a preset clustering algorithm to obtain clustering regions with different signaling densities;

[0130] A region screening unit for screening out clustering regions with signaling density lower than a preset density threshold from the clustering regions with different signaling densities as candidate regions for urban inefficient land use.

[0131] In one embodiment, when the data analysis unit performs clustering analysis on the mobile signaling data imported into the data analysis software using a preset clustering algorithm to obtain clustering regions with different signaling densities, it specifically is used for:

[0132] Using the DBSCAN density clustering algorithm, performing clustering analysis on the mobile signaling data imported into the data analysis software according to the set neighborhood radius and minimum number of samples, and dividing the density-connected points into the same clustering region to obtain clustering regions with different signaling densities;

[0133] In one embodiment, when the patch construction module 204 performs obtaining urban inefficient land use patches based on the candidate regions for urban inefficient land use, urban land management data, and urban planned land use data, it specifically is used for:

[0134] Overlaying the candidate regions for urban inefficient land use, urban land management data, and urban planned land use data in a geographic information system software to obtain urban inefficient land use patches.

[0135] In one embodiment, the patch processing module 205 includes:

[0136] A patch cutting unit, according to the boundary lines of the regions of each land use type in the urban land management data, using the cutting tool in the geographic information system software to cut the urban inefficient land use patches to obtain inefficient land use patches of different land use types;

[0137] A patch filling unit for reasonably filling the patches with data missing regions in each of the cut inefficient land use patches of different land use types to obtain inefficient land use regions of different land use types.

[0138] In one embodiment, when the patch filling unit performs reasonably filling the patches with data missing regions in each of the cut inefficient land use patches of different land use types, it specifically is used for:

[0139] For the patches with data missing areas among the patches of low-utility map patches of different land use types obtained by cutting, refer to the surrounding data characteristics of the data missing areas of the corresponding patches, and combine the urban planning land use data to reasonably fill the data missing areas by using spatial interpolation method or manual drawing method.

[0140] In one embodiment, when the recognition result generation module 206 executes to generate the urban inefficient land use recognition result based on the obtained low-utility areas of different land use types, it is specifically used for:

[0141] Generate a thematic map containing various types of inefficient land use in the geographic information system software based on the obtained low-utility areas of different land use types, and export the inefficient land use data report, where

[0142] In the thematic map, different types of inefficient land use are marked with different colors or symbols, and the inefficient land use data report includes the location coordinates, area, and land use type of the low-utility areas.

[0143] It should be noted that the recognition device for urban inefficient land use based on multi-source data in the above embodiments has the same working principle and technical effects as the recognition method for urban inefficient land use based on multi-source data in the above embodiments, and will not be elaborated here.

[0144] As Figure 3 shown, an embodiment of the present application provides an electronic device 3, the electronic device 3 includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and executable on the processor 302. Among them, the memory 301 and the processor 302 complete communication with each other through a bus 304, and when the processor 302 executes the computer program 303, it implements the steps of the recognition method for urban inefficient land use based on multi-source data as in the method embodiment of the present application above.

[0145] Specifically, the electronic device 3 may be an intelligent device such as an industrial control computer, a PC, or a smart mobile terminal with a memory and a processor, or a computer component such as a CPU or a GPU with a memory and a processor.

[0146] An embodiment of the present application also provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the recognition method for urban inefficient land use based on multi-source data as in the method embodiment of the present application above.

[0147] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0148] Those skilled in the art can further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0149] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0150] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying inefficient urban land based on multi-source data, characterized in that: The method comprises the following steps: S1, obtaining mobile phone signaling data within a target area within a preset time period, wherein the mobile phone signaling data at least includes user location information and a timestamp; S2, performing cluster analysis on the mobile phone signaling data to obtain candidate areas of urban inefficient land use; S3, obtaining urban land management data and urban planning land data corresponding to the candidate urban low-efficiency land area; S4, obtaining urban low-efficiency land use map patches based on the urban low-efficiency land use candidate areas, urban land use management data and urban planning land use data; S5, performing cutting and filling operations on the urban low-efficiency land use map patches to obtain low-efficiency land use areas of different land use types; S6, generating urban low-efficiency land identification results based on the obtained low-efficiency land areas of different land types.

2. The method for identifying inefficient urban land based on multi-source data according to claim 1 is characterized in that: In step S2, cluster analysis is performed on the mobile phone signaling data to obtain candidate areas of urban inefficient land use, including: S21, importing the mobile phone signaling data into a preset data analysis software; S22, using a preset clustering algorithm to perform cluster analysis on the mobile phone signaling data imported into the data analysis software to obtain clustering areas with different signaling densities; S23, selecting, from the clustering areas with different signaling densities, clustering areas with signaling densities lower than a preset density threshold as candidate areas for urban inefficient land use.

3. The method for identifying inefficient urban land based on multi-source data according to claim 2 is characterized in that: In step S22, the preset clustering algorithm is used to perform cluster analysis on the mobile phone signaling data imported into the data analysis software, and the clustering areas with different signaling densities include: The DBSCAN density clustering algorithm is used to perform cluster analysis on the mobile phone signaling data imported into the data analysis software according to the set neighborhood radius and minimum sample number, and the points connected by density are divided into the same clustering area to obtain clustering areas with different signaling densities.

4. The method for identifying inefficient urban land based on multi-source data according to claim 1, characterized in that: In step S4, obtaining the urban low-efficiency land map patches based on the urban low-efficiency land candidate areas, urban land management data and urban planning land data includes: The candidate urban low-efficiency land area, urban land management data and urban planning land data are superimposed in geographic information system software to obtain urban low-efficiency land map patches.

5. The method for identifying inefficient urban land based on multi-source data according to claim 1 is characterized in that: In step S5, the cutting operation and the filling operation are performed on the inefficient land use area of ​​the city to obtain inefficient land use areas of different land use types, including: S51, according to the boundary lines of the areas of each land use type in the urban land use management data, using the cutting tool in the geographic information system software to cut the low-efficiency land use patches of the city to obtain low-efficiency land use patches of different land use types; S52, reasonably filling the patches with data missing areas in the patches of inefficient land use of different land use types obtained by cutting, to obtain the inefficient land use areas of different land use types.

6. The method for identifying inefficient urban land based on multi-source data according to claim 5 is characterized in that: In step S52, the step of reasonably filling the patches with data missing areas in the patches of low-efficiency land use of different land use types obtained by cutting includes: For the patches with missing data in the inefficient land use patches of different land use types obtained by cutting, the data missing areas are reasonably filled in by using spatial interpolation or manual drawing with reference to the surrounding data features of the data missing areas of the corresponding patches and in combination with the urban planning land data.

7. The method for identifying low-efficiency urban land based on multi-source data according to any one of claims 1 to 6, characterized in that: In step S6, generating the urban low-efficiency land identification result based on the obtained low-efficiency land areas of different land types includes: Based on the obtained inefficient land use areas of different land use types, a thematic map containing various types of inefficient land use is generated in the geographic information system software, and an inefficient land use data report is exported, among which: In the thematic map, different types of inefficient land are marked with different colors or symbols, and the inefficient land data report includes the location coordinates, area and land type of the inefficient land area.

8. A device for identifying inefficient urban land based on multi-source data, characterized in that: include: A signaling data acquisition module, used to acquire mobile phone signaling data within a target area within a preset time period, wherein the mobile phone signaling data at least includes user location information and a timestamp; A data cluster analysis module, used to perform cluster analysis on the mobile phone signaling data to obtain candidate areas of urban inefficient land use; A land use data acquisition module, used to acquire urban land use management data and urban planning land use data corresponding to the candidate urban low-efficiency land use area; A patch construction module is used to obtain urban low-efficiency land patches based on the urban low-efficiency land candidate areas, urban land management data and urban planning land data; A patch processing module is used to perform cutting and filling operations on the urban low-efficiency land use patches to obtain low-efficiency land use areas of different land use types; The identification result generation module is used to generate urban low-efficiency land identification results based on the obtained low-efficiency land areas of different land types.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for identifying low-efficiency urban land based on multi-source data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for identifying urban low-efficiency land based on multi-source data as described in any one of claims 1 to 7 are implemented.

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