Land service total element data set acquisition method and device, terminal and medium

Abstract

The invention provides a land service total element data set obtaining method and device, a terminal and a medium, and the method comprises the steps: carrying out the screening and classification based on an initial correlation degree requirement for all land service data in a data pool through employing a pre-constructed correlation degree index model, and obtaining a land service total element data set; obtaining an initial land service data set and an initial residual data set; for each piece of land service data in the initial remaining data set, in combination with the initial land service data set, executing a compensation screening process based on the correlation index model to obtain the land service total factor data set, therefore, the situation that part of data cannot be accurately associated with other data due to inaccuracy or errors is avoided, and the accuracy and integrity of the obtained land business total-factor data set are improved.

Description

Technical Field

[0001] The present application belongs to the technical field of land resource management, and relates to a method, device, terminal and medium for acquiring a full-element data set of land business. Background Art

[0002] Land business data is data that describes the status of various land business operations in the process of land development and utilization projects, and can reflect the utilization status, planning, ownership, value and development potential of land parcels. In the process of land resource management, it is often necessary to obtain all land business data of a single land parcel in the same project cycle, that is, all land business data in the same life cycle, in order to construct a full-factor data set of land business in the life cycle, so as to achieve better land business management results.

[0003] In the prior art, the method for obtaining the land business full-factor data set is usually: based on the logical relationship between the data in the data pool in terms of business, space and time sequence, multiple land business data with associations are extracted to form a land business full-factor data set. However, in reality, there may be certain errors in the land business data, especially for some older data. Due to the incomplete coverage of the business management field at that time, there are often information omissions when the data is recorded, and due to the inconsistent connection standards between the text archive materials and the spatial graphics and other related information, it is difficult to associate the text archive materials with the spatial graphics and other related information, that is, the "map-property-file" is separated for a long time, which causes the land business data to be inaccurate or erroneous, and then when obtaining the association between these erroneous data, there may be no association or erroneous association with other data, which affects the accuracy and completeness of the land business full-factor data set, resulting in the inability of the land business full-factor data set to fully and comprehensively meet the business management needs of land plots in the process of development and utilization. Summary of the invention

[0004] In view of the shortcomings of the prior art described above, the purpose of the present application is to provide a method, terminal and medium for obtaining a land business full-factor data set, so as to solve the problem that when constructing the land business full-factor data set in the prior art, some data are not accurate or have errors due to information omissions, resulting in some data being unable to be accurately associated with other data, thereby resulting in the acquired land business full-factor data set having poor accuracy and completeness and being unable to meet business management needs.

[0005] In a first aspect, the present application provides a method for acquiring a land business full-factor data set, comprising:

[0006] For each land business data in the data pool, a pre-built correlation index model is used to perform screening and classification based on the initial correlation requirements to obtain an initial land business data group and an initial residual data group;

[0007] For each of the land business data in the initial remaining data group, combined with the initial land business data group, a compensation screening process based on the correlation index model is performed to obtain the land business full-factor data group.

[0008] In some embodiments of the first aspect of the present application, the compensation screening process includes:

[0009] Using the initial remaining data group as the current remaining data group and the initial land service data group as the current land service data group;

[0010] If the current remaining data group meets the space requirement, based on the compensation condition, obtaining the corresponding compensation correlation requirement;

[0011] For each of the land business data in the current remaining data group, using the correlation index model, performing screening and classification based on the compensation correlation requirement to obtain a new current land business data group and the current remaining data group;

[0012] Based on the new current remaining data set, compensation screening is performed again until the current remaining data set does not meet the space requirement.

[0013] In some embodiments of the first aspect of the present application, the spatial requirement is that each of the land business data in the current remaining data group has a spatial correlation greater than zero with the current remaining data group and other land business data in each of the current land business data groups.

[0014] In some implementations of the first aspect of the present application, the correlation index model is a pre-built model for obtaining the correlation degree between each of the land business data;

[0015] The correlation degree includes: time correlation, space correlation and business correlation, and the method for obtaining the correlation index model includes:

[0016] Preprocessing is performed based on each of the land business data to obtain a training data set;

[0017] Based on the training data set, a multi-dimensional indicator model is trained by a deep learning method to obtain the correlation indicator model.

[0018] In some implementations of the first aspect of the present application, the correlation index model is:

[0019] ;

[0020] ;

[0021] in, is the amount of land business data, is the spatial correlation between the land business data, is the business correlation between the land business data, is the time corresponding to each of the land business data, is the business stage value corresponding to each of the land business data, and .

[0022] In some implementations of the first aspect of the present application, the initial correlation requirement is:

[0023] ;

[0024] ;

[0025] in, is a preset numerical range, used to characterize the overall correlation between the land business data in the same land business full-factor data group based on spatial and business aspects;

[0026] When the time information of the land business data is inaccurate or erroneous, the compensation correlation requirement is:

[0027] ;

[0028] ;

[0029] When the spatial information or business information of the land business data is inaccurate or erroneous, the compensation correlation requirement is:

[0030] ;

[0031] ;

[0032] in, A numerical range for compensating the overall degree of correlation between the land business data based on past data experience.

[0033] In some implementations of the first aspect of the present application, it also includes: manually screening the land business full-factor data group, and using the screened land business full-factor data group as the final land business full-factor data group.

[0034] In a second aspect, the present application provides a device for acquiring a land business full-factor data set, including a preliminary classification module and a compensation screening module;

[0035] The preliminary classification module is used to perform screening and classification based on the initial relevance requirement for each land business data in the data pool using a pre-built relevance index model to obtain an initial land business data group and an initial remaining data group;

[0036] The compensation screening module is used to perform a compensation screening process based on the correlation index model for each of the land business data in the initial remaining data group, in combination with the initial land business data group, so as to obtain the land business full-factor data group.

[0037] In a third aspect, the present application provides a terminal, including: a processor and a memory, wherein the memory is communicatively connected to the processor;

[0038] The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that the terminal executes the method for acquiring the land business full-factor data set as described above.

[0039] In a fourth aspect, the present application provides a computer storage medium storing a computer program, which, when executed by a processor, implements the method for acquiring a land business full-element data set as described above.

[0040] As described above, the present application provides a method, device, terminal and medium for obtaining a land business full-element data group. By executing a compensatory screening process based on a correlation index model, the land business data in the initial remaining data group are re-screened and classified to avoid misjudged data from entering the correct land business data group due to inaccurate or erroneous data, so that the land business full-element data group finally obtained has higher accuracy and completeness, thereby achieving better land business management effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Shown is a flow chart of a method for acquiring a land business full-element data set as described in this application.

[0042] Figure 2 Shown is a schematic flow chart of a compensation screening process described in this application.

[0043] Figure 3 Shown is a flowchart of another method for acquiring a full-element data set of land business as described in this application.

[0044] Figure 4Shown is a flowchart of a method for obtaining a correlation index model described in this application.

[0045] Figure 5 Shown is a structural schematic diagram of a device for acquiring a land business full-factor data set as described in this application.

[0046] Figure 6 Shown is a schematic diagram of the structure of a terminal described in this application.

[0047] Description of reference numerals:

[0048] 41: preliminary classification module; 42: compensation screening module; 50: terminal; 51: processor; 52: memory; 53: network interface; 54: user interface; 55: bus system; 521: operating system; 522: application program. DETAILED DESCRIPTION

[0049] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0050] It should be noted that in the following description, with reference to the accompanying drawings, several embodiments of the present application are described in the accompanying drawings. It should be understood that other embodiments may also be used, and mechanical composition, structure, electrical and operational changes may be made without departing from the spirit and scope of the present application. The following detailed description should not be considered restrictive, and the scope of the embodiments of the present application is limited only by the claims of the published patents. The terms used here are only to describe specific embodiments and are not intended to limit the present application. Spatially related terms, such as "upper", "lower", "left", "right", "below", "below", "lower", "above", "upper", etc., may be used in the text to facilitate the description of the relationship between an element or feature shown in the figure and another element or feature.

[0051] Furthermore, as used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms "comprises", "includes", "includes" indicate the presence of the described features, operations, elements, components, items, types, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, operations, elements, components, items, types, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination.

[0052] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the technical solution in the embodiments of the present invention is further described in detail through the following embodiments and in combination with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the invention.

[0053] In response to the technical problems existing in the prior art, the present application provides, in the first aspect, a method for obtaining a land business full-factor data group. Based on the logical relationship between the data in the data pool in terms of business, space and time series, an initial land business data group and an initial residual data group are obtained, and based on the initial residual data group, a compensatory screening process is performed to obtain the final land business full-factor data group, thereby avoiding the inability to construct a land business full-factor data group or the construction of an erroneous land business full-factor data group due to inaccuracy or errors in some data, thereby improving the accuracy and completeness of the acquired land business full-factor data group, so as to achieve better land business management effects based on the land business full-factor data group.

[0054] The land business data refers to relevant data corresponding to each business stage of a land parcel in a project cycle, and the project cycle refers to the entire process from planning to development to recovery of a land parcel.

[0055] The land business full-factor data group is a collection of all land business data of the same land plot in the same project cycle, that is, all land business data in the same life cycle, which is used to characterize the land use status information in the life cycle.

[0056] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.

[0057] like Figure 1 As shown, this embodiment provides a method for obtaining a land business full-factor data set, including:

[0058] S100, for each land business data in the data pool, use the pre-built correlation index model to perform screening and classification based on the initial correlation requirement to obtain an initial land business data group and an initial remaining data group.

[0059] The land business data are data related to land plots within the scope to be managed, and the scope to be managed is the actual spatial scope where land business management is required, such as a district or county.

[0060] Specifically, the land business data related to the land plots within the scope to be managed are obtained through the national land space basic information platform.

[0061] The correlation index model is a pre-built model used to obtain the degree of correlation between each land business data, and screen and classify each land business data based on the initial correlation requirement, thereby forming each initial land business data group or the initial remaining data group.

[0062] The initial correlation requirement is a correlation degree requirement between the land business data within the same life cycle. Specifically, the initial correlation requirement is a correlation degree range between the land business data within the same life cycle.

[0063] Specifically, the correlation degree includes: time correlation, space correlation and business correlation.

[0064] Among them, the time correlation degree represents the temporal order between the business stages corresponding to each of the land business data, and the time correlation degree between each of the land business data is obtained based on the business stage and time information corresponding to each of the land business data.

[0065] Exemplarily, a complete life cycle of a land block includes six business stages of "approval-supply-use-supplement-check-registration", and the time correlation is the time difference between the later business stage and the previous business stage. Specifically, the business stage value corresponding to each of the land business data is obtained, wherein the business stage value represents the order of the business stage corresponding to the land business data in the entire business process, and the business stage value corresponding to the land business data with a business stage of "batch" is 1, the business stage value corresponding to the land business data with a business stage of "supply" is 2, and so on. The time corresponding to the land business data with a larger business stage value is subtracted from the time corresponding to the land business data with a smaller business stage value to obtain the time correlation between the two land business data. It should be noted that the time correlation between the land business data in a land business full-element data group is greater than zero.

[0066] The spatial correlation represents the degree of spatial overlap between the land plots corresponding to the land business data. For example, the spatial information corresponding to the land business data is overlaid and analyzed through software such as ArcGIS and SuperMap to obtain the spatial correlation.

[0067] The business relevance represents the degree of overlap between the business information corresponding to each of the land business data, wherein the business information represents the relevant information in each of the land business data that can describe the land development business, including but not limited to the land use function, the developer name and the project name. Specifically, based on the business information corresponding to each of the land business data, the business relevance is obtained using the longest common substring algorithm.

[0068] Furthermore, based on the degree of association between the data, the initial association requirement is used to determine whether the land business data are data of the same life cycle, so as to perform screening and classification. Specifically, between the land business data of the same life cycle, the time association is greater than zero, and the spatial association and the business association are within a preset range.

[0069] The initial land business data group is a set formed by the land business data that meet the initial correlation requirements, and the initial remaining data group is a set formed by the land business data that do not meet the initial correlation requirements with any other land business data. It should be noted that the data in the initial remaining data group may be isolated data that is not in the same life cycle as any other data, or it may be misjudged data that cannot meet the initial correlation requirements due to inaccurate or erroneous data itself, resulting in the correlation index model not being consistent with the actual situation when obtaining the correlation degree related to it.

[0070] Based on this, by screening and classifying each of the land business data through the correlation index model, the preliminary classification of the land business data can be achieved, so as to obtain the initial land business data group formed by data that does not contain errors, and classify the isolated data and the misjudged data into the initial remaining data group, so as to facilitate the subsequent acquisition of the accurate land business full-element data group based on the initial land business data group and the initial remaining data group.

[0071] S200, for each of the land business data in the initial remaining data group, combined with the initial land business data group, a compensation screening process based on the correlation index model is performed to obtain the land business full-factor data group.

[0072] Among them, the compensation screening process refers to re-obtaining the corresponding compensation correlation requirements based on the compensation conditions to avoid the misjudged data from entering the correct land business data group due to registration errors, thereby enhancing the accuracy and completeness of the acquired land business full-element data group, so as to better assist land business management.

[0073] It should be noted that the compensation condition is used to characterize the degree of correlation between the land business data within the same life cycle when the data is inaccurate or erroneous. Specifically, the compensation condition is a correlation degree condition obtained based on past data experience.

[0074] Exemplarily, when the time information of the land business data is inaccurate or erroneous, the time correlation is less than or equal to zero. When the spatial information or business information of the land business data is inaccurate or erroneous, based on past data experience, the boundary values ​​of the preset ranges corresponding to the spatial correlation and the business correlation are appropriately modified.

[0075] Specifically, Figure 2 As shown, the compensation screening process includes:

[0076] S210: Use the initial remaining data group as the current remaining data group, and use the initial land business data group as the current land business data group.

[0077] S220: If the current remaining data group meets the space requirement, obtain the corresponding compensation correlation requirement based on the compensation condition.

[0078] S230, for each of the land business data in the current remaining data group, use the correlation index model to perform screening and classification based on the compensation correlation requirement to obtain a new current land business data group and the current remaining data group.

[0079] Specifically, the degree of correlation between each land business data in the current remaining data group and the current remaining data group and other land business data in the current land business data group is obtained, and the land business data that meet the compensation correlation requirement are classified and screened into the corresponding current land business data group to construct a new current land business data group, and the land business data whose degree of correlation with other land business data does not meet the compensation correlation requirement are classified and screened into the current remaining data group to construct a new current remaining data group.

[0080] S240: re-perform compensation screening based on the new current remaining data group until the current remaining data group does not meet the space requirement.

[0081] The spatial requirement is that the spatial correlation between each of the land business data in the current remaining data group and other land business data in the current remaining data group and each of the current land business data groups has a value greater than zero.

[0082] Based on this, through the compensatory screening process, the misjudged data in the initial remaining data group is re-screened and classified to form the land business full-factor data group, thereby avoiding the risk of missing data in the land business full-factor data group due to the inaccuracy or errors in the data itself causing some data to fail to be correctly screened, effectively improving the completeness and accuracy of the land business full-factor data group, and facilitating better land business management results.

[0083] In some optional embodiments, in order to avoid possible over-compensation in the compensation screening process, the land business data that do not belong to the same life cycle are placed in the same land business full-factor data group, thereby affecting the accuracy of the land business full-factor data group, such as Figure 3 As shown, obtaining the land business full element data group also includes:

[0084] S300, manually screening the land business full-factor data group, and using the screened land business full-factor data group as the final land business full-factor data group.

[0085] Specifically, the operator manually screens each of the land business full-factor data groups based on past experience to ensure the accuracy of the land business full-factor data groups.

[0086] In some optional implementations, in order to improve the efficiency and accuracy of the screening and classification of each of the land business data, the correlation index model is a multi-dimensional index model constructed by a deep learning method. Specifically, when the correlation degree includes time correlation, spatial correlation and business correlation, such as Figure 4 As shown, the method of obtaining the correlation index model includes:

[0087] S110, performing preprocessing based on the land business data to obtain a training data set.

[0088] Among them, the preprocessing includes cleaning, normalizing, encoding and other processing operations on the ideal data subset to format all data in the ideal data subset, so as to facilitate the processing of the data in the ideal data subset in subsequent steps and improve the accuracy of the constructed correlation index model.

[0089] S120, based on the training data set, training the multi-dimensional indicator model by a deep learning method to obtain the correlation indicator model.

[0090] The multi-dimensional indicator model is a big data network model that classifies and screens data based on indicators of multiple dimensions. Exemplarily, the multi-dimensional indicator model can be any big data network model among the CLIP model, the BLIP model, the GLIP model, the VLMo model and the Flamingo model.

[0091] Through deep learning methods, the relationship between each indicator in the multidimensional indicator model is obtained. Specifically, for the spatial aspect, the spatial correlation coefficient is obtained as the weight of the spatial correlation in the correlation indicator model. For the business aspect, the business correlation coefficient is obtained as the weight of the business correlation in the correlation indicator model. The spatial correlation and the business correlation are jointly calculated through the weights to obtain the overall correlation degree between the land business data based on various aspects.

[0092] Exemplarily, the spatial aspect has the same weight as the business aspect.

[0093] It should be noted that the time correlation between each of the land business data is used to characterize the temporal order between each of the land business data. Based on this, the time correlation is used to independently judge the temporal order between each of the land business data, and is not suitable for joint calculation with the spatial correlation or the business correlation, but needs to be calculated separately.

[0094] Specifically, the business stages are sorted based on the business process sequence, and for each land business data, the corresponding business stage is obtained based on the business information of each land business data, and the time difference between the land business data with a later business stage and the land business data with an earlier business stage is obtained based on the time information of each land business data, as the time correlation. Wherein, for each land business data in the same land business full element data group, the time correlation is greater than zero.

[0095] Exemplarily, the correlation index model is:

[0096] ;

[0097] ;

[0098] in, is the amount of land business data, is the spatial correlation between the land business data, is the business correlation between the land business data, is the time corresponding to each of the land business data, is the business stage value corresponding to each of the land business data, and .

[0099] Exemplarily, a complete business process for a plot of land includes six business stages: "approval-supply-use-supplement-check-registration". The business stage value corresponding to the land business data with the business stage of "approval" is 1, the business stage value corresponding to the land business data with the business stage of "supply" is 2, and so on.

[0100] Furthermore, the initial correlation requirement is:

[0101] ;

[0102] ;

[0103] in, is a preset numerical range used to characterize the overall degree of correlation between the land business data in the same land business full element data group based on spatial and business aspects. .

[0104] Furthermore, the corresponding compensation correlation requirement is obtained based on the compensation condition, which is:

[0105] When the time information of the land business data is inaccurate or erroneous, the compensation correlation requirement is:

[0106] ;

[0107] ;

[0108] When the spatial information or business information of the land business data is inaccurate or erroneous, the compensation correlation requirement is:

[0109] ;

[0110] ;

[0111] in, The numerical range is a range of values ​​after the overall correlation between the land business data is compensated based on previous data experience, that is, a numerical range after appropriate modification. .

[0112] It should be noted that those skilled in the art may set different compensation correlation requirements based on the actual needs of the compensation screening process, or set multiple compensation correlation requirements to perform compensation screening in a hierarchical manner, and this embodiment does not impose any specific limitations thereto.

[0113] To sum up, this application performs compensatory screening on the data in the initial remaining data group through a compensatory screening process, thereby avoiding the inability to construct a correct land business full-factor data group due to the inaccuracy or errors of some data, thereby improving the accuracy and completeness of the land business full-factor data group, and thus facilitating the realization of better land business management results.

[0114] like Figure 5 As shown, this embodiment also provides a land business full-factor data group acquisition device, including a preliminary classification module 41 and a compensation screening module 42;

[0115] The preliminary classification module 41 is used to perform screening and classification based on the initial relevance requirement for each land business data in the data pool using a pre-built relevance index model to obtain an initial land business data group and an initial remaining data group;

[0116] The compensation screening module 42 is used to perform a compensation screening process based on the correlation index model for each of the land business data in the initial remaining data group, combined with the initial land business data group, to obtain the land business full-factor data group.

[0117] Based on the same technical concept, the method for acquiring the land business full-factor data set provided in the embodiment of the present invention can be implemented on the terminal side or the server side.

[0118] like Figure 6 As shown, it is an optional hardware structure diagram of a terminal provided by an embodiment of the present invention. The terminal 50 can be a mobile phone, a computer device, a tablet device, a personal digital processing device, a factory background processing device, etc. The terminal 50 includes: at least one processor 51, a memory 52, at least one network interface 53 and a user interface 54. The various components in the device are coupled together through a bus system 55. It can be understood that the bus system 55 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 55 also includes a power bus, a control bus and a status signal bus.

[0119] The user interface 54 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0120] It is understood that the memory 52 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory characterized by the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0121] The memory 52 in the embodiment of the present invention is used to store various types of data to support the operation of the terminal. Examples of these data include: any executable program used to operate on the terminal 50, such as an operating system 521 and an application 522; the operating system 521 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 522 can include various applications, such as a media player (MediaPlayer), a browser (Browser), etc., for implementing various application services. The method for obtaining the land business full-element data group provided in the embodiment of the present invention can be included in the application 522.

[0122] The method disclosed in the above embodiment of the present invention can be applied to the processor 51, or implemented by the processor 51. The processor 51 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 51 or the instruction in the form of software. The above processor may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 51 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The processor 51 may be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0123] In an exemplary embodiment, the terminal 50 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0124] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is called by a processor, the method for acquiring the land business full-factor data set provided by the present invention is implemented.

[0125] Among them, the computer-readable storage medium can be a tangible device that can hold and store instructions used by the instruction execution device. The computer-readable storage medium can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device.

[0126] The computer-readable program characterized herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. A network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0127] To sum up, this application, through a supplementary screening process, will re-classify and screen the land business data that is inaccurate or erroneous in itself to construct the land business full-factor data group, thereby effectively improving the accuracy and completeness of the land business full-factor data group, which is conducive to achieving better land business management results based on the obtained land business full-factor data group.

[0128] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0129] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for acquiring a land business full-factor data set, comprising: For each land business data in the data pool, a pre-built correlation index model is used to perform screening and classification based on the initial correlation requirements to obtain an initial land business data group and an initial residual data group; For each of the land business data in the initial remaining data group, in combination with the initial land business data group, a compensation screening process based on the correlation index model is performed to obtain the land business full element data group; Among them, the compensation screening process refers to re-obtaining the corresponding compensation correlation requirements based on the compensation conditions; the compensation conditions are used to characterize the degree of correlation between the land business data within the same life cycle when the data is inaccurate or erroneous.

2. The acquisition method according to claim 1, characterized in that: The compensation screening process includes: Using the initial remaining data group as the current remaining data group and the initial land service data group as the current land service data group; If the current remaining data group meets the space requirement, based on the compensation condition, obtaining the corresponding compensation correlation requirement; For each of the land business data in the current remaining data group, using the correlation index model, performing screening and classification based on the compensation correlation requirement to obtain a new current land business data group and the current remaining data group; Based on the new current remaining data set, compensation screening is performed again until the current remaining data set does not meet the space requirement.

3. The acquisition method according to claim 2, characterized in that: The spatial requirement is that each of the land business data in the current remaining data group has a spatial correlation greater than zero with the other land business data in the current remaining data group and each of the current land business data groups.

4. The acquisition method according to claim 2, characterized in that: The correlation index model is a pre-built model used to obtain the correlation degree between the land business data; The correlation degree includes: time correlation, space correlation and business correlation, and the method for obtaining the correlation index model includes: Preprocessing is performed based on each of the land business data to obtain a training data set; Based on the training data set, a multi-dimensional indicator model is trained by a deep learning method to obtain the correlation indicator model.

5. The acquisition method according to claim 4, characterized in that: The correlation index model is: ； ； in, is the amount of land business data, is the spatial correlation between the land business data, is the business correlation between the land business data, is the time corresponding to each of the land business data, is the business stage value corresponding to each of the land business data, and .

6. The acquisition method according to claim 5, characterized in that: The initial relevance requirement is: ； ； in, is a preset numerical range, used to characterize the overall correlation between the land business data in the same land business full-factor data group based on spatial and business aspects; When the time information of the land business data is inaccurate or erroneous, the compensation correlation requirement is: ； ； When the time information or business information of the land business data is inaccurate or erroneous, the compensation correlation requirement is: ； ； in, A numerical range for compensating the overall degree of correlation between the land business data based on past data experience.

7. The acquisition method according to claim 1, characterized in that: Also includes: The land business full-factor data group is manually screened, and the screened land business full-factor data group is used as the final land business full-factor data group.

8. A device for acquiring a land business full-factor data set, characterized in that: Includes a preliminary classification module and a compensatory screening module; The preliminary classification module is used to perform screening and classification based on the initial relevance requirement for each land business data in the data pool using a pre-built relevance index model to obtain an initial land business data group and an initial remaining data group; The compensation screening module is used to perform a compensation screening process based on the correlation index model for each of the land business data in the initial remaining data group, in combination with the initial land business data group, so as to obtain the land business full element data group; Among them, the compensation screening process refers to re-obtaining the corresponding compensation correlation requirements based on the compensation conditions; the compensation conditions are used to characterize the degree of correlation between the land business data within the same life cycle when the data is inaccurate or erroneous.

9. A terminal, characterized in that: include: A processor and a memory, wherein the memory is communicatively connected to the processor; The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that the terminal executes the method for acquiring the land business full-factor data set as described in any one of claims 1 to 7.

10. A computer storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for acquiring a land business full-factor data set as described in any one of claims 1 to 7 is implemented.

Images