Land resource optimal configuration system and method applied to land planning

Abstract

The invention discloses a land resource optimization configuration system and method applied to land planning, and relates to the technical field of land planning, and the method comprises the steps: obtaining industrial data and land resource data through a data source, and carrying out the preprocessing; constructing a matching algorithm model according to the industrial data and the land resource data, and analyzing industrial development requirements; recommending a land position and scale for each industry according to industrial development requirements and land resource data; and establishing a feedback information database, and adjusting parameters of the matching algorithm model according to user feedback information. According to the method, multiple factors can be comprehensively considered, accurate matching of the land and the industry is realized, industrial development requirements and effective utilization of land resources are met to the greatest extent, current actual requirements are concerned, and the future development trend can be predicted, so that land planning has perspectiveness, and the land planning efficiency is improved. Space can be reserved for industrial development in advance, supporting facility construction can be made, and sustainable development of the industry is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of land planning, and in particular to a land resource optimization configuration system and method used in land planning compilation. Background Art

[0002] In recent years, as a basic resource for the survival and development of human society, its rational planning and utilization are of vital importance to economic development, social stability and ecological environment protection. In modern society, land planning has become a key link for the government and relevant departments to carry out regional development planning, urban construction and industrial layout. With the acceleration of urbanization, the continuous adjustment of industrial structure and the growth of population, land planning is facing many severe challenges. The demand for land resources in different industries is diversified and differentiated. The manufacturing industry usually requires a large area of ​​land for the construction of factories and storage facilities, and has high requirements for transportation convenience and supporting infrastructure. The high-tech industry pays more attention to the scientific research environment, talent resources and the perfection of information and communication facilities around the land. Development is also dynamic, and its scale and demand will continue to evolve with market changes, technological innovation and other factors. How to accurately grasp the needs of industrial development and achieve precise matching of land resources and industries has become a major problem in land planning. Land resources themselves are complex and limited. The natural attributes of land, such as geographical location, topography, soil quality, water resources and ecological environment, are different. These factors directly affect the development and utilization methods and suitability of land. Social attributes such as land ownership, use rights and planned use also increase the complexity of land resource allocation. Under the conditions of limited land resources, in order to meet the development needs of various industries while taking into account ecological balance, urban sustainable development and other multi-faceted goals, a scientific and efficient land resource optimization allocation system and method is needed.

[0003] However, the current common solutions have many shortcomings, including: most of the analysis models of existing technologies only consider a few key factors, such as land area, industry scale, etc., while ignoring the combined impact of many other important factors. When recommending land locations for industries, they only make simple judgments based on land prices and transportation convenience, without considering factors such as the industry's environmental requirements, energy supply conditions, labor resource distribution, and future development potential of the land. As a result, the matching degree between land and industry is not high, which can easily lead to waste of resources or limited industrial development. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above-mentioned problems existing in the existing land resource optimization allocation system and method used for land planning, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide a land resource optimization allocation system and method for land planning, which is suitable for solving the problem that most of the analysis models in the prior art only consider a few key factors, while ignoring the combined influence of many other important factors. When recommending land locations for industries, simple judgments are made based only on land prices and transportation convenience, without considering factors such as the industry's environmental requirements, energy supply conditions, labor resource distribution, and future development potential of the land, resulting in a low match between land and industry, which can easily lead to waste of resources or limited industrial development.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides a method for optimizing land resource allocation for land planning, which includes obtaining industry data and land resource data through a data source and preprocessing them; constructing a matching algorithm model based on the industry data and land resource data and analyzing the development needs of the industry; recommending land location and scale for each industry based on the industry development needs and land resource data; establishing a feedback information database, and adjusting the parameters of the matching algorithm model based on user feedback information.

[0009] As a preferred solution of the land resource optimization allocation method applied to land planning compilation described in the present invention, wherein: the data source includes a satellite receiving system, unmanned aerial survey equipment and sensors; the industry data includes basic industry information, industry development demand data, industry special requirements data, industry policies and market data; the land resource data includes basic land information data, land natural attribute data, land development and utilization data and land infrastructure data.

[0010] As a preferred solution of the land resource optimization allocation method applied to land planning compilation described in the present invention, the specific steps of constructing the matching algorithm model are: obtaining industry data and land resource data through multiple data sources and preprocessing them; constructing a matching algorithm model based on a recurrent neural network and extracting industry development needs; using a machine learning algorithm to conduct an in-depth analysis of development goals, planning data and market supply and demand data in the industry data; inputting the industry data and land resource data into the selected matching algorithm model; based on the matching calculation results, recommending the land location with the highest matching degree for each industry; establishing a feedback information database, and adjusting the parameters of the matching algorithm model based on the feedback information analysis results.

[0011] As a preferred solution of the land resource optimization allocation method applied to land planning compilation described in the present invention, the specific formula of the machine learning algorithm is:

[0012]

[0013] Among them, M is the matching degree between land resources and industries; I is the industry data vector; L is the land resource data vector; and W is the weight vector of industrial development demand characteristics.

[0014] As a preferred scheme of the land resource optimization allocation method applied to land planning compilation described in the present invention, the specific situation of the matching degree between land resources and industries is as follows: when the matching degree between land resources and industries is close to 1, it means that the comprehensive matching degree between land resources and industries in various characteristics is high, which means that the development needs of the industry and the various conditions of the land are well matched with each other taking into account the weights; when the matching degree between land resources and industries is close to 0, it means that the comprehensive matching degree between land resources and industries in various characteristics is low, and it is necessary to reconsider industrial planning or find other more suitable land resources.

[0015] As a preferred solution of the land resource optimization configuration method applied to land planning compilation described in the present invention, the specific formula for dynamically adjusting the matching algorithm model parameters is:

[0016]

[0017] Among them, P is the probability of adjusting the matching algorithm model parameters; I is the industry data vector; L is the land resource data vector; W is the industry development demand feature weight vector; D is the feedback information difference vector; μ is the adjustment coefficient.

[0018] As a preferred scheme of the land resource optimization allocation method applied to land planning compilation described in the present invention, the specific circumstances of the dynamic adjustment of the matching algorithm model parameters are as follows: when the probability of adjusting the matching algorithm model parameters is close to 1, it means that based on the comprehensive analysis of the current industrial data, land resource data and feedback information, the possibility of adjusting the matching algorithm model is high, which means that the system believes that the current parameter settings do not conform to the actual situation; when the probability of adjusting the matching algorithm model parameters is close to 0, it means that the probability of adjusting the parameters is low, that is, the current parameter settings are reasonable to a certain extent, which means that the system believes that the current difference is not enough to adjust the matching algorithm parameters.

[0019] In the second aspect, in order to further solve the above technical problems, the present invention provides a land resource optimization configuration system for land planning, which includes: a data acquisition module for obtaining industry data and land resource data through a data source and performing preprocessing; a model building module for building a matching algorithm model based on the industry data and land resource data and analyzing the development needs of the industry; a land planning module for recommending land location and scale for each industry based on the industry development needs and land resource data; and a parameter adjustment module for establishing a feedback information database and adjusting the parameters of the matching algorithm model based on user feedback information.

[0020] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of a method for optimizing land resource allocation applied to land planning as described in the first aspect of the present invention.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of a method for optimizing land resource allocation applied to land planning as described in the first aspect of the present invention.

[0022] The beneficial effects of the present invention are as follows: the matching algorithm model constructed based on industrial data and land resource data can comprehensively consider multiple factors and realize accurate matching of land and industry. Compared with traditional methods, this accurate matching improves the rationality and scientificity of land resource allocation, meets the needs of industrial development and the effective use of land resources to the greatest extent, and deeply analyzes the development needs of the industry. It not only pays attention to current actual needs, but also predicts future development trends. This makes land planning forward-looking, and can reserve space for industrial development in advance and build supporting facilities, thereby promoting the sustainable development of the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0024] Figure 1 This is a flow chart for implementing the present invention in Example 1. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1

[0029] Reference Figure 1 , which is the first embodiment of the present invention, and provides a land resource optimization configuration method applied to land planning, comprising the following steps:

[0030] S1: Obtain industry data and land resource data through data sources and perform preprocessing.

[0031] Preferably, Figure 1 The implementation process of the present invention is shown as follows: first, industrial data and land resource data are obtained through a data source and preprocessed; a matching algorithm model is constructed based on the industrial data and land resource data and the development needs of the industry are analyzed; land location and scale are recommended for each industry based on the industrial development needs and land resource data; a feedback information database is established, and the parameters of the matching algorithm model are adjusted based on user feedback information.

[0032] Furthermore, the data sources include satellite receiving systems, unmanned aerial survey equipment and sensors.

[0033] Furthermore, the industry data includes basic industry information, industry development demand data, industry special requirement data, industry policies and market data.

[0034] Furthermore, the land resource data includes basic land information data, land natural attribute data, land development and utilization data, and land infrastructure data.

[0035] Specifically, preprocessing ensures the quality and consistency of data by performing real-time preprocessing of industrial data and land resource data on data nodes, including data cleaning, noise reduction and standardization.

[0036] S2: Construct a matching algorithm model based on the industry data and land resource data and analyze the development needs of the industry.

[0037] Preferably, the specific steps of constructing the matching algorithm model are:

[0038] Acquire industrial data and land resource data through multiple data sources and perform pre-processing;

[0039] Build a matching algorithm model based on recurrent neural network and extract industrial development needs;

[0040] Use machine learning algorithms to conduct in-depth analysis of development goals, planning data, and market supply and demand data in industry data;

[0041] Input industrial data and land resource data into the selected matching algorithm model;

[0042] Based on the matching calculation results, the land location with the highest matching degree is recommended for each industry;

[0043] Establish a feedback information database and adjust the parameters of the matching algorithm model based on the feedback information analysis results.

[0044] Specifically, the specific formula of the machine learning algorithm is:

[0045]

[0046] Among them, M is the matching degree between land resources and industries; I is the industry data vector; L is the land resource data vector; and W is the weight vector of industrial development demand characteristics.

[0047] Furthermore, the specific situation of the matching degree between the land resources and the industry is as follows:

[0048] When the matching degree between land resources and industries is close to 1, it means that the comprehensive matching degree between land resources and industries in various characteristics is high, which means that the development needs of the industry and the various conditions of the land are well matched after considering the weights;

[0049] When the matching degree between land resources and industries is close to 0, it means that the comprehensive matching degree between land resources and industries in various characteristics is low, and it is necessary to reconsider industrial planning or find other more suitable land resources.

[0050] Preferably, analyzing industrial development needs can predict the future development trend of the industry and provide forward-looking guidance for land planning.

[0051] S3: Recommend land location and scale for each industry based on industrial development needs and land resource data.

[0052] Preferably, a matching algorithm model that comprehensively considers multiple factors can more accurately reflect the complex relationship between land and industry and improve the matching accuracy between land and industry.

[0053] Preferably, through a precise matching algorithm model, the optimal configuration of land resources and industries can be achieved, thereby improving land use efficiency and economic benefits.

[0054] S4: Establish a feedback information database and adjust the parameters of the matching algorithm model based on user feedback information.

[0055] Preferably, the specific formula for dynamically adjusting the matching algorithm model parameters is:

[0056]

[0057] Among them, P is the probability of adjusting the matching algorithm model parameters; I is the industry data vector; L is the land resource data vector; W is the industry development demand feature weight vector; D is the feedback information difference vector; μ is the adjustment coefficient.

[0058] Furthermore, the specific situation of dynamically adjusting the matching algorithm model parameters is as follows:

[0059] When the probability of adjusting the matching algorithm model parameters is close to 1, it means that based on the comprehensive analysis of the current industry data, land resource data and feedback information, there is a high possibility of adjusting the matching algorithm model, which means that the system believes that the current parameter settings do not conform to the actual situation;

[0060] When the probability of adjusting the matching algorithm model parameters is close to 0, it means that the probability of adjusting the parameters is low, that is, the current parameter settings are reasonable to a certain extent, which means that the system believes that the current difference is not enough to adjust the vehicle method parameters.

[0061] Preferably, the establishment of a feedback mechanism makes land planning no longer a one-time static process, but a dynamic process that can be continuously adjusted and optimized based on actual feedback.

[0062] This embodiment also provides a land resource optimization configuration system for land planning, including: a data acquisition module for obtaining industry data and land resource data through a data source and performing preprocessing; a model building module for building a matching algorithm model based on the industry data and land resource data and analyzing the development needs of the industry; a land planning module for recommending land location and scale for each industry based on the industry development needs and land resource data; and a parameter adjustment module for establishing a feedback information database and adjusting the parameters of the matching algorithm model based on user feedback information.

[0063] This embodiment also provides a computer device, which is applicable to a method for optimizing the allocation of land resources applied to land planning, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a method for optimizing the allocation of land resources applied to land planning as proposed in the above embodiment.

[0064] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0065] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a method for optimizing the allocation of land resources applied to land planning is implemented as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0066] In summary, the matching algorithm model constructed based on industrial data and land resource data in the present invention can comprehensively consider multiple factors and realize accurate matching of land and industry. Compared with traditional methods, this accurate matching improves the rationality and scientificity of land resource allocation, meets the needs of industrial development and the effective use of land resources to the greatest extent, and deeply analyzes the development needs of the industry. It not only pays attention to current actual needs, but also predicts future development trends. This makes land planning forward-looking, and can reserve space for industrial development in advance and build supporting facilities, thereby promoting the sustainable development of the industry.

[0067] Example 2

[0068] Referring to Tables 1 to 3, the second embodiment of the present invention is shown. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related instructions of the present invention in an actual environment are provided.

[0069] Tables 1 and 2 show the comparison between the prior art and our invention in data collection, matching algorithms and recommendation results in this example.

[0070] Table 1 Data collection comparison table

[0071]

[0072] Table 2 Comparison of matching algorithms and recommendation results

[0073]

[0074] Table 3 is an evaluation table of the improvement effect of the present invention on the technical solution after using the feedback mechanism.

[0075] Table 3 Feedback mechanism effect evaluation table

[0076] time Feedback efficiency improvement index of the technical solution of the present invention (%) Improvement index of the technical solution of the present invention (%) First Year 22 16 Year 2 30 22 Year 3 38 28

[0077] It can be seen from the above table that the present invention can comprehensively consider multiple factors and achieve accurate matching of land and industry. Compared with traditional methods, this accurate matching improves the rationality and scientificity of land resource allocation, and meets the needs of industrial development and the effective use of land resources to the greatest extent.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A land resource optimization allocation method applied to land planning, characterized by: include: Obtain industrial data and land resource data through data sources and perform pre-processing; Construct a matching algorithm model based on the industrial data and land resource data and analyze the development needs of the industry; Recommend land location and size for each industry based on industry development needs and land resource data; Establish a feedback information database and adjust the parameters of the matching algorithm model based on user feedback information.

2. The land resource optimization allocation method for land planning as claimed in claim 1, characterized in that: The data sources include satellite receiving systems, drone aerial survey equipment and sensors; The industry data includes basic industry information, industry development demand data, industry special requirements data, industry policies and market data; The land resource data includes basic land information data, land natural attribute data, land development and utilization data, and land infrastructure data.

3. The land resource optimization allocation method for land planning as claimed in claim 2 is characterized by: The specific steps of constructing the matching algorithm model are: Acquire industrial data and land resource data through multiple data sources and perform pre-processing; Build a matching algorithm model based on recurrent neural network and extract industrial development needs; Use machine learning algorithms to conduct in-depth analysis of development goals, planning data, and market supply and demand data in industry data; Input industrial data and land resource data into the selected matching algorithm model; Based on the matching calculation results, the land location with the highest matching degree is recommended for each industry; Establish a feedback information database and adjust the parameters of the matching algorithm model based on the feedback information analysis results.

4. The land resource optimization allocation method for land planning as claimed in claim 3 is characterized by: The specific formula of the machine learning algorithm is: Among them, M is the matching degree between land resources and industries; I is the industry data vector; L is the land resource data vector; and W is the weight vector of industrial development demand characteristics.

5. The land resource optimization allocation method for land planning as claimed in claim 4 is characterized by: The specific details of the matching degree between land resources and industries are as follows: When the matching degree between land resources and industries is close to 1, it means that the comprehensive matching degree between land resources and industries in various characteristics is high, which means that the development needs of the industry and the various conditions of the land are well matched after considering the weights; When the matching degree between land resources and industries is close to 0, it means that the comprehensive matching degree between land resources and industries in various characteristics is low, and it is necessary to reconsider industrial planning or find other more suitable land resources.

6. The land resource optimization allocation method for land planning as claimed in claim 5, characterized in that: The specific formula for dynamically adjusting the matching algorithm model parameters is: Among them, P is the probability of adjusting the matching algorithm model parameters; I is the industry data vector; L is the land resource data vector; W is the industry development demand feature weight vector; D is the feedback information difference vector; μ is the adjustment coefficient.

7. The land resource optimization allocation method for land planning as claimed in claim 6, characterized in that: The specific situation of dynamically adjusting the matching algorithm model parameters is as follows: When the probability of adjusting the matching algorithm model parameters is close to 1, it means that based on the comprehensive analysis of the current industry data, land resource data and feedback information, there is a high possibility of adjusting the matching algorithm model, which means that the system believes that the current parameter settings do not conform to the actual situation; When the probability of adjusting the matching algorithm model parameters is close to 0, it means that the probability of adjusting the parameters is low, that is, the current parameter settings are reasonable to a certain extent, which means that the system believes that the current difference is not enough to adjust the vehicle method parameters.

8. A land resource optimization configuration system applied to land planning compilation, based on a land resource optimization configuration method applied to land planning compilation according to any one of claims 1 to 7, characterized in that: include, Data collection module. Used to obtain industrial data and land resource data through data sources and perform pre-processing; A model building module, used to build a matching algorithm model based on the industry data and land resource data and analyze the development needs of the industry; Land planning module, which is used to recommend land location and scale for each industry based on industrial development needs and land resource data; The parameter adjustment module is used to establish a feedback information database and adjust the parameters of the matching algorithm model according to user feedback information.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for optimizing land resource allocation applied to land planning and preparation as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for optimizing land resource allocation applied to land planning as described in any one of claims 1 to 7 are implemented.

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