Urban and rural planning data storage method and device, medium and equipment

Abstract

The invention relates to a method and a device for storing urban and rural planning data, a medium and equipment. The method comprises the following steps: acquiring stored urban and rural map planning data of a target area; the urban and rural map planning data comprises an urban and rural planning text and urban and rural map data; the urban and rural planning text comprises a plurality of planning projects, and the urban and rural map data identifies a plurality of planning areas corresponding to the plurality of planning projects; performing text recognition on the plurality of planning projects to obtain a plurality of project planning times; and according to the plurality of project planning times, updating and storing the urban and rural map data of the corresponding planning area. The urban and rural map planning data of the target region can be updated in time, the urban and rural map data can be prevented from being not updated in time corresponding to the project planning time, and the accuracy of the stored urban and rural map planning data is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of storage updating of urban and rural planning data, and particularly relates to a storage method, device, medium and equipment for urban and rural planning data. BACKGROUND

[0002] Urban planning, as a discipline with a long history, has been quietly emerging and developing since ancient times. In the long course of history, different nations have gradually formed their own unique and distinctive knowledge systems based on their unique culture, geography, society and other factors. These knowledge systems, like the stars in the starry sky, each with its own characteristics, enrich the connotation of the discipline of urban planning.

[0003] Urban planning focuses on in-depth research on the future development direction of the city. It not only considers the simple surface phenomena such as city size and population growth, but also involves precise insight and forward-looking prediction of the development trend of the city's economy, culture, environment and other multi-dimensional aspects. At the same time, urban planning attaches great importance to the rational layout of the city, from the scientific division of the city's functional areas such as business districts, residential areas and industrial areas, to the optimal allocation of transportation networks and public facilities, every link is carefully weighed and designed. In addition, urban planning also undertakes the important mission of comprehensively arranging various engineering construction in the city, which covers the overall deployment of large public buildings, landscape greening and other projects from the city's infrastructure construction such as roads, bridges, water and electricity supply. Urban planning is the grand blueprint of city development in a certain period, which provides clear guidance and solid guarantee for the orderly construction and sustainable development of the city.

[0004] Urban planning data can display urban planning through visual maps and data, but since urban development is accompanied by many engineering projects, if the urban planning data cannot be updated in real time corresponding to the engineering projects, it will seriously affect the accuracy of the stored urban planning data. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a storage method, device, medium and equipment for urban and rural planning data, which can overcome the shortcomings of the prior art.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is:

[0007] The first embodiment of the present application provides a storage method for urban and rural planning data, comprising:

[0008] acquire stored urban-rural map planning data of a target region; the urban-rural map planning data comprises urban-rural planning texts and urban-rural map data; the urban-rural planning texts comprise a plurality of planning projects, and the urban-rural map data identifies a plurality of planning areas corresponding to the plurality of planning projects;

[0009] perform text recognition on the plurality of planning projects to obtain a plurality of project planning times;

[0010] update the urban-rural map data of the corresponding planning areas according to the plurality of project planning times.

[0011] As an implementation form, the step of performing text recognition on the plurality of planning projects to obtain a plurality of project planning times comprises:

[0012] inputting the plurality of planning projects into a trained text semantic recognition model to obtain semantic texts of the respective planning projects;

[0013] inputting the respective semantic texts into a trained planning time recognition model to obtain project planning times of the respective semantic texts.

[0014] As an implementation form, the step of inputting the plurality of planning projects into a trained text semantic recognition model to obtain semantic texts of the respective planning projects comprises:

[0015] training a plurality of initial semantic recognition models according to a plurality of text semantic recognition training samples to obtain a plurality of candidate semantic recognition models;

[0016] testing and evaluating the plurality of candidate semantic recognition models according to a plurality of text semantic recognition test samples, and confirming a candidate semantic recognition model with the highest evaluation as the text semantic recognition model;

[0017] inputting the plurality of planning projects into the trained text semantic recognition model to obtain semantic texts of the respective planning projects.

[0018] As an implementation form, the step of inputting the respective semantic texts into a trained planning time recognition model to obtain project planning times of the respective semantic texts comprises:

[0019] training a plurality of initial planning time recognition models according to a plurality of project planning time recognition training samples to obtain a plurality of candidate planning time recognition models;

[0020] testing and evaluating the plurality of candidate planning time recognition models according to a plurality of project planning time recognition test samples, and confirming a candidate planning time recognition model with the highest evaluation as the planning time recognition model;

[0021] input each of the semantic texts into a trained planning time recognition model to obtain a project planning time of each of the semantic texts.

[0022] As an implementation form, the step of updating the urban-rural map data of the corresponding planning area according to the project planning times comprises:

[0023] a planning project corresponding to a project planning time reaching a real-time time is determined as a target planning project;

[0024] a planning area corresponding to the target planning project is determined as a target planning area, and the urban-rural map data of the target planning area is updated.

[0025] As an implementation form, the step of updating the urban-rural map data of the target planning area comprises:

[0026] obtaining a region location and a region range of the target planning area;

[0027] obtaining a region remote sensing image of the target planning area from a region remote sensing image of the target area according to the region location and the region range;

[0028] updating the urban-rural map data of the target planning area according to the region remote sensing image.

[0029] As an implementation form, the step of updating the urban-rural map data of the target planning area comprises:

[0030] obtaining a region location and a region range of the target planning area;

[0031] driving a drone to shoot a region map image of the target planning area according to the region location and the region range;

[0032] updating the urban-rural map data of the target planning area according to the region map image.

[0033] The second embodiment of the present application provides a storage device for urban-rural planning data, comprising:

[0034] an urban-rural map planning data acquisition module, configured to acquire urban-rural map planning data of a target area stored; the urban-rural map planning data comprises urban-rural planning texts and urban-rural map data; the urban-rural planning texts comprise a plurality of planning projects, and the urban-rural map data is marked with a plurality of planning areas corresponding to the plurality of planning projects;

[0035] a project planning time acquisition module, configured to perform text recognition on the plurality of planning projects to obtain a plurality of project planning times;

[0036] The urban-rural map data storage updating module is configured to update the urban-rural map data of the corresponding planning area according to the multiple project planning times.

[0037] The third embodiment of the present application provides a computer readable storage medium storing a computer program, and the computer program is executed by a processor to implement the steps of the storage method for urban-rural planning data.

[0038] The fourth embodiment of the present application provides a computer device including a storage, a processor, and a computer program stored in the storage and executable by the processor, and the processor implements the steps of the storage method for urban-rural planning data when executing the computer program.

[0039] Compared with the prior art, the present application has the following beneficial effects:

[0040] The present application can obtain multiple project planning times by text recognition on multiple planning projects of urban-rural planning texts, update the urban-rural map data of the corresponding planning area according to the multiple project planning times, and update the urban-rural map planning data of the target area in a timely manner, thereby preventing the urban-rural map data from failing to update in a timely manner according to the project planning time and improving the accuracy of the stored urban-rural map planning data.

[0041] In order to better understand and implement, the present application will be described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The flowchart of the storage method for urban-rural planning data of one embodiment of the present application;

[0043] Figure 2 The flowchart of step S2 of the storage method for urban-rural planning data of one embodiment of the present application;

[0044] Figure 3 The module connection diagram of the storage device for urban-rural planning data of one embodiment of the present application.

[0045] 100, a storage device for urban-rural planning data; 101, an urban-rural map planning data acquisition module; 102, a project planning time acquisition module; 103, an urban-rural map data storage updating module. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the present application clearer, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

[0047] It should be noted that the embodiments described are merely some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0048] In the following description, same numbers in different drawings represent same or similar elements unless otherwise indicated. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are merely used to distinguish similar objects, and do not necessarily indicate a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. The singular forms "a", "an" and "the" used in the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. The word "if" used herein can be interpreted as "when" or "when" or "in response to determining".

[0049] In addition, in the description of the present application, "multiple" means two or more, unless otherwise specified. The association between the objects described by "and / or" can represent three possible relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. The character " / " generally represents an "or" relationship between the associated objects before and after it.

[0050] Please refer to Figure 1 which is a flowchart of the storage method of urban and rural planning data of the first embodiment of the present application, the method comprising:

[0051] S1: Obtain the stored urban and rural map planning data of the target area; the urban and rural map planning data includes urban and rural planning text and urban and rural map data; the urban and rural planning text includes a plurality of planning projects, and the urban and rural map data identifies a plurality of planning regions corresponding to the plurality of planning projects.

[0052] The types of planning projects can include residential construction projects, public facility projects, infrastructure construction projects, etc., wherein the residential construction projects include affordable housing, shantytown reconstruction and residential community development, aiming to improve the living conditions of residents; the public facility projects involve the construction of schools, hospitals, parks, stadiums, urban greenery, etc., providing education, medical and leisure services; the infrastructure construction projects include roads, bridges, tunnels and public transportation systems, etc.

[0053] The planning region refers to the map data region corresponding to the planning project, including the map data region where the planning project is located and the map data region affected by the planning project.

[0054] S2: text recognition is performed on the plurality of planning projects to obtain a plurality of project planning times.

[0055] The project planning time can be a planned end time or an acceptance time of the planning project.

[0056] S3: according to the plurality of project planning times, the urban-rural map data of the corresponding planning area is updated.

[0057] The visual display of the urban-rural map data is conducive to more intuitive display of the progress and effect of urban-rural planning. Therefore, according to the project planning time, the urban-rural map data of the corresponding planning area is stored, which can improve the accuracy of the stored urban planning data of the planning area.

[0058] The present application can obtain a plurality of project planning times by performing text recognition on a plurality of planning projects of urban-rural planning text, and update the urban-rural map data of the corresponding planning area according to the plurality of project planning times, so as to update the urban-rural map planning data of the target area in a timely manner.

[0059] The storage method for urban-rural planning data can obtain a plurality of project planning times by performing text recognition on a plurality of planning projects of urban-rural planning text, and update the urban-rural map data of the corresponding planning area according to the plurality of project planning times, so as to update the urban-rural map planning data of the target area in a timely manner, which can prevent the urban-rural map data from failing to update in a timely manner according to the project planning time, and improve the accuracy of the stored urban-rural map planning data.

[0060] Please refer to Figure 2 In a feasible embodiment, the S2: text recognition is performed on the plurality of planning projects to obtain a plurality of project planning times, which includes:

[0061] S21: inputting the plurality of planning projects into a trained text semantic recognition model to obtain semantic text of each of the planning projects.

[0062] Since the recorded text descriptions of different planning projects are different, the accuracy of directly recognizing the project planning time of the planning project is low. After obtaining the semantic text of the planning project, since the semantic text is the semantic extraction result of the planning project, the interference of text words irrelevant to semantics is reduced, so the project planning time can be more accurately extracted according to the semantic text.

[0063] S22: inputting each of the semantic texts into a trained planning time recognition model to obtain the project planning time of each of the semantic texts.

[0064] In the embodiment, the semantic text of each planning project is obtained through the text semantic recognition model, and then the project planning time of each planning project is accurately obtained through the planning time recognition model.

[0065] In a feasible embodiment, the step S21 of inputting the plurality of planning projects into the trained text semantic recognition model to obtain the semantic text of each planning project comprises:

[0066] S211: training a plurality of initial semantic recognition models according to a plurality of text semantic recognition training samples to obtain a plurality of candidate semantic recognition models;

[0067] The initial semantic recognition model includes a neural network language model, a Transformer model, a BERT model, etc. The text semantic recognition training sample includes text content to be recognized and semantic information corresponding to the text content.

[0068] S212: testing and evaluating the plurality of candidate semantic recognition models according to a plurality of text semantic recognition test samples, and confirming the candidate semantic recognition model with the highest evaluation as the text semantic recognition model.

[0069] The test evaluation results of the plurality of second organization identification models can be obtained according to mIoU, Precision and Recal, etc. The higher the mIoU, Precision and Recal, etc., the better the test evaluation results.

[0070] The MIoU (Mean Intersection over Union) is a core index in the field of computer vision for evaluating the performance of a semantic segmentation model. It quantifies the accuracy of the model by calculating the proportion of the overlapping area between the predicted results and the true labels.

[0071] Precision is a key parameter for measuring the accuracy of model prediction, and its calculation formula is: Precision=TP / (TP+FP), TP is the correct positive class, and FP is the predicted false positive class (that is, the negative class is treated as the positive class).

[0072] Recal (Recall) is the proportion of the number of correctly predicted positive classes to the total number of true positive classes, that is, in the positive class, how many positive classes are retrieved, and its calculation formula is Recal=TP / (TP+FN), where TP is the correctly predicted positive class, and FN is the predicted false negative class (that is, the positive class is treated as the negative class).

[0073] S213: input the plurality of planning projects into the trained text semantic recognition model to obtain semantic texts of the respective planning projects.

[0074] In the embodiment, by training and testing and evaluating a plurality of initial semantic recognition models, the most accurate text semantic recognition model can be obtained, and the accuracy of the obtained semantic texts of the respective planning projects is improved.

[0075] In a feasible embodiment, the step S22 of inputting the respective semantic texts into the trained planning time recognition model to obtain project planning times of the respective semantic texts comprises:

[0076] S221: training a plurality of initial planning time recognition models according to a plurality of project planning time recognition training samples to obtain a plurality of candidate planning time recognition models;

[0077] The initial planning time recognition model can be a convolutional neural network model, a recurrent neural network model, a Transformer model, etc.

[0078] S222: testing and evaluating the plurality of candidate planning time recognition models according to a plurality of project planning time recognition test samples, and determining the candidate planning time recognition model with the highest evaluation as the planning time recognition model.

[0079] The results of the testing and evaluation of the plurality of second organization identification models can be obtained according to mIoU, Precision, and Recal, etc. The higher the mIoU, Precision, and Recal, etc., the better the results of the testing and evaluation.

[0080] The MIoU (Mean Intersection over Union) is a core index in the field of computer vision for evaluating the performance of a semantic segmentation model. It quantifies the accuracy of the model by calculating the proportion of the overlapping area between the predicted results and the true labels.

[0081] Precision is a key parameter for measuring the accuracy of a model, and its calculation formula is: Precision = TP / (TP+FP), where TP is the correctly predicted positive class, and FP is the incorrectly predicted positive class (i.e., treating negative class as positive class).

[0082] Recal (Recall) is the proportion of the number of correctly predicted positive classes to the total number of true positive classes, i.e., how many positive classes are retrieved from the positive classes. Its calculation formula is: Recal = TP / (TP+FN), where TP is the correctly predicted positive class, and FN is the incorrectly predicted negative class (i.e., treating positive class as negative class).

[0083] S223: input each of the semantic texts into the trained planning time recognition model to obtain the project planning time of each of the semantic texts.

[0084] In this embodiment, by training and testing evaluation on a plurality of initial planning time recognition models, the planning time recognition model with the highest accuracy can be obtained to improve the accuracy of the obtained project planning time.

[0085] In a feasible embodiment, the S3: updating the urban and rural map data of the corresponding planning area according to the plurality of project planning times, comprises:

[0086] S31: determining the project planning time corresponding to the real-time time as a target planning project;

[0087] S32: determining the planning area corresponding to the target planning project as a target planning area, and updating the urban and rural map data of the target planning area.

[0088] In a feasible embodiment, the S32: updating the urban and rural map data of the target planning area, comprises:

[0089] S321: obtaining the regional positioning and regional range of the target planning area;

[0090] S3221: obtaining the regional remote sensing image of the target planning area from the regional remote sensing image of the target area according to the regional positioning and regional range;

[0091] S3222: updating the urban and rural map data of the target planning area according to the regional remote sensing image.

[0092] In this embodiment, when the urban and rural map data is a remote sensing image map data, the regional remote sensing image of the target planning area can be obtained from the regional remote sensing image of the target area according to the regional positioning and regional range, so as to complete the updating storage of the urban and rural map data of the target planning area.

[0093] In a feasible embodiment, the S3: updating the urban and rural map data of the target planning area, comprises:

[0094] S321: obtaining the regional positioning and regional range of the target planning area;

[0095] S3231: driving the unmanned aerial vehicle to shoot the regional map image of the target planning area according to the regional positioning and regional range;

[0096] S3232: updating the urban-rural map data of the target planning area according to the regional map image.

[0097] In the embodiment, when the urban-rural map data is a map image taken by a UAV at a high altitude, the UAV needs to be driven to take pictures above the target planning area to obtain a regional map image, which can be used to update the stored urban-rural map data of the target planning area.

[0098] Referring to Figure 3 The second embodiment of the present application provides a storage device 100 for urban-rural planning data, comprising:

[0099] An urban-rural map planning data acquisition module 101 is configured to acquire stored urban-rural map planning data of a target area; the urban-rural map planning data comprises urban-rural planning text and urban-rural map data; the urban-rural planning text comprises a plurality of planning projects, and the urban-rural map data identifies a plurality of planning areas corresponding to the plurality of planning projects;

[0100] A project planning time acquisition module 102 is configured to perform text recognition on the plurality of planning projects to obtain a plurality of project planning times.

[0101] An urban-rural map data storage updating module 103 is configured to update the urban-rural map data of the corresponding planning area according to the plurality of project planning times.

[0102] It should be noted that the storage device 100 for urban-rural planning data provided by the second embodiment of the present application, when performing the storage method for urban-rural planning data, is only exemplified by the above division of functional modules. In actual applications, the above functions can be completed by different functional modules according to needs, i.e., the internal structure of the device is divided into different functional modules to complete all or part of the above-described functions. In addition, the storage device 100 for urban-rural planning data provided by the second embodiment of the present application and the storage method for urban-rural planning data provided by the first embodiment of the present application belong to the same concept, and the implementation process is described in detail in the method embodiment. Here, it is not repeated.

[0103] The third embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the storage method for urban-rural planning data as described above.

[0104] The fourth embodiment of the present application provides a computer device, which comprises a storage, a processor, and a computer program stored in the storage and executable by the processor, and the processor executes the computer program to implement the steps of the storage method for urban-rural planning data as described above.

[0105] The device embodiments described above are only illustrative, wherein the components illustrated as separate components can or can not be physically separate, and the components illustrated as units can or can not be physical units, i.e., can be located at one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the solutions of the present application according to actual needs. Those skilled in the art can understand and implement without creative effort.

[0106] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with selected functions of one or more flows and / or blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with selected functions of one or more flows and / or blocks.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to generate a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with selected functions of one or more flows and / or blocks.

[0109] In one typical arrangement, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0110] Memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. A memory can also include non-volatile memory, such as a read only memory (ROM), EPROM, EEPROM, or flash memory. Memory can further include a data storage 112, which can include a hard disk drive, solid state drive, or other data storage device. Memory can store data, including index data, and / or instructions (e.g., software) for execution by processing unit(s) of the computing device. Memory is an example of computer readable media.

[0111] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for the storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definitions herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0112] It should also be noted that the terms "comprising," "including," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus.

[0113] The above embodiments are only exemplary and are not intended to limit the present application. The present application can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A storage method for urban and rural planning data, characterized in that, The method comprises the following steps: acquiring stored urban-rural map planning data of a target region; the urban-rural map planning data comprises urban-rural planning texts and urban-rural map data; the urban-rural planning texts comprise a plurality of planning projects, and the urban-rural map data identifies a plurality of planning areas corresponding to the plurality of planning projects; performing text recognition on the plurality of planning projects to obtain a plurality of project planning times; updating the urban-rural map data of the corresponding planning areas according to the plurality of project planning times.

2. The storage method for urban and rural planning data according to claim 1, characterized in that, The step of performing text recognition on the plurality of planning projects to obtain a plurality of project planning times comprises the following steps: inputting the plurality of planning projects into a trained text semantic recognition model to obtain semantic texts of the respective planning projects; inputting the respective semantic texts into a trained planning time recognition model to obtain project planning times of the respective semantic texts.

3. The storage method for urban and rural planning data according to claim 2, characterized in that, The step of inputting the plurality of planning projects into a trained text semantic recognition model to obtain semantic texts of the respective planning projects comprises the following steps: training a plurality of initial semantic recognition models according to a plurality of text semantic recognition training samples to obtain a plurality of candidate semantic recognition models; testing and evaluating the plurality of candidate semantic recognition models according to a plurality of text semantic recognition test samples, and confirming the candidate semantic recognition model with the highest evaluation as the text semantic recognition model; inputting the plurality of planning projects into the trained text semantic recognition model to obtain the semantic texts of the respective planning projects.

4. The storage method for urban and rural planning data according to claim 2, characterized in that, The step of inputting the respective semantic texts into a trained planning time recognition model to obtain project planning times of the respective semantic texts comprises the following steps: training a plurality of initial planning time recognition models according to a plurality of project planning time recognition training samples to obtain a plurality of candidate planning time recognition models; testing and evaluating the plurality of candidate planning time recognition models according to a plurality of project planning time recognition test samples, and confirming the candidate planning time recognition model with the highest evaluation as the planning time recognition model; inputting the respective semantic texts into the trained planning time recognition model to obtain the project planning times of the respective semantic texts.

5. The storage method for urban and rural planning data according to claim 1, characterized in that, The step of updating the urban-rural map data of the corresponding planning areas according to the plurality of project planning times comprises the following steps: determining a planning project with a project planning time reaching a real-time time as a target planning project; determining a planning area corresponding to the target planning project as a target planning area, and updating the urban-rural map data of the target planning area.

6. The storage method for urban and rural planning data according to claim 5, characterized in that, The step of updating the urban-rural map data of the target planning area comprises the following steps: acquiring a region location and a region range of the target planning area; acquiring a region remote sensing image of the target planning area from a region remote sensing image of the target region according to the region location and the region range; updating the urban-rural map data of the target planning area according to the region remote sensing image.

7. The storage method for urban and rural planning data according to claim 5, characterized in that, The step of updating the urban-rural map data of the target planning area comprises the following steps: acquiring a region location and a region range of the target planning area; According to the region positioning and the region range, the unmanned aerial vehicle is driven to shoot a region map image of the target planning region; According to the region map image, urban and rural map data of the target planning region is updated.

8. A storage device for urban and rural planning data, characterized by, Comprise: An urban and rural map planning data acquisition module is configured to acquire stored urban and rural map planning data of a target region; The urban and rural map planning data comprises urban and rural planning texts and urban and rural map data; The urban and rural planning texts comprise a plurality of planning projects, and the urban and rural map data identifies a plurality of planning regions corresponding to the plurality of planning projects; A project planning time acquisition module is configured to perform text recognition on the plurality of planning projects to obtain a plurality of project planning times; An urban and rural map data storage updating module is configured to update the urban and rural map data of the corresponding planning regions according to the plurality of project planning times.

9. A computer-readable storage medium, the computer-readable storage medium storing a computer program, characterized in that: The computer program, when executed by a processor, implements the steps of the storage method for urban and rural planning data according to any one of claims 1 to 7.

10. A computer device, comprising: The computer program, when executed by a processor, implements the steps of the storage method for urban and rural planning data according to any one of claims 1 to 7.

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