Intelligent Planning and Design Method, System and Device for Landscape Areas Based on Data Analysis

Abstract

The present invention provides an intelligent planning and design method, system and device for a landscape area based on data analysis, which relates to the technical field of landscape planning. The method mainly includes: statistically analyzing the design templates of historical landscape areas to obtain the independent coefficients and occupied area values of the analysis templates; analyzing the characteristic critical values of historical landscape areas to obtain the dispersion critical value and the concentration critical value, constructing a characteristic discrimination range, and storing it in a database; according to the required occupied area value and the required function type of the required landscape area, performing template screening analysis, comparing the required occupied area value with the characteristic discrimination range, and marking the planning mode of the required landscape area; based on the planning mode, performing layout planning on the required landscape area to obtain a layout planning framework diagram. This solution can analyze and match the planning and design mode of the required landscape area by combining historical design data, so that the required landscape area can be planned and designed using a more suitable planning mode.

Description

Technical Field

[0001] The present invention relates to the technical field of landscape planning, and in particular to an intelligent planning and design method, system and device for a landscape area based on data analysis. Background Art

[0002] Landscape area planning and design refers to the landscape planning and design activities carried out within the scope of a region. Starting from the basic characteristics and attributes of the region, it aims to create a visual landscape image, environmental ecological greening, and a landscape environment that is coordinated with the public's behavior and psychology. Landscape planning and design not only focuses on visual aesthetics and viewing convenience, but also emphasizes the harmonious coexistence between humans and nature, and pays attention to environmental protection and resource conservation.

[0003] The invention patent application with the publication number CN113971299A and the name of a combined design method for garden landscape construction discloses a combined design method for garden landscape construction. This design method can use the global dynamic simulation function to learn, import relevant system data into the system interface, and thus form a new simulation view; it can completely display the garden landscape effect diagram in the software, and secondly, it can timely adjust the data of the garden landscape effect diagram. However, this design method cannot analyze and match the planning and design mode of the landscape area in combination with historical design data, resulting in the inability to improve the design rationality and environmental protection of the landscape area planning framework diagram macroscopically, thus affecting the design efficiency, effect and user satisfaction. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent planning and design method, system and device for a landscape area based on data analysis to solve at least one of the above technical problems existing in the prior art.

[0005] In a first aspect, to solve the above technical problems, the present invention provides an intelligent planning and design method for a landscape area based on data analysis, including the following steps:

[0006] Step 1: Statistically analyze the design templates of historical landscape areas: Mark the design templates of landscape areas whose construction time is more than a preset time from now as analysis templates; obtain the independent coefficient and the occupied area value of the analysis templates;

[0007] Step 2: Analyze the characteristic critical values of historical landscape areas: Mark the analysis templates through the independent coefficient to obtain dispersed templates and concentrated templates; form a dispersed set from the occupied area values of all dispersed templates, and form a concentrated set from the occupied area values of all concentrated templates; respectively perform cleaning processing on the dispersed set and the concentrated set to obtain a dispersed critical value and a concentrated critical value; use the dispersed critical value and the concentrated critical value as boundary values respectively to construct a characteristic discrimination range and store it in the database; this is convenient for improving the subsequent template screening and analysis efficiency;

[0008] Step 3. According to the required floor area value and required function type of the required landscape area, conduct template screening and analysis: retrieve the feature differentiation range through the database; compare the required floor area value with the feature differentiation range and mark the planning mode of the required landscape area; the planning mode includes a decentralized planning mode and a centralized planning mode.

[0009] Step 4. Based on the planning mode, conduct layout planning for the required landscape area to obtain a layout planning framework diagram.

[0010] Through the above method, based on the design scheme of the historical landscape area, it can be used for comparison and reference by the required landscape area, that is, taking into account both the resource utilization rate of the landscape area and the viewing convenience of users, which is conducive to efficiently, intelligently, and reasonably designing a landscape area planning scheme that meets the user's needs.

[0011] In a feasible implementation manner, the specific method for obtaining the independent coefficient and floor area value of the analysis template in step 1 includes:

[0012] Count the number of sub-regions of different function types in the analysis template, mark the number of sub-regions of the same function type as the dispersion value corresponding to the function type, mark the function type with a dispersion value of one as the independent type, and mark the ratio of the number of independent types to the number of function types as the independent coefficient of the analysis template; in this way, the planning tendency of the analysis template can be reflected through the independent coefficient, providing data support for subsequent feature critical value analysis.

[0013] Obtain the floor area value of the corresponding landscape area in the analysis template and mark it as the floor area value of the analysis template; this facilitates the subsequent construction of the feature differentiation range.

[0014] In a feasible implementation manner, the specific method for marking the analysis template in step 2 is:

[0015] Compare the independent coefficient of the analysis template with a preset independent threshold:

[0016] When the independent coefficient is less than the independent threshold, mark the corresponding analysis template as a decentralized template;

[0017] When the independent coefficient is greater than or equal to the independent threshold, mark the corresponding analysis template as a centralized template;

[0018] In this way, the analysis templates can be marked differently.

[0019] In a feasible implementation manner, the specific method for cleaning the decentralized set in step 2 is:

[0020] Step a1: Calculate the variance of all elements in the dispersed set to obtain the dispersion performance value of the dispersed set;

[0021] Step a2: Compare the dispersion performance value with a preset first critical performance threshold:

[0022] If the dispersion performance value is less than the first critical performance threshold, mark the minimum element of the dispersed set as the dispersion critical value;

[0023] If the dispersion performance value is greater than or equal to the first critical performance threshold, remove the maximum and minimum elements from the dispersed set, and then re - execute Step a1 until the dispersion critical value is obtained.

[0024] In a feasible implementation manner, the specific method for cleaning the concentrated set in Step 2 is:

[0025] Step b1: Calculate the variance of all elements in the concentrated set to obtain the concentration performance value of the concentrated set;

[0026] Step b2: Compare the concentration performance value with a preset second critical performance threshold:

[0027] If the concentration performance value is less than the second critical performance threshold, mark the minimum element of the concentrated set as the concentration critical value;

[0028] If the concentration performance value is greater than or equal to the second critical performance threshold, remove the maximum and minimum elements from the concentrated set, and then re - execute Step b1 until the concentration critical value is obtained.

[0029] In a feasible implementation manner, the specific method for comparing the required floor area value with the feature discrimination range and marking the planning mode of the required landscape area in Step 3 is:

[0030] If the required floor area value is less than the minimum boundary value of the feature discrimination range, mark it as the concentrated planning mode;

[0031] If the required floor area value is greater than the maximum boundary value of the feature discrimination range, mark it as the dispersed planning mode;

[0032] If the required floor area value is within the feature discrimination range, mark all analysis templates with floor area values within the feature discrimination range as screening templates, and obtain the priority coefficient YX of all screening templates; mark the screening template with the smallest YX value as the matching template: if the matching template is a dispersed template, mark it as the dispersed planning mode; if the matching template is a concentrated template, mark it as the concentrated planning mode.

[0033] In a feasible implementation manner, the specific method for obtaining the priority coefficient YX is as follows: Obtain the cycle data ZQ, maintenance data WH, and optimization data YH of the screening template and perform numerical calculations to obtain the priority coefficient YX;

[0034] Among them, the cycle data ZQ is the construction cycle duration value of the screening template; the maintenance data WH is the average single maintenance duration value of the landscape area corresponding to the screening template; the optimization data YH is the number of layout optimizations of the landscape area corresponding to the screening template.

[0035] In a feasible implementation manner, the decentralized planning mode includes:

[0036] Freely combine the demand function types to obtain several function demand groups; according to the topography and landform of the demand landscape area, divide the demand landscape area into several planning sub-areas, and make the number of planning sub-areas equal to the number of function demand groups; match the function demand groups with the planning sub-areas to obtain a layout planning framework diagram; in this mode, each function area is relatively dispersed and has a large repetition rate, which can improve the viewing convenience of the landscape area.

[0037] In a feasible implementation manner, the centralized planning mode includes:

[0038] According to the topography and landform of the demand landscape area, divide the demand landscape area into several planning sub-areas, so that the number of planning sub-areas is equal to the number of demand function types; match the demand function types with the planning sub-areas to obtain a layout planning framework diagram; in this mode, each function area is relatively concentrated and has a small repetition rate, which can improve the resource utilization rate of the landscape area.

[0039] In a second aspect, based on the same inventive concept, the present application also provides an intelligent planning and design system for a landscape area based on data analysis, including a data receiving module, a data processing module, and a result generating module;

[0040] The data receiving module is used to receive the design template of the historical landscape area, the required floor area value and the required function type of the required landscape area;

[0041] The data processing module includes a data statistics unit, a feature analysis unit, a template screening unit, a planning analysis unit, and a database;

[0042] The data statistics unit is used to perform statistical analysis on the design template of the historical landscape area: Mark the landscape area design template whose completion time is more than the preset time from now as the analysis template; obtain the independent coefficient and the face value of the analysis template;

[0043] The feature analysis unit is used to analyze the feature threshold values of the historical landscape area: mark the analysis template through the independence coefficient to obtain the dispersed template and the concentrated template; form a dispersed set from the occupied area values of all the dispersed templates, and form a concentrated set from the occupied area values of all the concentrated templates; perform cleaning processing on the dispersed set and the concentrated set respectively to obtain the dispersed threshold value and the concentrated threshold value; use the dispersed threshold value and the concentrated threshold value as boundary values respectively to construct a feature discrimination range and store it in the database;

[0044] The template screening unit is used to perform template screening analysis according to the required occupied area value and the required function type of the required landscape area: retrieve the feature discrimination range through the database; compare the required occupied area value with the feature discrimination range and mark the planning mode of the required landscape area; the planning mode includes a dispersed planning mode and a concentrated planning mode;

[0045] The planning analysis unit is based on the planning mode to perform layout planning on the required landscape area to obtain a layout planning framework diagram;

[0046] The result generation module is used to send out the layout planning framework diagram.

[0047] In a third aspect, based on the same inventive concept, the present application also provides an intelligent planning and design device for a landscape area based on data analysis, including a processor, a memory, and a bus. The memory stores instructions and data read by the processor, and the processor is used to call the instructions and data in the memory to execute the above-mentioned intelligent planning and design method for a landscape area based on data analysis. The bus is connected between each functional component for transmitting information.

[0048] Adopting the above technical solutions, the present invention has the following beneficial effects:

[0049] An intelligent planning and design method, system, and device for a landscape area based on data analysis provided by the present invention can analyze and match the planning design mode of the required landscape area in combination with historical design data; provide data support for feature threshold value analysis by feeding back the planning tendency of the analysis template through the independence coefficient; screen the feature threshold value by means of cleaning processing, and the feature discrimination range composed of the dispersed threshold value and the concentrated threshold value can significantly improve the template screening efficiency; through the method of template screening and matching, the required landscape area can be planned and designed by adopting a more suitable planning mode; the layout planning framework diagrams generated by the two planning modes have their own characteristics, and their optimization directions are consistent with the required characteristics of the landscape area, thereby improving user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0051] Figure 1 Flowchart of an intelligent planning and design method for a landscape area based on data analysis provided by an embodiment of the present invention;

[0052] Figure 2 Flowchart of the specific method for cleaning and processing the dispersed set in step 2 provided by an embodiment of the present invention;

[0053] Figure 3 Flowchart of the specific method for cleaning and processing the concentrated set in step 2 provided by an embodiment of the present invention;

[0054] Figure 4 Schematic diagram of the planned zoning of the contour map of the required landscape area provided by an embodiment of the present invention;

[0055] Figure 5 System diagram of an intelligent planning and design for a landscape area based on data analysis provided by an embodiment of the present invention;

[0056] Reference numerals:

[0057] 1 - First planning zone; 2 - Second planning zone; 3 - Third planning zone; 4 - Fourth planning zone; 5 - Fifth planning zone; 6 - Sixth planning zone. Detailed embodiments

[0058] The following will clearly and completely describe the technical solutions of the present invention with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0059] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0060] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0061] The following further explains and illustrates the present invention in conjunction with specific embodiments.

[0062] It should also be noted that the following specific embodiments or specific implementation manners are a series of optimized setting manners listed by the present invention to further explain the specific invention content, and these setting manners can be combined with each other or used in association with each other.

[0063] Embodiment 1:

[0064] As Figure 1 shown, a method for intelligent planning and design of a landscape area based on data analysis provided in this embodiment includes the following steps:

[0065] Step 1: Statistically analyze the design templates of historical landscape areas: Mark the design templates of landscape areas that have been built for more than L1 (numerical constant) months from the present as analysis templates; obtain the independent coefficient and the occupied area value of the analysis templates.

[0066] Step 2: Analyze the characteristic critical values of historical landscape areas: Mark the analysis templates through the independent coefficient to obtain scattered templates and concentrated templates; form a scattered set from the occupied area values of all scattered templates, and form a concentrated set from the occupied area values of all concentrated templates; perform cleaning processing on the scattered set and the concentrated set respectively to obtain a scattered critical value a and a concentrated critical value b; use the scattered critical value a and the concentrated critical value b as boundary values respectively to construct a characteristic discrimination range (a, b) and store it in the database; this is convenient for improving the subsequent template screening and analysis efficiency.

[0067] Step 3: Perform template screening and analysis according to the required occupied area value and the required function type of the required landscape area: Retrieve the characteristic discrimination range through the database; compare the required occupied area value with the characteristic discrimination range and mark the planning mode of the required landscape area; the planning mode includes a scattered planning mode and a concentrated planning mode.

[0068] Step 4: Based on the planning mode, perform layout planning on the required landscape area to obtain a layout planning framework diagram.

[0069] Through the above method, the design scheme of the historical landscape area can be used for comparison and reference by the required landscape area, which takes into account both the resource utilization rate of the landscape area and the convenience of users' viewing, and is conducive to efficiently, intelligently and reasonably designing a landscape area planning scheme that meets the needs of users.

[0070] Further, the specific method for obtaining the independent coefficient and the occupied area value of the analysis template in step 1 includes:

[0071] Count the number of sub-regions of different functional types in the analysis template, mark the number of sub-regions of the same functional type as the dispersion value corresponding to the functional type, mark the functional type with a dispersion value of one as the independent type, and mark the ratio of the number of independent types to the number of functional types as the independent coefficient of the analysis template; in this way, the planning tendency of the analysis template can be reflected through the independent coefficient, providing data support for the subsequent analysis of the feature critical value.

[0072] Obtain the floor area value of the corresponding landscape area in the analysis template and mark it as the occupied area value of the analysis template; this is convenient for constructing the subsequent feature differentiation range.

[0073] Further, the specific method for marking the analysis template in step 2 is:

[0074] Compare the independent coefficient of the analysis template with a preset independent threshold:

[0075] When the independent coefficient is less than the independent threshold, mark the corresponding analysis template as a dispersed template;

[0076] When the independent coefficient is greater than or equal to the independent threshold, mark the corresponding analysis template as a concentrated template;

[0077] In this way, the analysis templates can be marked differently.

[0078] Further, as Figure 2 shown, the specific method for cleaning the dispersed set in step 2 is:

[0079] Step a1: Calculate the variance of all elements in the dispersed set to obtain the dispersion performance value of the dispersed set; that is, use the common variance calculation formula to calculate the variance value of all elements in the dispersed set, and take the variance value as the dispersion performance value.

[0080] Step a2: Compare the dispersion performance value with a preset first critical performance threshold:

[0081] If the dispersion performance value is less than the first critical performance threshold, mark the smallest element of the dispersed set as the dispersion critical value;

[0082] If the dispersion performance value is greater than or equal to the first critical performance threshold, then the maximum and minimum elements in the dispersion set are removed, and then step a1 is executed again until the dispersion critical value is obtained.

[0083] Further, as Figure 3 shown, the specific method for cleaning the centralized set in step 2 is:

[0084] Step b1: Calculate the variance of all elements in the centralized set to obtain the centralized performance value of the centralized set; that is, use the common variance calculation formula to calculate the variance value of all elements in the centralized set, and take the variance value as the centralized performance value;

[0085] Step b2: Compare the centralized performance value with the preset second critical performance threshold:

[0086] If the centralized performance value is less than the second critical performance threshold, then mark the minimum element of the centralized set as the centralized critical value;

[0087] If the centralized performance value is greater than or equal to the second critical performance threshold, then remove the maximum and minimum elements in the centralized set, and then execute step b1 again until the centralized critical value is obtained.

[0088] Further, the specific method for comparing the required floor area value with the feature discrimination range and marking the planning mode of the required landscape area in step 3 is:

[0089] If the required floor area value is less than the minimum boundary value of the feature discrimination range, it is marked as the centralized planning mode;

[0090] If the required floor area value is greater than the maximum boundary value of the feature discrimination range, it is marked as the dispersed planning mode;

[0091] If the required floor area value is within the feature discrimination range, mark all analysis templates with floor area values within the feature discrimination range as screening templates, and obtain the priority coefficient YX of all screening templates; mark the screening template with the smallest priority coefficient YX as the matching template: if the matching template is a dispersed template, it is marked as the dispersed planning mode; if the matching template is a centralized template, it is marked as the centralized planning mode.

[0092] Further, the specific method for obtaining the priority coefficient YX is: obtain the cycle data ZQ, maintenance data WH, and optimization data YH of the screening template and perform numerical calculations to obtain the priority coefficient YX. The specific formula is: YX = k1×ZQ + k2×WH + k3×YH;

[0093] Among them, the periodic data ZQ is the construction period duration value of the screening template; the maintenance data WH is the average single maintenance duration value of the corresponding landscape area of the screening template; the optimization data YH is the number of layout optimizations of the corresponding landscape area of the screening template; k1, k2, and k3 are all proportionality coefficients, and k1 > k2 > k3 > 1;

[0094] Particularly, after collecting multiple groups of sample data and setting corresponding priority coefficients YX for each group of sample data; substituting the set priority coefficients YX and the collected sample data into the above formulas, any three formulas form a system of linear equations with three variables. After screening and taking the average of the calculated coefficients, the values of k1, k2, and k3 are obtained as 5.28, 3.47, and 2.21 respectively;

[0095] It should be noted that the magnitudes of the proportionality coefficients are specific values obtained by quantifying each parameter for subsequent comparison. Regarding the magnitudes of the proportionality coefficients, they depend on the quantity of the sample data and the initially set priority coefficients for each group of sample data, as long as the proportional relationship between the parameters and the quantified values is not affected. For example, the magnitude of the proportionality coefficient is directly proportional to the value of the periodic data.

[0096] Furthermore, the decentralized planning mode includes:

[0097] Freely combine (randomly combine) the demand function types (such as entrance and exit functions, entertainment functions, viewing functions, ecological protection functions, and service functions, etc.) to obtain several function demand groups; according to the topography and geomorphology of the demand landscape area, divide the demand landscape area into several planning sub - areas, and make the number of planning sub - areas equal to the number of function demand groups; match the function demand groups with the planning sub - areas to obtain a layout planning framework diagram. In this mode, each functional area is relatively dispersed and has a large degree of repetition, which can improve the viewing convenience of the landscape area.

[0098] Furthermore, the centralized planning mode includes:

[0099] According to the topography and geomorphology of the demand landscape area, divide the demand landscape area into several planning sub - areas, making the number of planning sub - areas equal to the number of demand function types; match the demand function types with the planning sub - areas to obtain a layout planning framework diagram. In this mode, each functional area is relatively concentrated and has a small degree of repetition, which can improve the resource utilization rate of the landscape area.

[0100] Furthermore, as Figure 4 shown, the specific method of dividing the demand landscape area into several planning sub - areas according to the topography and geomorphology of the demand landscape area includes: based on the contour map of the demand landscape area, set several interest point coordinates (i.e., the longitude and latitude of the center of the scenic spot, such as Figure 4at the solid dot in); use the closed range of the smallest contour line where the coordinates of the point of interest are located as the first planning sub - area 1 to cover each scenic spot; connect adjacent coordinates of the points of interest with a straight line, use the straight line as the center line of the first passage, and after adding the first preset passage width, use it as the second planning sub - area 2 to achieve short - distance interconnection between each scenic spot; for several coordinates of the points of interest closest to the first contour line (or the overall contour line of the required landscape area), draw the shortest perpendicular line to the first contour line, use the shortest perpendicular line as the center line of the second passage, and after adding the second preset passage width, use it as the third planning sub - area 3 to achieve short - distance interconnection between the required landscape area and the outside world; use the common intersection area among the first planning sub - area 1, the second planning sub - area 2, and the third planning sub - area 3 as the fourth planning sub - area 4, so as to obtain several core areas where more people may gather; in the first planning sub - area 1, the second planning sub - area 2, and the third planning sub - area 3, extract the intersection area between any two of them, and after removing the fourth planning sub - area 4, obtain the fifth planning sub - area 5, so as to obtain several sub - core areas; after removing the fourth planning sub - area 4 and the fifth planning sub - area 5 from the union area among the first planning sub - area 1, the second planning sub - area 2, and the third planning sub - area 3, obtain several sixth planning sub - areas 6; sort and number the fourth planning sub - area 4, the fifth planning sub - area 5, and the sixth planning sub - areas 6 according to the planning sub - area serial number and area size, and put them into the planning sub - area set together for subsequent processing; in this way, the required landscape area can be automatically divided into several planning sub - areas, and the preliminary landscape area planning is basically realized quickly and simply.

[0101] Embodiment 2:

[0102] As Figure 5 shown, this embodiment provides an intelligent planning and design system for landscape areas based on data analysis, including a data receiving module, a data processing module, and a result generating module;

[0103] The data receiving module is used to receive the design template of the historical landscape area, the required floor area value and the required function type of the required landscape area;

[0104] The data processing module includes a data statistics unit, a feature analysis unit, a template screening unit, a planning analysis unit, and a database;

[0105] The data statistics unit is used to perform statistical analysis on the design template of the historical landscape area: mark the design template of the landscape area that has been completed for more than L1 months from the current time as the analysis template; obtain the independent coefficient and the floor area value of the analysis template;

[0106] The feature analysis unit is used to analyze the feature threshold values of historical landscape areas: by using the independence coefficient, mark the analysis templates to obtain the dispersed templates and the concentrated templates; form a dispersed set from the occupied area values of all the dispersed templates, and form a concentrated set from the occupied area values of all the concentrated templates; perform cleaning processing on the dispersed set and the concentrated set respectively to obtain the dispersed threshold value and the concentrated threshold value; use the dispersed threshold value and the concentrated threshold value as boundary values respectively to construct a feature discrimination range and store it in the database;

[0107] The template screening unit is used to perform template screening analysis according to the required occupied area value and the required function type of the required landscape area: retrieve the feature discrimination range through the database; compare the required occupied area value with the feature discrimination range and mark the planning mode of the required landscape area; the planning mode includes the dispersed planning mode and the concentrated planning mode;

[0108] The planning analysis unit, based on the planning mode, performs layout planning on the required landscape area to obtain a layout planning framework diagram;

[0109] The result generation module is used to send out the layout planning framework diagram.

[0110] Embodiment 3:

[0111] This embodiment provides an intelligent planning and design device for landscape areas based on data analysis, including a processor, a memory, and a bus. The memory stores instructions and data read by the processor. The processor is used to call the instructions and data in the memory to execute the above-mentioned intelligent planning and design method for landscape areas based on data analysis. The bus is connected between each functional component for transmitting information.

[0112] In another implementation manner of this solution, it can be implemented in the form of an integrated device, and the device can include corresponding modules that execute each or several steps in the above-mentioned various embodiments. The module can be one or more hardware modules specifically configured to execute the corresponding steps, or implemented by a processor configured to execute the corresponding steps, or stored in a computer-readable medium for implementation by the processor, or implemented through a certain combination.

[0113] The processor executes the various methods and processes described above. For example, the method embodiments in this solution can be implemented as a software program that is tangibly included in a machine-readable medium, such as a memory. In some embodiments, part or all of the software program can be loaded and / or installed via the memory and / or communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps of the methods described above can be executed. Alternatively, in other embodiments, the processor can be configured to execute one of the above methods in any other suitable manner (e.g., by means of firmware).

[0114] The device can be implemented using a bus architecture. The bus architecture can include any number of interconnected buses and bridges, depending on the specific application of the hardware and overall design constraints. The bus connects various circuits including one or more processors, memories, and / or hardware modules together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, power management circuits, external antennas, etc.

[0115] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Component (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0116] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A landscape area intelligent planning and design method based on data analysis, characterized in that: include: Step 1: Statistically analyze the design templates of the historical landscape area: mark the design templates of the landscape area that were built more than a preset time ago as analysis templates; Get the independent coefficient and the area value of the analysis template; Step 2: Analyze the critical value of the characteristic of the historical landscape area: mark the analysis template through the independent coefficient to obtain the dispersed template and the concentrated template; The area values ​​of all dispersed templates constitute a dispersed set, and the area values ​​of all concentrated templates constitute a concentrated set; The dispersed set and the concentrated set are cleaned separately to obtain the dispersed critical value and the concentrated critical value; The dispersion critical value and the concentration critical value are used as boundary values ​​to construct the feature distinction range and store it in the database; Step 3: Perform template screening and analysis based on the required land area value and required functional type of the required landscape area: retrieve the feature distinction range through the database; Compare the required area value with the characteristic distinction range and mark the planning mode of the required landscape area; the planning mode includes a decentralized planning mode and a centralized planning mode; Step 4: Based on the planning model, layout planning is performed on the required landscape area to obtain a layout planning framework diagram; The specific method for obtaining the independent coefficient and the par value of the analysis template in step 1 includes: The number of sub-regions of different functional types in the analysis template is counted, the number of sub-regions of the same functional type is marked as the dispersion value of the corresponding functional type, the functional type with a dispersion value of one is marked as an independent type, and the ratio of the number of independent types to the number of functional types is marked as the independence coefficient of the analysis template; Obtain the area value of the corresponding landscape area in the analysis template and mark it as the area value of the analysis template; The specific method for marking the analysis template in step 2 is: Compare the independence coefficient of the analysis template with the preset independence threshold: When the independence coefficient is less than the independence threshold, the corresponding analysis template is marked as a dispersed template; When the independence coefficient is greater than or equal to the independence threshold, the corresponding analysis template is marked as a centralized template; The specific method of comparing the required land area value with the characteristic distinction range and marking the planning mode of the required landscape area in step 3 is: If the required floor area value is less than the minimum boundary value of the feature distinction range, it is marked as centralized planning mode; If the required area value is greater than the maximum boundary value of the feature distinction range, it is marked as a decentralized planning mode; If the required floor area value is within the feature differentiation range, all analysis templates whose floor area values ​​are within the feature differentiation range are marked as screening templates, and the priority coefficients of all screening templates are obtained; the screening template with the smallest priority coefficient is marked as a matching template: if the matching template is a dispersed template, it is marked as a dispersed planning mode; if the matching template is a centralized template, it is marked as a centralized planning mode; The specific method for obtaining the priority coefficient is: obtaining the periodic data ZQ, maintenance data WH and optimization data YH of the screening template and performing numerical calculations to obtain the priority coefficient YX, and the specific formula is: YX=k1×ZQ+k2×WH+k3×YH; Among them, the cycle data ZQ is the construction cycle duration value of the screening template; the maintenance data WH is the average single maintenance duration value of the landscape area corresponding to the screening template; the optimization data YH is the number of layout optimizations of the landscape area corresponding to the screening template; k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1.

2. The method according to claim 1, characterized in that The specific method for cleaning the scattered collection in step 2 is: Step a1, calculate the variance of all elements in the scattered set to obtain the scattered performance value of the scattered set; Step a2: Compare the dispersion performance value with a preset first critical performance threshold: If the dispersion performance value is less than the first critical performance threshold, the minimum element of the dispersion set is marked as the dispersion critical value; If the dispersion performance value is greater than or equal to the first critical performance threshold, the maximum element and the minimum element in the dispersion set are removed, and then step a1 is executed again until the dispersion critical value is obtained.

3. The method according to claim 1, characterized in that The specific method for cleaning the centralized collection in step 2 is: Step b1, performing variance calculation on all elements in the concentrated set to obtain the concentrated performance value of the concentrated set; Step b2: Compare the concentration performance value with a preset second critical performance threshold: If the concentrated representation value is less than the second critical representation threshold, marking the minimum element of the concentrated set as the concentrated critical value; If the concentrated performance value is greater than or equal to the second critical performance threshold, the maximum element and the minimum element in the concentrated set are removed, and then step b1 is re-executed until the concentrated critical value is obtained.

4. The method according to claim 1, characterized in that The decentralized planning mode includes: freely combining the required functional types to obtain a plurality of functional demand groups; dividing the required landscape area into a plurality of planning zones according to the topography of the required landscape area, and making the number of planning zones equal to the number of functional demand groups; matching the functional demand groups with the planning zones to obtain a layout planning framework diagram; The centralized planning mode includes: dividing the required landscape area into a number of planning zones according to the topography of the required landscape area, so that the number of planning zones is equal to the number of required functional types; matching the required functional types with the planning zones to obtain a layout planning framework diagram.

5. A landscape area intelligent planning and design system based on data analysis using the method as described in any one of claims 1 to 4, characterized in that: It includes a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive the design template of the historical landscape area, the required area value and the required function type of the required landscape area; The data processing module includes a data statistics unit, a feature analysis unit, a template screening unit, a planning analysis unit and a database; The data statistics unit is used to perform statistical analysis on the design templates of the historical landscape area: the design templates of the landscape area that were built more than a preset time ago are marked as analysis templates; Get the independent coefficient and the area value of the analysis template; The characteristic analysis unit is used to analyze the characteristic critical value of the historical landscape area: the analysis template is marked by the independent coefficient to obtain the dispersed template and the concentrated template; The area values ​​of all dispersed templates constitute a dispersed set, and the area values ​​of all concentrated templates constitute a concentrated set; The dispersed set and the concentrated set are cleaned separately to obtain the dispersed critical value and the concentrated critical value; The dispersion critical value and the concentration critical value are used as boundary values ​​to construct the feature distinction range and store it in the database; The template screening unit is used to perform template screening analysis according to the required land area value and required functional type of the required landscape area: retrieve the feature distinction range through the database; Compare the required area value with the characteristic distinction range and mark the planning mode of the required landscape area; the planning mode includes a decentralized planning mode and a centralized planning mode; The planning analysis unit performs layout planning on the required landscape area based on the planning model to obtain a layout planning framework diagram; The result generation module is used to send out the layout planning framework diagram.

6. A landscape area intelligent planning and design device based on data analysis, characterized in that: It includes a processor, a memory and a bus, wherein the memory stores instructions and data read by the processor, the processor is used to call the instructions and data in the memory to execute the method described in any one of claims 1-4, and the bus connects the functional components for transmitting information.

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