Landscape area intelligent planning design method, system and device based on data analysis

Abstract

The invention provides a landscape area intelligent planning design method, system and device based on data analysis, and relates to the technical field of landscape planning, and the method mainly comprises the steps: carrying out the statistical analysis of a design template of a historical landscape area, and obtaining an independent coefficient and an occupied area value of the analysis template; analyzing the feature critical value of the historical landscape area to obtain a dispersed critical value and a concentrated critical value, constructing a feature distinguishing range, and storing the feature distinguishing range in a database; performing template screening analysis according to the required occupied area value and the required function type of the required landscape area, comparing the required occupied area value with the feature distinguishing range, and marking a planning mode of the required landscape area; and based on the planning mode, performing layout planning on the required landscape area to obtain a layout planning framework diagram. According to the scheme, planning and design mode analysis and matching can be performed on the required landscape area in combination with historical design data, so that the required landscape area is planned and designed in a more suitable planning mode.

Description

Technical Field

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

[0002] Landscape regional planning and design refers to landscape planning and design activities carried out within the scope of a region. It starts from the basic characteristics and attributes of the region and aims to create a landscape environment with visual landscape image, environmental ecological greening and 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 man and nature, and pays attention to protecting the environment and saving resources.

[0003] The invention patent application with the announcement number CN113971299A, entitled A Garden Landscape Construction Combination Design Method, discloses a garden landscape construction combination design method, which can use the global dynamic simulation function for learning, import relevant system data in the system interface, and thus form a new simulation view; the garden landscape rendering can be fully displayed in the software, and the garden landscape rendering data can be adjusted in a timely manner. However, the design method cannot combine historical design data to analyze and match the planning and design mode of the landscape area, resulting in the inability to improve the design rationality and environmental protection of the landscape area planning framework map from a macro perspective, thereby affecting the design efficiency, effect and user satisfaction. Summary of the invention

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

[0005] In the first aspect, in order to solve the above technical problems, the present invention provides a landscape area intelligent planning and design method based on data analysis, comprising the following steps: Step 1: Statistically analyze the design templates of the historical landscape area: mark the design templates of the landscape area that was built more than a preset time ago as analysis templates; obtain the independent coefficient and the area value of the analysis template; Step 2, analyzing the characteristic critical value of the historical landscape area: marking the analysis template through the independent coefficient to obtain the dispersed template and the concentrated template; forming a dispersed set by the surface values ​​of all dispersed templates, and forming a concentrated set by the surface values ​​of all concentrated templates; cleaning the dispersed set and the concentrated set, respectively, to obtain the dispersed critical value and the concentrated critical value; using the dispersed critical value and the concentrated critical value as the boundary value, respectively, to construct the characteristic distinction range, and store it in the database; this is convenient to improve the efficiency of subsequent template screening and analysis; Step 3: Perform template screening and analysis based on the required land area value and required function type of the required landscape area: retrieve the feature distinction range through the database; compare the required land area value with the feature 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.

[0006] Through the above method, the design plan based on the historical landscape area can be used for comparison and reference for the landscape area in demand, which takes into account both the resource utilization rate of the landscape area and the convenience of user viewing, and is conducive to the efficient, intelligent and reasonable design of landscape area planning plans that meet user needs.

[0007] In a feasible implementation manner, the specific method for obtaining the independent coefficient and the face value of the analysis template in step 1 includes: The number of sub-regions of different functional types in the analysis template is counted, and the number of sub-regions of the same functional type is marked as the dispersion value of the corresponding functional type, and the functional type with a dispersion value of one is marked as an independent type. The ratio of the number of independent types to the number of functional types is marked as the independent coefficient of the analysis template; in this way, the planning tendency of the analysis template can be fed back through the independent coefficient, providing data support for the subsequent feature critical value analysis; Get the area value of the corresponding landscape area in the analysis template and mark it as the area value of the analysis template; this will facilitate the subsequent construction of feature differentiation ranges.

[0008] In a feasible implementation manner, 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; This allows analysis templates to be differentially labeled.

[0009] In a feasible implementation manner, the specific method for cleaning the dispersed set 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.

[0010] In a feasible implementation manner, 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.

[0011] In a feasible implementation manner, 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 distinction range, all analysis templates whose floor area values ​​are within the feature distinction range are marked as screening templates, and the priority coefficient YX of all screening templates is obtained; the screening template with the smallest priority coefficient YX value 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.

[0012] In a feasible implementation manner, the specific method for obtaining the priority coefficient YX is: obtaining the periodic data ZQ, the maintenance data WH and the optimization data YH of the screening template and performing numerical calculations to obtain the priority coefficient YX; 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; and the optimization data YH is the number of layout optimization times of the landscape area corresponding to the screening template.

[0013] In a feasible implementation, the decentralized planning mode includes: The required functional types are freely combined to obtain several functional demand groups; according to the topography of the required landscape area, the required landscape area is divided into several planning zones, and the number of planning zones is equal to the number of functional demand groups; the functional demand groups are matched with the planning zones to obtain a layout planning framework diagram; in this mode, the functional areas are relatively dispersed and have high repetitiveness, which can improve the viewing convenience of the landscape area.

[0014] In a feasible implementation manner, the centralized planning mode includes: According to the topography of the required landscape area, the required landscape area is divided into several planning zones, so that the number of planning zones is equal to the number of required functional types; the required functional types are matched with the planning zones to obtain a layout planning framework diagram; in this model, the functional areas are relatively concentrated with little duplication, which can improve the resource utilization rate of the landscape area.

[0015] In the second aspect, based on the same inventive concept, the present application also provides a landscape area intelligent planning and design system based on data analysis, including 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: marking the design templates of the landscape area that were built more than a preset time ago as analysis templates; obtaining the independent coefficient and the area value of the analysis template; The feature analysis unit is used to analyze the feature critical value of the historical landscape area: mark the analysis template through the independent coefficient to obtain a dispersed template and a concentrated template; form a dispersed set by the surface values ​​of all dispersed templates, and form a concentrated set by the surface values ​​of all concentrated templates; clean the dispersed set and the concentrated set respectively 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 feature distinction range, and store it in a database; The template screening unit is used to perform template screening analysis according to the required land area value and the required function type of the required landscape area: retrieve the feature distinction range through the database; compare the required land area value with the feature 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.

[0016] In the third aspect, based on the same inventive concept, the present application also provides a landscape area intelligent planning and design device 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 landscape area intelligent planning and design method based on data analysis as described above, and the bus connects the functional components for transmitting information.

[0017] By adopting the above technical solution, the present invention has the following beneficial effects: The present invention provides a method, system and device for intelligent planning and design of landscape areas based on data analysis, which can analyze and match the planning and design patterns of demand landscape areas in combination with historical design data; provide feedback on the planning tendency of the analysis template through independent coefficients to provide data support for feature critical value analysis; screen feature critical values ​​through cleaning processing, and the feature distinction range composed of dispersed critical values ​​and concentrated critical values ​​can significantly improve the template screening efficiency; through template screening and matching, the demand landscape area can adopt a more suitable planning model for planning and design processing; the layout planning framework diagrams generated by the two planning models have their own characteristics, and their optimization directions are consistent with the demand characteristics of the landscape area, thereby improving user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flow chart of a landscape area intelligent planning and design method based on data analysis provided by an embodiment of the present invention; Figure 2 A flow chart of a specific method for cleaning a dispersed set in step 2 provided in an embodiment of the present invention; Figure 3 A flowchart of a specific method for cleaning the centralized collection in step 2 provided in an embodiment of the present invention; Figure 4 A schematic diagram of planning zoning of a contour map of a required landscape area provided in an embodiment of the present invention; Figure 5 A landscape area intelligent planning and design system diagram based on data analysis provided by an embodiment of the present invention; Reference numerals: 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 DESCRIPTION

[0020] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are 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 therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0022] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" 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 it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] The present invention is further explained below in conjunction with specific implementation modes.

[0024] It should also be noted that the following specific embodiments or specific implementations are a series of optimized settings listed in the present invention to further explain the specific content of the invention, and these settings can be used in combination or in association with each other.

[0025] Embodiment 1: like Figure 1 As shown, this embodiment provides a landscape area intelligent planning and design method based on data analysis, including the following steps: Step 1: Statistically analyze the design templates of the historical landscape area: mark the design templates of the landscape area that was built more than L1 (numerical constant) months ago as analysis templates; obtain the independent coefficient and area value of the analysis template; Step 2, analyze the characteristic critical value of the historical landscape area: mark the analysis template through the independent coefficient to obtain the dispersed template and the concentrated template; the dispersed set is composed of the surface values ​​of all the dispersed templates, and the concentrated set is composed of the surface values ​​of all the concentrated templates; the dispersed set and the concentrated set are cleaned separately to obtain the dispersed critical value a and the concentrated critical value b; the dispersed critical value a and the concentrated critical value b are used as boundary values, respectively, to construct the characteristic distinction range (a, b), and store it in the database; this is convenient to improve the efficiency of subsequent template screening and analysis; Step 3: Perform template screening and analysis based on the required land area value and required function type of the required landscape area: retrieve the feature distinction range through the database; compare the required land area value with the feature 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.

[0026] Through the above method, the design plan based on the historical landscape area can be used for comparison and reference for the landscape area in demand, which takes into account both the resource utilization rate of the landscape area and the convenience of user viewing, and is conducive to the efficient, intelligent and reasonable design of landscape area planning plans that meet user needs.

[0027] Furthermore, the specific method for obtaining the independent coefficient and the face value of the analysis template in step 1 includes: The number of sub-regions of different functional types in the analysis template is counted, and the number of sub-regions of the same functional type is marked as the dispersion value of the corresponding functional type, and the functional type with a dispersion value of one is marked as an independent type. The ratio of the number of independent types to the number of functional types is marked as the independent coefficient of the analysis template; in this way, the planning tendency of the analysis template can be fed back through the independent coefficient, providing data support for the subsequent feature critical value analysis; Get the area value of the corresponding landscape area in the analysis template and mark it as the area value of the analysis template; this facilitates the subsequent construction of feature differentiation ranges.

[0028] Furthermore, 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; This allows analysis templates to be differentially labeled.

[0029] Furthermore, if Figure 2 As shown, the specific method for cleaning the scattered set in step 2 is: Step a1, performing variance calculation on all elements in the scattered set to obtain the scattered performance value of the scattered set; that is, using a common variance calculation formula to calculate the variance values ​​of all elements in the scattered set, and using the variance values ​​as the scattered performance value; 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.

[0030] Furthermore, if Figure 3 As shown, 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 a concentrated performance value of the concentrated set; that is, using a common variance calculation formula to calculate the variance values ​​of all elements in the concentrated set, and using the variance values ​​as the concentrated performance value; 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.

[0031] Furthermore, in step 3, the specific method of comparing the required area value with the characteristic distinction range and marking the planning mode of the required landscape area 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 distinction range, all analysis templates whose floor area values ​​are within the feature distinction range are marked as screening templates, and the priority coefficient YX of all screening templates is obtained; the screening template with the smallest priority coefficient YX value 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.

[0032] Furthermore, the specific method for obtaining the priority coefficient YX 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 layout optimization times of the landscape area corresponding to the screening template; k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1; In particular, after collecting multiple groups of sample data and setting a corresponding priority coefficient YX for each group of sample data; substituting the set priority coefficient YX and the collected sample data into the above formula, any three formulas constitute a three-variable linear equation system, and the calculated coefficients are screened and averaged to obtain the values ​​of k1, k2 and k3, which are 5.28, 3.47 and 2.21 respectively; It should be noted that the size of the proportional coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the proportional coefficient depends on the number of sample data and the priority coefficient initially set for each group of sample data. As long as it does not affect the proportional relationship between the parameter and the quantized value, such as the proportional coefficient is proportional to the value of the periodic data.

[0033] Furthermore, the decentralized planning mode includes: The required functional types (such as entrance and exit functions, entertainment functions, viewing functions, ecological protection functions, and service functions, etc.) are freely combined (randomly combined) to obtain several functional demand groups; according to the topography of the required landscape area, the required landscape area is divided into several planning zones, and the number of planning zones is equal to the number of functional demand groups; the functional demand groups are matched with the planning zones to obtain a layout planning framework diagram; in this model, the functional areas are relatively dispersed and have high repetition, which can improve the viewing convenience of the landscape area.

[0034] Furthermore, the centralized planning mode includes: According to the topography of the required landscape area, the required landscape area is divided into several planning zones, so that the number of planning zones is equal to the number of required functional types; the required functional types are matched with the planning zones to obtain a layout planning framework diagram; in this model, the functional areas are relatively concentrated with little duplication, which can improve the resource utilization rate of the landscape area.

[0035] Furthermore, if Figure 4As shown in FIG, according to the topography of the required landscape area, the specific method of dividing the required landscape area into several planning zones includes: based on the contour map of the required landscape area, setting the coordinates of several points of interest (i.e., the longitude and latitude of the center of the scenic spot, such as Figure 4 The solid circle in the figure); the minimum closed range of the calculated curve where the coordinates of the points of interest are located is taken as the first planning zone 1 to cover all scenic spots; the coordinates of adjacent points of interest are connected with a straight line, and the straight line is taken as the center line of the first channel. After adding the first preset channel width, it is taken as the second planning zone 2 to achieve close communication between the scenic spots; the coordinates of several points of interest closest to the first curve (or the overall contour line of the required landscape area) are used to make the shortest perpendicular line to the first curve, and the shortest perpendicular line is taken as the center line of the second channel. After adding the second preset channel width, it is taken as the third planning zone 3 to achieve close communication between the required landscape area and the outside world; the common intersection area between the first planning zone 1, the second planning zone 2 and the third planning zone 3 is taken as the fourth planning zone 4, so that several possible concentrated areas are obtained. The core area of ​​pedestrian flow; in the first planning zone 1, the second planning zone 2 and the third planning zone 3, the intersection area between any two is extracted, and after removing the fourth planning zone 4, the fifth planning zone 5 is obtained, so that several secondary core areas are obtained; in the union area between the first planning zone 1, the second planning zone 2 and the third planning zone 3, after removing the fourth planning zone 4 and the fifth planning zone 5, several sixth planning zones 6 are obtained; the fourth planning zone 4, the fifth planning zone 5 and the sixth planning zone 6 are sorted and numbered according to the planning zone sequence number and area size, and put into the planning zone set together for subsequent processing; in this way, the required landscape area can be automatically divided into several planning zones, and the preliminary landscape area planning can be basically realized quickly and easily.

[0036] Embodiment 2: like Figure 5 As shown, this embodiment provides a landscape area intelligent planning and design system based on data analysis, including 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: marking the design templates of the landscape area that was built more than L1 months ago as analysis templates; obtaining the independent coefficient and the area value of the analysis template; The feature analysis unit is used to analyze the feature critical value of the historical landscape area: mark the analysis template through the independent coefficient to obtain a dispersed template and a concentrated template; form a dispersed set by the surface values ​​of all dispersed templates, and form a concentrated set by the surface values ​​of all concentrated templates; clean the dispersed set and the concentrated set respectively 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 feature distinction range, and store it in a database; The template screening unit is used to perform template screening analysis according to the required land area value and the required function type of the required landscape area: retrieve the feature distinction range through the database; compare the required land area value with the feature 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.

[0037] Embodiment three: The present embodiment provides a landscape area intelligent planning and design device based on data analysis, including 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 landscape area intelligent planning and design method based on data analysis as described above, and the bus connects the functional components for transmitting information.

[0038] In another embodiment, the present solution can be implemented by an integrated device, which may include corresponding modules for performing each or several steps in the above-mentioned embodiments. The module may be one or more hardware modules specially configured to perform the corresponding steps, or implemented by a processor configured to perform the corresponding steps, or stored in a computer-readable medium for implementation by a processor, or implemented by some combination.

[0039] The processor performs the various methods and processes described above. For example, the method implementation in the present solution can be implemented as a software program, which is tangibly contained 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 a memory and / or a communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps in the method described above can be executed. Alternatively, in other embodiments, the processor can be configured to perform one of the above methods by any other appropriate means (for example, by means of firmware).

[0040] The device can be implemented using a bus architecture. The bus architecture can include any number of interconnecting buses and bridges, depending on the specific application and overall design constraints of the hardware. 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 peripherals, voltage regulators, power management circuits, external antennas, etc.

[0041] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0042] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to 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.

2. The method according to claim 1, characterized in that 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; Get the area value of the corresponding landscape area in the analysis template and mark it as the area value of the analysis template.

3. The method according to claim 1, characterized in that 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.

4. 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.

5. 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.

6. The method according to claim 1, characterized in that The specific method of comparing the required 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 centralized planning mode.

7. The method according to claim 6, characterized in that 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.

8. 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.

9. A landscape area intelligent planning and design system based on data analysis, 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.

10. 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 any method as claimed in claim 1-8, and the bus connects the functional components for transmitting information.

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