Design method and system for smart city planning

Abstract

The invention is suitable for the technical field of city planning, and provides a design method and system for smart city planning, and the method comprises the following steps: calling city road network information and service facility information, the service facility information comprises a plurality of service facilities, and each service facility corresponds to a facility type, a service coordinate and service energy efficiency; calculating the energy efficiency cardinal number of each living area, calling the population number of each living area, and calculating the per capita energy efficiency value; and determining facility planning information based on the position of each living area, the per capita energy efficiency value and the facility type, wherein the facility planning information comprises the facility type, the planning position and the service energy efficiency. According to the method, the energy efficiency cardinal number of each living area is calculated by integrating the weight of the service facility, the service energy efficiency, the distance attenuation coefficient and the path distance, and the service facility supply level of each area can be accurately reflected. By calculating the per capita energy efficiency value, the supply and demand matching degrees of service facilities in different areas can be visually compared, and data support is provided for facility supplementation.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, and in particular to a design method and system for smart city planning. Background Art

[0002] With the acceleration of global urbanization, cities are expanding in size and increasing population density, posing unprecedented challenges to urban management. Smart cities, as the mainstream direction of future urban development, aim to enhance urban operational efficiency, improve residents' quality of life, and promote efficient resource utilization through advanced information technology and intelligent means. Scientific and rational urban planning is the foundation and core of smart city development, directly impacting a city's sustainable development and residents' well-being. Service facilities are crucial for meeting residents' daily needs and improving their quality of life. However, traditional urban service facility planning methods often focus on macro-level layout and qualitative analysis, lacking precise assessment and dynamic adjustment mechanisms for the energy efficiency of service facilities at the micro level. This results in uneven distribution of service facilities in some areas, low service energy efficiency, and difficulty meeting residents' growing needs. Therefore, there is a need for a design method and system for smart city planning to address these issues. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a design method and system for smart city planning to solve the problems existing in the above-mentioned background technology.

[0004] The present invention is implemented as follows: a design method for smart city planning, the method comprising the following steps:

[0005] Retrieving urban road network information and service facility information, wherein the service facility information includes a plurality of service facilities, each of which corresponds to a facility type, service coordinates, and service energy efficiency;

[0006] Calculate the energy efficiency base Z of each living area, Z = ∑αi × Si × e-βi × ri, where αi represents the weight of the i-th service facility, Si represents the service energy efficiency of the i-th service facility, βi represents the distance attenuation coefficient of the i-th service facility, and ri represents the path distance between the i-th service facility and the living area;

[0007] Get the population N of each living area and calculate the per capita energy efficiency value G, which is G = Z / N;

[0008] Facility planning information is determined based on the location of each living area, the per capita energy efficiency value, and the facility type. The facility planning information includes the facility type, the planned location, and the service energy efficiency.

[0009] As a further solution of the present invention: the step of calculating the energy efficiency base Z of each living area specifically includes:

[0010] Determine the service facilities corresponding to each living area, where the service facilities are located within a circle with a center of the living area as the center and a threshold value R as the radius;

[0011] The construction age M and facility type of the service facility are retrieved, and the time decay coefficient γ is determined according to the facility type. The weight α is calculated as α = e-γ × M;

[0012] Determine the distance attenuation coefficient β of each service facility according to the type of facility and calculate the energy efficiency base Z.

[0013] As a further solution of the present invention, the step of determining facility planning information based on the location, per capita energy efficiency value, and facility type of each living area specifically includes:

[0014] Filter out the per capita energy efficiency value G that is lower than the preset per capita value G M living areas;

[0015] The filtered living areas are grouped based on the circular range with the threshold R as the diameter;

[0016] Determine the types of facilities that need to be added, their planning locations, and service energy efficiency based on the location, per capita energy efficiency value, and facility types of each living area in each group;

[0017] Integrate all the groupings to add facility types, planning locations and service energy efficiency to obtain facility planning information.

[0018] As a further solution of the present invention, the step of determining the type of facilities to be added, the planned location, and the service energy efficiency based on the location, per capita energy efficiency value, and facility type of each living area in each group specifically includes:

[0019] Connecting the location coordinates of each living area in each group in sequence to obtain a polygon, and determining the geometric center point of the polygon;

[0020] Determine the offset information of the geometric center point based on the per capita energy efficiency value, and determine the planning position based on the offset information;

[0021] Retrieving the types of facilities within each group and the corresponding service energy efficiency, and determining the scarce facility types, wherein the scarce facility types are the types of facilities that need to be increased;

[0022] The service energy efficiency of the added service facilities is gradually determined according to the preset gradient Δg and the initial value of service energy efficiency G0 until the per capita energy efficiency value of each living area within each group meets the conditions.

[0023] As a further solution of the present invention: the step of determining the offset information of the geometric center point according to the per capita energy efficiency value specifically includes:

[0024] Calculate the per capita energy efficiency value G of each living area in each group and the preset per capita value G M The difference ΔG;

[0025] Calculate the average distance value F between the geometric center point and the location coordinates of each living area;

[0026] Several offset vectors are determined based on the average distance value F and several difference values ΔG. The length of the offset vector = δ×ΔG / F, δ is the length coefficient, and the direction of the offset vector is from the geometric center point to the corresponding living area. The offset information is obtained based on the several offset vectors.

[0027] Another object of the present invention is to provide a design system for smart city planning, the system comprising:

[0028] A service facility retrieval module is used to retrieve urban road network information and service facility information. The service facility information includes a number of service facilities, each of which corresponds to a facility type, service coordinates, and service energy efficiency;

[0029] An energy efficiency base calculation module is used to calculate the energy efficiency base Z of each living area, Z = ∑αi×Si×e-βi×ri, where αi represents the weight of the i-th service facility, Si represents the service energy efficiency of the i-th service facility, βi represents the distance attenuation coefficient of the i-th service facility, and ri represents the path distance between the i-th service facility and the living area;

[0030] The per capita energy efficiency value module is used to retrieve the population N of each living area and calculate the per capita energy efficiency value G, where G = Z / N.

[0031] The facility planning information module is used to determine facility planning information based on the location of each living area, the per capita energy efficiency value and the facility type. The facility planning information includes the facility type, planned location and service energy efficiency.

[0032] As a further solution of the present invention: the energy efficiency base calculation module includes:

[0033] a corresponding facility determination unit, configured to determine the service facilities corresponding to each living area, wherein the service facilities are located within a circle having a center of the living area as the center and a threshold value R as the radius;

[0034] The time weight calculation unit is used to retrieve the construction age M and facility type of the service facility, determine the time decay coefficient γ according to the facility type, and calculate the weight α, α = e-γ × M;

[0035] The energy efficiency base determination unit is used to determine the distance attenuation coefficient β of each service facility according to the type of facility and calculate the energy efficiency base Z.

[0036] As a further solution of the present invention: the facility planning information module includes:

[0037] Living area screening unit, used to screen out the areas where the per capita energy efficiency value G is lower than the preset per capita value G M living areas;

[0038] A living area grouping unit, configured to group the screened living areas based on a circular range with a threshold R as its diameter;

[0039] Add a facility determination unit, which is used to determine the type of facilities that need to be added, the planning location and the service energy efficiency based on the location, per capita energy efficiency value and facility type of each living area in each group;

[0040] The facility planning information unit is used to integrate the facility types, planned locations and service energy efficiency that need to be added to all groups to obtain facility planning information.

[0041] As a further solution of the present invention: the facility addition determination unit includes:

[0042] A geometric center point subunit is used to sequentially connect the position coordinates of each living area in each group to obtain a polygon and determine the geometric center point of the polygon;

[0043] A planning position determination subunit is used to determine the offset information of the geometric center point according to the per capita energy efficiency value, and determine the planning position according to the offset information;

[0044] The scarce facility category subunit is used to retrieve the facility categories and corresponding service energy efficiencies within each group and determine the scarce facility categories, where the scarce facility categories are the facility categories that need to be increased;

[0045] The service energy efficiency determination subunit is used to gradually determine the service energy efficiency of the added service facilities according to the preset gradient Δg and the initial service energy efficiency value G0 until the per capita energy efficiency value of each living area within each group meets the conditions.

[0046] As a further solution of the present invention: the planning position determination subunit includes:

[0047] Difference calculation area, used to calculate the per capita energy efficiency value G of each living area in each group and the preset per capita value G M The difference ΔG;

[0048] The average value calculation area is used to calculate the average distance value F between the geometric center point and the location coordinates of each living area;

[0049] The offset vector determination area is used to determine several offset vectors based on the average distance value F and several difference values ΔG. The length of the offset vector = δ×ΔG / F, δ is the length coefficient, and the direction of the offset vector is from the geometric center point to the corresponding living area. The offset information is obtained based on several offset vectors.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention calculates the energy efficiency base of each living area by comprehensively considering the weight of service facilities, service energy efficiency, distance attenuation coefficient, and path distance, and can accurately reflect the level of service facility supply in each area. By calculating the per capita energy efficiency value, the supply and demand matching of service facilities in different areas can be intuitively compared, service blind spots can be accurately identified, and data support can be provided for the addition of facilities. Facility planning information is also determined based on the location of each living area, per capita energy efficiency value, and facility type. New service facilities can be added in areas with weak service in a targeted manner to avoid waste of resources and inefficient configuration, achieve accurate urban facility energy efficiency assessment and scientific planning decisions, and meet the growing needs of residents. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flowchart of a design method for smart city planning.

[0053] Figure 2 A flowchart for calculating the energy efficiency base in a design method for smart city planning.

[0054] Figure 3 A flowchart for determining facility planning information in a design method for smart city planning.

[0055] Figure 4 A flowchart for determining the types of facilities that need to be added, their planning locations, and service energy efficiency in a design method for smart city planning.

[0056] Figure 5 A flowchart for determining offset information of a geometric center point in a design method for smart city planning.

[0057] Figure 6 This is a schematic diagram of the structure of a design system for smart city planning. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0060] like Figure 1 As shown, an embodiment of the present invention provides a design method for smart city planning, the method comprising the following steps:

[0061] S100, retrieving urban road network information and service facility information, wherein the service facility information includes a plurality of service facilities, each of which corresponds to a facility type, service coordinates, and service energy efficiency;

[0062] S200, calculate the energy efficiency base Z of each living area, Z = ∑αi×Si×e-βi×ri;

[0063] S300, retrieve the population N of each living area and calculate the per capita energy efficiency value G, where G = Z / N;

[0064] S400 , determining facility planning information based on the location of each living area, the per capita energy efficiency value, and the facility type. The facility planning information includes the facility type, planned location, and service energy efficiency.

[0065] It should be noted that in the construction of smart cities, scientific and reasonable urban planning is the foundation and core, which is directly related to the sustainable development capabilities of the city and the happiness of residents. Service facilities are an important guarantee for meeting the daily needs of residents and improving their quality of life. Traditional urban service facility planning methods mostly use qualitative analysis or simple quantitative evaluation, which fails to fully consider factors such as the service energy efficiency, distance attenuation effect, and population distribution of service facilities, resulting in deviations between the evaluation results and actual needs. Due to the lack of accurate energy efficiency evaluation and planning decision support, some areas have excess or insufficient service facilities, resulting in waste of resources or service shortages. The embodiments of the present invention are intended to solve the above problems.

[0066] In this embodiment of the present invention, city road network information and service facility information are first retrieved. Both city road network information and service facility information must be collected and confirmed in advance. This service facility information includes a large number of service facilities, each of which corresponds to a facility type, service coordinates, and service energy efficiency. Facility types include shopping malls, hospitals, schools, parks, etc. Service coordinates refer to the location coordinates of the service facility, and service energy efficiency reflects the number of people the service facility can accommodate. Based on this information, the energy efficiency base Z for each living area can be calculated using the following formula: Z = ∑αi × Si × e-βi × ri, where αi represents the weight of the i-th service facility, Si represents the service energy efficiency of the i-th service facility, βi represents the distance decay coefficient of the i-th service facility, and ri represents the path distance between the i-th service facility and the living area. The entire city is divided into a large number of living areas, and the center coordinates of each living area are the location coordinates of that living area. This embodiment of the present invention calculates the energy efficiency base for each living area by integrating the service facility weights, service energy efficiency, distance decay coefficient, and path distance, accurately reflecting the level of service facility supply in each area. Next, the population size N of each living area will be retrieved. The population size also needs to be collected and determined in advance. The per capita energy efficiency value G will be calculated. This can be used to visually compare the supply and demand matching of service facilities in different areas, accurately identify service blind spots (such as areas with too low G values) and oversupplied areas (such as areas with too high G values), and provide data support for the addition or optimization of facilities. Finally, facility planning information will be determined based on the location of each living area, the per capita energy efficiency value, and the type of facilities. New service facilities can be added to underserved areas in a targeted manner to avoid waste of resources and inefficient allocation, and achieve accurate urban facility energy efficiency assessment and scientific planning decisions.

[0067] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of calculating the energy efficiency base Z of each living area specifically includes:

[0068] S201, determining service facilities corresponding to each living area, wherein the service facilities are located within a circle with a center of the living area as the center and a threshold value R as the radius;

[0069] S202, retrieve the construction age M and facility type of the service facility, determine the time decay coefficient γ according to the facility type, and calculate the weight α, α = e-γ × M;

[0070] S203: Determine the distance attenuation coefficient β of each service facility according to the type of facility, and calculate the energy efficiency base Z.

[0071] In an embodiment of the present invention, before calculating the energy efficiency base of each living area, it is necessary to determine the service facilities corresponding to each living area. These corresponding service facilities are located within a range with the center of the living area as the center and a threshold value R as the radius. The threshold value R is a fixed value set in advance, for example, R is 8km. Then the construction age M and facility type of each service facility will be retrieved, and the time attenuation coefficient γ will be determined according to the facility type. The correspondence between the facility type and the time attenuation coefficient γ needs to be set in advance, and then the weight can be calculated. Then, it is also necessary to determine the distance attenuation coefficient β of each service facility according to the facility type. The correspondence between the facility type and the distance attenuation coefficient β also needs to be set in advance. In this way, the energy efficiency base can be calculated, and the method of determining the energy efficiency base is scientific and accurate.

[0072] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of determining facility planning information based on the location of each living area, the per capita energy efficiency value, and the facility type specifically includes:

[0073] S401, filter out the per capita energy efficiency value G that is lower than the preset per capita value G M living areas;

[0074] S402, grouping the screened living areas based on a circular range with a threshold R as its diameter;

[0075] S403, determining the type of facilities to be added, the planned location, and the service energy efficiency based on the location, per capita energy efficiency value, and facility type of each living area in each group;

[0076] S404: Integrate the types of facilities, planned locations, and service energy efficiencies that need to be added to all groups to obtain facility planning information.

[0077] In the embodiment of the present invention, in order to determine the facility planning information, the first step is to automatically filter out the per capita energy efficiency value G that is lower than the preset per capita value G. M The selected living areas are marked on the map. The selected living areas are then grouped according to a circular range with a threshold R as the diameter. The living areas of each group are located within the same circular range. The grouping principle is to use the smallest possible circular range. Next, based on the location, per capita energy efficiency value, and facility type of each living area in each group, the types of facilities to be added, their planned locations, and service energy efficiency are determined. Specifically, a service facility is planned for each living area group. Finally, the facility planning information is obtained by integrating the types of facilities to be added, their planned locations, and service energy efficiency across all groups.

[0078] like Figure 4As shown, as a preferred embodiment of the present invention, the step of determining the type of facilities to be added, the planned location, and the service energy efficiency based on the location, per capita energy efficiency value, and facility type of each living area in each group specifically includes:

[0079] S4031, sequentially connecting the location coordinates of each living area in each group to obtain a polygon, and determining the geometric center point of the polygon;

[0080] S4032: Determine offset information of the geometric center point based on the per capita energy efficiency value, and determine the planned location based on the offset information;

[0081] S4033: Retrieve the facility types and corresponding service energy efficiencies within each group to determine scarce facility types, where the scarce facility types are those that need to be added.

[0082] S4034 , gradually determining the service energy efficiency of the added service facilities according to the preset gradient Δg and the initial value of service energy efficiency G0 until the per capita energy efficiency value of each living area within each group meets the conditions.

[0083] In an embodiment of the present invention, when determining the types of facilities, planned locations, and service energy efficiency that need to be added to the planning in each group, first, the location coordinates of each living area in the group are connected in sequence to obtain a polygon, and the geometric center point of the polygon is determined. Then, the offset information of the geometric center point is determined based on the per capita energy efficiency value, and the planned location is determined based on the offset information, so that the planned location will be more accurate. Then, the types of facilities and the corresponding service energy efficiency within each group are retrieved to determine the types of scarce facilities. Here, the service energy efficiency of each type of facility can be accumulated, and the type of facility with the lowest accumulated value is the scarce facility type. Then, the service energy efficiency of the added service facilities is gradually determined according to the preset gradient Δg and the initial value of the service energy efficiency G0. Both Δg and G0 need to be determined in advance until the per capita energy efficiency value of each living area within each group meets the conditions, and the service energy efficiency will be finally determined.

[0084] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of determining the offset information of the geometric center point according to the per capita energy efficiency value specifically includes:

[0085] S40321: Calculate the per capita energy efficiency value G of each living area in each group and the preset per capita value G M The difference ΔG;

[0086] S40322, calculating the average distance F between the geometric center point and the location coordinates of each living area;

[0087] S40323, determine several offset vectors based on the average distance value F and several difference values ΔG. The length of the offset vector = δ×ΔG / F, δ is the length coefficient, and the direction of the offset vector is from the geometric center point to the corresponding living area. The offset information is obtained based on the several offset vectors.

[0088] In the embodiment of the present invention, in order to determine the precise location of the added service facilities, it is necessary to calculate the per capita energy efficiency value G of each living area in each group and the preset per capita value G M The difference ΔG is calculated, and the average distance F between the geometric center point and the location coordinates of each living area is calculated. In this way, several offset vectors can be determined based on the average distance F and the several differences ΔG. The length of the offset vector = δ × ΔG / F, where δ is the length coefficient and is a constant value. The direction of the offset vector is from the geometric center point to the corresponding living area. Based on the offset information obtained from the several offset vectors, the specific location of the required service facility can be obtained by offsetting the geometric center point.

[0089] like Figure 6 As shown, an embodiment of the present invention further provides a design system for smart city planning, the system comprising:

[0090] A service facility retrieval module 100 is used to retrieve urban road network information and service facility information, wherein the service facility information includes a plurality of service facilities, each of which corresponds to a facility type, service coordinates, and service efficiency;

[0091] The energy efficiency base calculation module 200 is used to calculate the energy efficiency base Z of each living area, Z = ∑αi×Si×e-βi×ri, where αi represents the weight of the i-th service facility, Si represents the service energy efficiency of the i-th service facility, βi represents the distance attenuation coefficient of the i-th service facility, and ri represents the path distance between the i-th service facility and the living area;

[0092] The per capita energy efficiency value module 300 is used to retrieve the population N of each living area and calculate the per capita energy efficiency value G, where the per capita energy efficiency value G=Z / N;

[0093] The facility planning information module 400 is used to determine facility planning information based on the location of each living area, the per capita energy efficiency value, and the facility type. The facility planning information includes the facility type, planned location, and service energy efficiency.

[0094] As a preferred embodiment of the present invention, the energy efficiency base calculation module 200 includes:

[0095] a corresponding facility determination unit, configured to determine the service facilities corresponding to each living area, wherein the service facilities are located within a circle having a center of the living area as the center and a threshold value R as the radius;

[0096] The time weight calculation unit is used to retrieve the construction age M and facility type of the service facility, determine the time decay coefficient γ according to the facility type, and calculate the weight α, α = e-γ × M;

[0097] The energy efficiency base determination unit is used to determine the distance attenuation coefficient β of each service facility according to the type of facility and calculate the energy efficiency base Z.

[0098] As a preferred embodiment of the present invention, the facility planning information module 400 includes:

[0099] Living area screening unit, used to screen out the areas where the per capita energy efficiency value G is lower than the preset per capita value G M living areas;

[0100] A living area grouping unit, configured to group the screened living areas based on a circular range with a threshold R as its diameter;

[0101] Add a facility determination unit, which is used to determine the type of facilities that need to be added, the planning location and the service energy efficiency based on the location, per capita energy efficiency value and facility type of each living area in each group;

[0102] The facility planning information unit is used to integrate the facility types, planned locations and service energy efficiency that need to be added to all groups to obtain facility planning information.

[0103] As a preferred embodiment of the present invention, the added facility determination unit includes:

[0104] A geometric center point subunit is used to sequentially connect the position coordinates of each living area in each group to obtain a polygon and determine the geometric center point of the polygon;

[0105] A planning position determination subunit is used to determine the offset information of the geometric center point according to the per capita energy efficiency value, and determine the planning position according to the offset information;

[0106] The scarce facility category subunit is used to retrieve the facility categories and corresponding service energy efficiencies within each group and determine the scarce facility categories, where the scarce facility categories are the facility categories that need to be increased;

[0107] The service energy efficiency determination subunit is used to gradually determine the service energy efficiency of the added service facilities according to the preset gradient Δg and the initial service energy efficiency value G0 until the per capita energy efficiency value of each living area within each group meets the conditions.

[0108] As a preferred embodiment of the present invention, the planning position determination subunit includes:

[0109] Difference calculation area, used to calculate the per capita energy efficiency value G of each living area in each group and the preset per capita value G M The difference ΔG;

[0110] The average value calculation area is used to calculate the average distance value F between the geometric center point and the location coordinates of each living area;

[0111] The offset vector determination area is used to determine several offset vectors based on the average distance value F and several difference values ΔG. The length of the offset vector = δ×ΔG / F, δ is the length coefficient, and the direction of the offset vector is from the geometric center point to the corresponding living area. The offset information is obtained based on several offset vectors.

[0112] The above is only a detailed description of the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0113] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0114] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0115] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A design method for smart city planning, characterized in that: The method comprises the following steps: Retrieving urban road network information and service facility information, wherein the service facility information includes a plurality of service facilities, each of which corresponds to a facility type, service coordinates, and service energy efficiency; Calculate the energy efficiency base Z of each living area, Z = ∑αi × Si × e-βi × ri, where αi represents the weight of the i-th service facility, Si represents the service energy efficiency of the i-th service facility, βi represents the distance attenuation coefficient of the i-th service facility, and ri represents the path distance between the i-th service facility and the living area; Get the population N of each living area and calculate the per capita energy efficiency value G, which is G = Z / N; Facility planning information is determined based on the location of each living area, the per capita energy efficiency value, and the facility type. The facility planning information includes the facility type, the planned location, and the service energy efficiency.

2. The design method for smart city planning according to claim 1, characterized in that: The step of calculating the energy efficiency base Z of each living area specifically includes: Determine the service facilities corresponding to each living area, where the service facilities are located within a circle with a center of the living area as the center and a threshold value R as the radius; The construction age M and facility type of the service facility are retrieved, and the time decay coefficient γ is determined according to the facility type. The weight α is calculated as α = e-γ × M; Determine the distance attenuation coefficient β of each service facility according to the type of facility and calculate the energy efficiency base Z.

3. The design method for smart city planning according to claim 1, characterized in that: The step of determining facility planning information based on the location, per capita energy efficiency value, and facility type of each living area specifically includes: Filter out the per capita energy efficiency value G that is lower than the preset per capita value G M living areas; The filtered living areas are grouped based on the circular range with the threshold R as the diameter; Determine the types of facilities that need to be added, their planning locations, and service energy efficiency based on the location, per capita energy efficiency value, and facility types of each living area in each group; Integrate all the groupings to add facility types, planning locations and service energy efficiency to obtain facility planning information.

4. The design method for smart city planning according to claim 3, characterized in that: The step of determining the type of facilities to be added, the planned location, and the service energy efficiency based on the location, per capita energy efficiency value, and facility type of each living area in each group specifically includes: Connecting the location coordinates of each living area in each group in sequence to obtain a polygon, and determining the geometric center point of the polygon; Determine the offset information of the geometric center point based on the per capita energy efficiency value, and determine the planning position based on the offset information; Retrieving the types of facilities within each group and the corresponding service energy efficiency, and determining the scarce facility types, wherein the scarce facility types are the types of facilities that need to be increased; The service energy efficiency of the added service facilities is gradually determined according to the preset gradient Δg and the initial value of service energy efficiency G0, until the per capita energy efficiency value of each living area within each group meets the conditions.

5. The design method for smart city planning according to claim 4, characterized in that: The step of determining the offset information of the geometric center point according to the per capita energy efficiency value specifically includes: Calculate the per capita energy efficiency value G of each living area in each group and the preset per capita value G M The difference ΔG; Calculate the average distance F between the geometric center point and the location coordinates of each living area; Several offset vectors are determined based on the average distance value F and several difference values ΔG. The length of the offset vector = δ×ΔG / F, δ is the length coefficient, and the direction of the offset vector is from the geometric center point to the corresponding living area. The offset information is obtained based on the several offset vectors.

6. A design system for smart city planning, characterized in that: The system comprises: A service facility retrieval module is used to retrieve urban road network information and service facility information. The service facility information includes a number of service facilities, each of which corresponds to a facility type, service coordinates, and service energy efficiency; An energy efficiency base calculation module is used to calculate the energy efficiency base Z of each living area, Z = ∑αi×Si×e-βi×ri, where αi represents the weight of the i-th service facility, Si represents the service energy efficiency of the i-th service facility, βi represents the distance attenuation coefficient of the i-th service facility, and ri represents the path distance between the i-th service facility and the living area; The per capita energy efficiency value module is used to retrieve the population N of each living area and calculate the per capita energy efficiency value G, where G = Z / N. The facility planning information module is used to determine facility planning information based on the location of each living area, the per capita energy efficiency value and the facility type. The facility planning information includes the facility type, planned location and service energy efficiency.

7. The smart city planning design system according to claim 6, characterized in that: The energy efficiency base calculation module includes: a corresponding facility determination unit, configured to determine the service facilities corresponding to each living area, wherein the service facilities are located within a circle having a center of the living area as the center and a threshold value R as the radius; The time weight calculation unit is used to retrieve the construction age M and facility type of the service facility, determine the time decay coefficient γ according to the facility type, and calculate the weight α, α = e-γ × M; The energy efficiency base determination unit is used to determine the distance attenuation coefficient β of each service facility according to the type of facility and calculate the energy efficiency base Z.

8. The smart city planning design system according to claim 6, characterized in that: The facility planning information module includes: Living area screening unit, used to screen out the areas where the per capita energy efficiency value G is lower than the preset per capita value G M living areas; A living area grouping unit, configured to group the screened living areas based on a circular range with a threshold R as its diameter; Add a facility determination unit, which is used to determine the type of facilities that need to be added, the planning location and the service energy efficiency based on the location, per capita energy efficiency value and facility type of each living area in each group; The facility planning information unit is used to integrate the facility types, planned locations and service energy efficiency that need to be added to all groups to obtain facility planning information.

9. The smart city planning design system according to claim 8, characterized in that: The added facility determination unit includes: A geometric center point subunit is used to sequentially connect the position coordinates of each living area in each group to obtain a polygon and determine the geometric center point of the polygon; A planning position determination subunit is used to determine the offset information of the geometric center point according to the per capita energy efficiency value, and determine the planning position according to the offset information; The scarce facility category subunit is used to retrieve the facility categories and corresponding service energy efficiencies within each group and determine the scarce facility categories, where the scarce facility categories are the facility categories that need to be increased; The service energy efficiency determination subunit is used to gradually determine the service energy efficiency of the added service facilities according to the preset gradient Δg and the initial service energy efficiency value G0 until the per capita energy efficiency value of each living area within each group meets the conditions.

10. The smart city planning design system according to claim 9, characterized in that: The planning position determination subunit includes: Difference calculation area, used to calculate the per capita energy efficiency value G of each living area in each group and the preset per capita value G M The difference ΔG; The average value calculation area is used to calculate the average distance value F between the geometric center point and the location coordinates of each living area; The offset vector determination area is used to determine several offset vectors based on the average distance value F and several difference values ΔG. The length of the offset vector = δ×ΔG / F, δ is the length coefficient, and the direction of the offset vector is from the geometric center point to the corresponding living area. The offset information is obtained based on several offset vectors.

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