Urban complex element evolution prediction method and device

Abstract

The invention provides a city complex element evolution prediction method and device, and the method is characterized in that the method comprises the steps: S1, building a facility distance function according to the existing city planning data; s2, sequentially judging whether each existing facility point in the specified area meets a facility distance function or not, and if not, executing the step S3; s3, according to the facility distance function and the POI data, calculating to obtain position data and types of predicted facility points corresponding to existing facility points which do not meet the facility distance function; s4, for each predicted facility point, according to the urban building data of the specified region and the position data of the predicted facility point, judging whether the predicted facility point is feasible, and if so, taking the predicted data point as a newly added facility point; and S5, taking the position data and types of all newly added facility points as evolution prediction results. In a word, the method can generate an accurate urban evolution prediction result.

Description

Technical Field

[0001] The invention belongs to the field of urban construction planning, and specifically relates to a method and device for predicting the evolution of complex urban elements. Background Art

[0002] Urban renewal is gradually becoming an important way to promote urban development. Urban renewal is a broad concept, involving many factors such as urban material space transformation, environmental improvement, functional replacement, population mobility, and changes in social production and lifestyle. Among them, the renewal of urban service facilities is an important content, emphasizing the improvement of the quality of life in urban blocks or communities by supplementing missing functions and inserting new functions in existing spaces.

[0003] With the development of technology, a series of studies have emerged at home and abroad that use urban intelligent planning methods to solve complex urban element problems. The effective application of computer-aided tools in different fields of urban development has improved the scientific nature of planning and design. For example, new planning method research, future urban development scenario simulation, design scheme machine generation, urban performance evaluation and optimization, etc.

[0004] These intelligent planning methods provide supporting technologies for the configuration of urban elements. Some data- and model-driven technical tools have emerged, which mainly optimize the layout of elements through diagnosis, visualization, and simulation of urban functions. However, these tools are mainly used in strategic and conventional research, and rarely directly assist in the study of the spatial layout of urban elements. The key bottleneck lies in the lack of understanding of the laws of the evolution of urban-related elements and their spatial benefits, and the lack of corresponding technical means. Therefore, it is difficult to accurately optimize the layout in planning and design. Summary of the invention

[0005] The present invention is made to solve the above-mentioned problems, and its purpose is to provide a method and device for predicting the evolution of complex urban elements.

[0006] The present invention provides an urban complex element evolution prediction method, which is used to generate an evolution prediction result of a specified area, and has the following characteristics, including the following steps: step S1, constructing a facility distance function according to existing urban planning data, the urban planning data including point of interest (POI) data and urban building data; step S2, judging in turn whether each existing facility point in the specified area satisfies the facility distance function, if not, executing step S3; step S3, calculating the location data and type of predicted facility points corresponding to each existing facility point that does not satisfy the facility distance function according to the facility distance function and all point of interest (POI) data; step S4, for each predicted facility point, judging whether the predicted facility point is feasible according to the urban building data of the specified area and the location data of the predicted facility point, if yes, taking the predicted data point as a newly added facility point; step S5, taking the location data and types of all newly added facility points as the evolution prediction result.

[0007] The urban complex element evolution prediction method provided by the present invention may also have the following features: wherein, all point of interest (POI) data of urban planning data are clustered and analyzed to obtain multiple types. In step S1, the point of interest (POI) data are subjected to machine learning and probability distribution calculation to obtain a facility distance function. The facility distance function obtains the constraint conditions between two existing facility points based on the relationship between the types corresponding to the two facility points.

[0008] In the urban complex element evolution prediction method provided by the present invention, it can also have the following characteristics: wherein, the relationship includes integration, dependence, stalemate, avoidance and separation. When the relationship is integration, the constraint condition is that facility point A and facility point B are distributed in the same space and the distance does not exceed k; when the relationship is dependence, the constraint condition is that facility point A and facility point B are distributed in two adjacent spaces respectively and the distance is not more than k; when the relationship is stalemate, the constraint condition is that facility point A and facility point B coexist within the range of k, and the distance between facility point B and facility point A is positively correlated with the density of facility point A; when the relationship is avoidance, the constraint condition is that facility point A and facility point B coexist within the range of k, and when facility point B is within the range of k of facility point A, the function of facility point A is invalid; when the relationship is separation, the constraint condition is that facility point A and facility point B cannot coexist within the range of k, and the area corresponding to the urban planning data is divided according to the preset rules. Each sub-area is used as space.

[0009] The urban complex element evolution prediction method provided by the present invention may also have the following feature: in step S2, if the existing facility point and other existing facility points meet the constraint conditions, then the existing facility point meets the facility distance function.

[0010] The method for predicting the evolution of complex urban elements provided by the present invention may also have the following features: in step S3, when the predicted facility point and each existing facility point satisfy the constraint conditions, the location data of the preset facility point is obtained.

[0011] The urban complex element evolution prediction method provided by the present invention may also have the following feature: wherein, in step S4, when it is calculated based on the location data of the predicted facility point and the urban building data that the predicted facility point is in a certain building, the predicted facility point is feasible.

[0012] The present invention also provides an urban complex element evolution prediction device, which is used to generate evolution prediction results for a specified area and has the following characteristics: a function construction module, an existing facility point judgment module, a location and type calculation module, a new facility point generation module and a result generation module, wherein the function construction module is used to construct a facility distance function based on existing urban planning data, and the urban planning data includes point of interest (POI) data and urban building data; the existing facility point judgment module is used to sequentially judge whether each existing facility point in the specified area satisfies the facility distance function; if not, the location and type calculation module is executed; the location and type calculation module is used to calculate the location data and type of predicted facility points corresponding to each existing facility point that does not satisfy the facility distance function based on the facility distance function and all point of interest (POI) data; the new facility point generation module is used to judge whether each predicted facility point is feasible based on the urban building data of the specified area and the location data of the predicted facility point; if so, the predicted data point is used as a new facility point; and the result generation module is used to use the location data and types of all new facility points as evolution prediction results.

[0013] Functions and Effects of the Invention

[0014] According to the method and device for predicting the evolution of complex urban elements involved in the present invention, on the one hand, the facility distance function of the mutual distance relationship between various types of facility points is obtained by extracting the existing urban planning data; on the other hand, corresponding new facility points are generated for the existing facility points according to the facility distance function, thereby optimizing the urban planning configuration. Therefore, the method and device for predicting the evolution of complex urban elements of the present invention can generate accurate urban evolution prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a block diagram of a device for predicting the evolution of complex urban elements in an embodiment of the present invention;

[0016] Figure 2 It is a flow chart of a method for predicting the evolution of complex urban elements in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the following embodiments and the accompanying drawings specifically illustrate the method and device for predicting the evolution of complex urban elements of the present invention.

[0018] This embodiment provides a device for predicting the evolution of complex elements in an urban area, which is used to generate an evolution prediction result for a specified area.

[0019] Figure 1 It is a block diagram of a device for predicting the evolution of complex urban elements in an embodiment of the present invention.

[0020] like Figure 1 As shown, the urban complex element evolution prediction device 100 includes a function construction module 11, an existing facility point judgment module 12, a location and type calculation module 13, a new facility point generation module 14, a result generation module 15 and a control module 16 for controlling the operation of the above modules.

[0021] The function construction module 11 is used to construct a facility distance function based on the existing urban planning data, and the urban planning data includes point of interest POI data and urban building data. In this embodiment, each point of interest POI data of the urban planning data corresponds to each existing facility point, and the point of interest POI data includes the location coordinates, density and time of the existing facility point. The density is calculated based on the number of existing facility points in the calculation unit. In this embodiment, the density of the existing facility point A is the number of existing facility points of the same type as the existing facility point A in the calculation unit, which is used to reflect the number of facilities of the same type in the calculation unit. In other embodiments, the density of the existing facility point A is the ratio of the number of existing facility points of the same type as the existing facility point A in the calculation unit to all existing facility points in the calculation unit, which is used to reflect the proportion of the type corresponding to the existing facility point in the calculation unit. The position coordinates are three-dimensional coordinates, including plane coordinates x and y, and height coordinate z.

[0022] The function construction module 11 performs cluster analysis on all POI data of the urban planning data to obtain multiple types. In addition, the function construction module 11 also obtains a facility distance function by machine learning and probability distribution calculation of the POI data. The facility distance function obtains the constraint conditions between the two existing facility points based on the relationship between the types corresponding to the two facility points. In this embodiment, the types include nature, governance, residence, travel, business, medical care, education, industry, infrastructure, and innovation.

[0023] Relationships include integration, interdependence, sustenance, avoidance and separation. Different constraints corresponding to different relationships in the facility distance function include:

[0024] When the relationship is integrated, the constraint condition is that facility point A and facility point B are distributed in the same space and the distance does not exceed k.

[0025] When the relationship is dependent, the constraint condition is that facility point A and facility point B are distributed in two adjacent spaces respectively, and the distance between them is no more than k.

[0026] Among them, each sub-area of ​​the area corresponding to the urban planning data divided according to preset rules is regarded as space.

[0027] When the relationship is stalemate, the constraint condition is that facility point A and facility point B coexist within the range of k, and the distance between facility point B and facility point A is positively correlated with the density of facility point A.

[0028] When the relationship is avoidance, the constraint condition is that facility point A and facility point B coexist within the k range, and when facility point B is within the k range of facility point A, the function of facility point A is invalid.

[0029] When the relationship is separated, the constraint condition is that facility point A and facility point B cannot coexist within the range of k.

[0030] In this embodiment, the value of k is obtained by extracting the data of each existing facility point in the point of interest POI data. Through probability distribution, the conditions that meet the needs of most existing facility points are selected as constraint conditions. In this embodiment, when the relationship between 80% or more of two existing facility points meets a certain condition, the condition is a constraint condition.

[0031] In this embodiment, each facility point corresponds to a different store, that is, the fine-grainedness of the facility point is the store. In other embodiments, facility points corresponding to other fine-grained levels, such as floors, may be set as needed.

[0032] The existing facility point determination module 12 is used to sequentially determine whether each existing facility point in the designated area satisfies the facility distance function. If not, the location and type calculation module 13 is executed.

[0033] Among them, in the existing facility point judgment module 12, if the existing facility point and other existing facility points meet the constraint conditions, then the existing facility point meets the facility distance function. That is, according to the facility distance function, it is calculated that a certain type of facility point needs to be set near the existing facility point C, then the existing facility point C does not meet the facility distance function, and the corresponding predicted facility point needs to be generated.

[0034] The location and type calculation module 13 is used to calculate the location data and type of predicted facility points corresponding to each existing facility point that does not satisfy the facility distance function based on the facility distance function and all POI data.

[0035] Among them, in the location and type calculation module 13, when the predicted facility point and each existing facility point meet the constraint conditions, the location data of the preset facility point is obtained.

[0036] The newly added facility point generation module 14 is used to determine whether each predicted facility point is feasible based on the urban building data of the designated area and the location data of the predicted facility point. If so, the predicted data point is used as a newly added facility point.

[0037] Among them, in the newly added facility point generation module 14, when it is calculated based on the location data of the predicted facility point and the urban building data that the predicted facility point is in a certain building, the predicted facility point is feasible. In this embodiment, the urban building data includes the boundary and height data of each building. According to whether the location data of the predicted facility point is within the boundary and height data, it is determined whether the predicted facility point can be set in the corresponding building. If so, the predicted facility point is feasible.

[0038] The result generation module 15 is used to take the location data and types of all newly added facility points as the evolution prediction results.

[0039] In this embodiment, the newly added facility points can make up for the unreasonable configuration of the existing facility points and optimize the functional facility configuration of the entire designated area.

[0040] The control module 16 stores a control program for controlling the operation of each module.

[0041] The following describes the process of using the urban complex element evolution prediction device 100 to perform an urban complex element evolution prediction method in conjunction with the accompanying drawings.

[0042] Figure 2 It is a flow chart of a method for predicting the evolution of complex urban elements in an embodiment of the present invention.

[0043] like Figure 2 As shown in Figure 2, the urban complex element evolution prediction method includes the following steps:

[0044] Step S1, using the function construction module 11 to construct a facility distance function according to existing urban planning data, the urban planning data includes point of interest POI data and urban building data.

[0045] Step S2, using the existing facility point determination module 12 to determine in turn whether each existing facility point in the designated area satisfies the facility distance function, if not, executing step S3.

[0046] Step S3, using the location and type calculation module 13 to calculate the location data and type of predicted facility points corresponding to each existing facility point that does not satisfy the facility distance function according to the facility distance function and all POI data.

[0047] Step S4, using the newly added facility point generation module 14 to determine whether each predicted facility point is feasible based on the urban building data of the designated area and the location data of the predicted facility point, and if so, the predicted data point is used as a newly added facility point.

[0048] Step S5: Using the result generation module 15, the location data and types of all newly added facility points are used as evolution prediction results.

[0049] Functions and Effects of the Embodiments

[0050] According to the method and device for predicting the evolution of complex urban elements involved in this embodiment, on the one hand, a facility distance function of the mutual distance relationship between various types of facility points is obtained through the extraction of existing urban planning data; on the other hand, corresponding new facility points are generated for existing facility points according to the facility distance function, thereby optimizing the urban planning configuration. In short, this method can generate accurate urban evolution prediction results.

[0051] Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for predicting the evolution of complex urban elements, used to generate the evolution prediction results of a specified area, characterized in that: The following steps are involved: Step S1, constructing a facility distance function based on existing urban planning data, wherein the urban planning data includes point of interest (POI) data and urban building data; Step S2, determining in turn whether each existing facility point in the designated area satisfies the facility distance function, if not, executing step S3; Step S3, calculating the location data and type of predicted facility points corresponding to each of the existing facility points that do not satisfy the facility distance function according to the facility distance function and all the POI data; Step S4, for each predicted facility point, judging whether the predicted facility point is feasible based on the urban building data of the designated area and the location data of the predicted facility point, and if so, taking the predicted data point as a newly added facility point; Step S5: taking the location data and the types of all the newly added facility points as the evolution prediction results.

2. The method for predicting the evolution of complex urban elements according to claim 1, characterized in that: in, Performing cluster analysis on all the POI data of the urban planning data to obtain a plurality of the types, In step S1, the POI data is subjected to machine learning and probability distribution calculation to obtain the facility distance function. The facility distance function obtains the constraint condition between the two existing facility points according to the relationship between the types corresponding to the two existing facility points.

3. The method for predicting the evolution of complex urban elements according to claim 2, characterized in that: in, The relationships include integration, dependence, support, avoidance and separation. When the relationship is integration, the constraint condition is that facility point A and facility point B are distributed in the same space and the distance does not exceed k; When the relationship is dependent, the constraint condition is that facility point A and facility point B are distributed in two adjacent spaces, and the distance between them is no more than k; When the relationship is a stalemate, the constraint condition is that facility point A and facility point B coexist within a range of k, and the distance between facility point B and facility point A is positively correlated with the density of facility point A; When the relationship is avoidance, the constraint condition is that facility point A and facility point B coexist within a range of k, and when facility point B is within the range of k of facility point A, the function of facility point A is invalid; When the relationship is separated, the constraint condition is that facility point A and facility point B cannot coexist within the range of k. Each sub-area of ​​the area corresponding to the urban planning data divided according to a preset rule is used as the space.

4. The method for predicting the evolution of complex urban elements according to claim 2, characterized in that: in, In the step S2, if the existing facility point and other existing facility points satisfy the constraint condition, then the existing facility point satisfies the facility distance function.

5. The method for predicting the evolution of complex urban elements according to claim 2, characterized in that: in, In the step S3, when the predicted facility point and each of the existing facility points satisfy the constraint conditions, the location data of the preset facility point is obtained.

6. The method for predicting the evolution of complex urban elements according to claim 1, characterized in that: in, In the step S4, when it is calculated based on the location data of the predicted facility point and the urban building data that the predicted facility point is inside a certain building, the predicted facility point is feasible.

7. A device for predicting the evolution of complex urban elements, used to generate the evolution prediction results of a specified area, characterized in that: include: Function construction module, existing facility point judgment module, location and type calculation module, new facility point generation module and result generation module, The function building module is used to build a facility distance function based on existing urban planning data, wherein the urban planning data includes point of interest (POI) data and urban building data. The existing facility point determination module is used to sequentially determine whether each existing facility point in the designated area satisfies the facility distance function. If not, the location and type calculation module is executed. The location and type calculation module is used to calculate the location data and type of the predicted facility points corresponding to the existing facility points that do not meet the facility distance function according to the facility distance function and all the POI data. The newly added facility point generation module is used to determine whether each predicted facility point is feasible based on the urban building data of the specified area and the location data of the predicted facility point. If so, the predicted data point is used as a newly added facility point. The result generation module is used to use the location data and the types of all the newly added facility points as the evolution prediction results.

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