CN117520618A - Thermodynamic density expression method and system for multi-dimensional capacity space - Google Patents
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Abstract
The invention is applicable to the technical field of data processing, and provides a thermodynamic density expression method and a thermodynamic density expression system for a multi-dimensional capacity space.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a thermodynamic density expression method and a thermodynamic density expression system for a multi-dimensional capacity space.
Background
The multi-dimensional capability index refers to various elements reflecting urban development and quality of life, including but not limited to population density, employment opportunities, traffic convenience, educational resources, medical facilities, and business centers, etc. By calculating and analyzing these indices, a multi-dimensional capability value for each geographic location point, i.e., the performance of that location point on the individual capability indices, can be obtained.
In the multidimensional capacity space thermodynamic density expression technology, the thermodynamic density is an important concept. It represents the concentration and strength of multi-dimensional capacity values around a geographic location and can be used to measure the overall capacity level of a region. Areas of high thermal density generally represent areas with high performance and potential in terms of multidimensional capacity index, possibly hot spot areas within a city, suitable for key development and investment. In contrast, areas of low thermal density may be potential areas of development, requiring further improvement and development.
Therefore, in the fields of urban planning and municipal engineering, the multidimensional capacity space thermodynamic density expression technology is widely applied to the fields of urban planning, public facility construction, traffic network optimization and the like. The technology can help a decision maker to identify and evaluate hot spot areas and potential development areas in the city by calculating and analyzing multi-dimensional capability indexes in the city, so as to guide the decision making and implementation of city planning and municipal engineering.
In the information age today, the acquisition and application of geographical information data is becoming increasingly important. However, conventional geographic information processing methods often consider only the geographic location dimension, while ignoring other attribute dimensions. In order to better understand and compare the thermodynamic distribution in a multi-dimensional capacity space, a thermodynamic density expression method oriented to the multi-dimensional capacity space is needed.
Disclosure of Invention
The embodiment of the invention aims to provide a thermodynamic density expression method and a thermodynamic density expression system for a multi-dimensional capacity space, and aims to solve the problems in the background art.
The embodiment of the invention is realized in such a way that the thermodynamic density expression method facing the multi-dimensional capacity space comprises the following steps:
step 1, acquiring a city map and geographic information data, wherein the geographic information data comprises position information and multidimensional capacity data corresponding to the position information;
step 2, carrying out standardization processing on the geographic information data to obtain a plurality of groups of multi-dimensional capacity data, and establishing the position relation between each group of multi-dimensional capacity data and the urban map based on the position information;
and 3, describing the thermodynamic distribution of different areas in the capacity space according to the multiple groups of multi-dimensional capacity data and the position relation between each group of multi-dimensional capacity data and the urban map.
According to a further technical scheme, the step 3 comprises the following specific steps:
step 3.1, determining the space coordinates of the urban map;
step 3.2, establishing the corresponding relation between each group of multi-dimensional capacity data and the space coordinates based on the position information;
and 3.3, constructing a thermodynamic density function according to the corresponding relation between the multi-dimensional capacity data and the space coordinates so as to express thermodynamic distribution of different areas.
According to a further technical scheme, the step 3.3 comprises the following specific steps:
step 3.3.1, setting the thermodynamic distribution to be expressed as n dimensions, wherein the value range of each dimension is [ a, b ];
step 3.3.2, constructing an initial thermodynamic density function;
and 3.3.3, correcting the initial thermodynamic density function by using a Gaussian function to obtain a smooth thermodynamic density function.
In a further technical solution, in the step 3.3.2, the initial thermodynamic density expression is as follows:
ρ(c 1 ,c 2 ,…,c n )＝f(c 1 ,c 2 ,…,c n )
wherein ρ represents the thermodynamic density, c 1 、c 2 、…、c n Representing the values of the dimensions in the capacity space, f is a multiple function describing the state of the thermodynamic density as the dimensions change.
In a further technical solution, in the step 3.3.3, the thermodynamic density function expression is as follows:
wherein m is 1 、m 2 、…、m n Is the average value of each dimension, sigma 1 、σ 2 、…、σ n Is the standard deviation of the individual dimensions.
In a further technical solution, in the step 3.3.3, if a dimension of genojson data mapping exists in the n dimensions of the thermodynamic distribution, the thermodynamic density is expressed as follows:
wherein c 1 And c 2 Is the coordinate dimension in the capability space, a 1 And a 2 Is another attribute in genojson, f is a multiple function describing how the thermal density varies with the dimension, m 1 、m 2 、m 3 And m 4 Is the average value of each dimension, sigma 1 、σ 2 、σ 3 Sum sigma 4 Is the standard deviation of the individual dimensions.
In a further technical solution, before the step of correcting the initial thermodynamic density function by using a gaussian function in the step 3.3.3, the method further includes:
introducing weight factors to adjust the importance of different dimensions, assuming n dimensions, denoted as c 1 、c 2 、…、c n And has a corresponding attribute value a 1 ，a 2 ，…，a n The definition of the initial thermodynamic density function is as follows:
wherein ρ represents the thermodynamic density, w i Is a weight factor for adjusting the importance of the ith dimension. m is m i Is the mean value of the ith dimension, sigma i Is the standard deviation of the i-th dimension.
Another object of the embodiment of the present invention is to provide a thermodynamic density expression system for a multi-dimensional capacity space, based on the above thermodynamic density expression method for a multi-dimensional capacity space, including:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring a city map and geographic information data, and the geographic information data comprises position information and multidimensional capacity data corresponding to the position information;
the standardized processing module is used for carrying out standardized processing on the geographic information data to obtain a plurality of groups of multi-dimensional capacity data, and establishing the position relation between each group of multi-dimensional capacity data and the urban map based on the position information;
and the description module is used for describing the thermodynamic distribution of different areas in the capacity space according to the multiple groups of multi-dimensional capacity data and the position relation between each group of multi-dimensional capacity data and the urban map.
A further object of an embodiment of the invention is an electronic device comprising a memory, in which a computer program is stored, and a processor arranged to run the computer program to perform the above-mentioned method of expressing the thermodynamic density for a multi-dimensional capacity space.
A further object of an embodiment of the present invention is a readable storage medium having stored therein a computer program comprising program code for controlling a process to execute a process comprising the above-described thermodynamic density expression method for a multi-dimensional capacity space.
According to the thermodynamic density expression method and system for the multi-dimensional capacity space, provided by the embodiment of the invention, the urban map and the geographic information data are obtained, the geographic information data are subjected to standardized processing to obtain a plurality of groups of multi-dimensional capacity data, the position relation between each group of multi-dimensional capacity data and the urban map is established based on the position information, and then the thermodynamic distribution of different areas in the capacity space is described according to the plurality of groups of multi-dimensional capacity data and the position relation between each group of multi-dimensional capacity data and the urban map, so that data visualization is realized, data trend is more intuitively seen, and insight and decision support for the urban capacity space are obtained.
Drawings
FIG. 1 is a flow chart of a thermodynamic density expression method for a multi-dimensional capacity space according to an embodiment of the present invention;
FIG. 2 is a graph of thermodynamic distribution effects provided by a thermodynamic density expression method for a multi-dimensional capacity space according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a thermodynamic density expression system for a multi-dimensional capacity space according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
As shown in fig. 1, a thermodynamic density expression method for a multi-dimensional capacity space according to an embodiment of the present invention includes the following steps:
step 1, acquiring a city map and geographic information data, wherein the geographic information data comprises position information and multidimensional capacity data corresponding to the position information. For example, a geographic information data set of a city may be obtained, which includes location information (e.g., latitude and longitude) and a plurality of capability data (e.g., population density, traffic flow, environmental index, etc.) corresponding to the location information.
And step 2, carrying out standardization processing on the geographic information data to obtain a plurality of groups of multi-dimensional capacity data, and establishing the position relation between each group of multi-dimensional capacity data and the urban map based on the position information. The normalization process can unify the value ranges of different capability data, so that each capability data has comparability. For example, population density data is normalized and converted to a value between 0 and 1. The set of multi-dimensional capability data may be correlated to location points in the urban map for subsequent thermodynamic density calculations.
And 3, describing the thermodynamic distribution of different areas in the capacity space according to the multiple groups of multi-dimensional capacity data and the position relation between each group of multi-dimensional capacity data and the urban map. The thermodynamic distribution of different regions in capacity space can be described using a multi-dimensional thermodynamic density function. The multidimensional thermodynamic density function may be based on a gaussian kernel function, calculating the thermodynamic density by taking into account the weights and standard deviations of the plurality of capability data. In the implementation, the weight and standard deviation of the data with different capacities can be adjusted according to the actual demands so as to obtain the thermodynamic density distribution which is more in line with the actual situation.
In the embodiment of the present invention, taking traffic planning of a city as an example, it is assumed that location information (longitude and latitude) of the city and a plurality of capability data corresponding to the location information are obtained, including road density, traffic flow, number of bus stops, and the like. First, these capability data are normalized and converted to values between 0 and 1 for comparison and comprehensive analysis. Then, the positional relationship between each group of multi-dimensional capability data and the urban map is established, and the capability data is corresponding to the position points in the map. Finally, according to the multi-group multi-dimensional capability data and the position relation, describing the thermodynamic distribution situation of different areas in the capability space by using the multi-dimensional thermodynamic density function, as shown in fig. 2, thermodynamic diagrams can be generated to show the thermodynamic densities of the different areas in the traffic plan, wherein the expression effects of the final thermodynamic density distribution are different according to the different dimensions selected by the actual situation, for example, in fig. 2, the registering address of the scientific enterprise is selected as the data of one dimension in the multi-dimensional capability data, at this time, the chromaticity distinction of the image in the diagram represents the scientific enterprise distribution situation, the dark place represents the enterprise distribution densely, the light place represents the enterprise distribution relatively sparsely, and in addition, in order to more obviously observe the data of different dimensions in the actual application, the more obvious colors such as red, green and the like can be adopted for the thermodynamic diagrams, and the embodiment of the invention is not particularly limited.
As a preferred embodiment of the present invention, the step 3 includes the following specific steps:
and 3.1, determining the space coordinates of the urban map. In this step, the coordinate system type of the city map, for example, a latitude and longitude coordinate system or a UTM coordinate system, may be determined according to the map type used.
And 3.2, establishing the corresponding relation between each group of multi-dimensional capacity data and the space coordinates based on the position information. In this step, the position information may be mapped with the spatial coordinates to establish correspondence between the sets of multi-dimensional capability data and the spatial coordinates. For example, for a longitude and latitude coordinate system, longitude and latitude values may be used to represent position information and be converted into coordinate values in a plane rectangular coordinate system, so as to establish a correspondence between the sets of multi-dimensional capability data and the spatial coordinates.
And 3.3, constructing a thermodynamic density function according to the corresponding relation between the multi-dimensional capacity data and the space coordinates so as to express thermodynamic distribution of different areas. In this step, the thermodynamic density function may be calculated using a multi-dimensional gaussian kernel function taking into account the weights and standard deviations of the plurality of capacity data. For example, weighted multidimensional gaussian kernel functions can be used, wherein the weight factors and standard deviations of different dimensions can be adaptively adjusted according to actual data distribution characteristics so as to obtain thermodynamic density distribution which is more in line with actual conditions.
In embodiments of the invention, the method describes the thermodynamic density by defining a multiple function with each dimension of the capacity space as an independent variable.
In particular, the method comprises the steps of,assuming that the capacity space has n dimensions, the value range of each dimension is [ a ] 1 ,b 1 ],[a 2 ,b 2 ],…,[a n ,b n ]. The multidimensional thermodynamic density function can be expressed as:
ρ(c 1 ,c 2 ,…,c n )＝f(c 1 ,c 2 ,…,c n )
wherein ρ represents the thermodynamic density, c 1 、c 2 、…、c n Representing the value of each dimension in the capacity space, f is a multiple function describing how the thermodynamic density varies with each dimension.
The choice of the multidimensional thermodynamic density function can be determined according to specific application scenarios and requirements. One common option is to use a gaussian function to describe the thermodynamic density. The gaussian function has a smooth characteristic and can better describe the distribution in the capacity space. Specifically, the gaussian function can be expressed as:
wherein m is 1 、m 2 、…、m n Is the average value of each dimension, sigma 1 、σ 2 、…、σ n Is the standard deviation of the individual dimensions.
By defining a multi-dimensional thermodynamic density function, quantification and comparison of thermodynamic densities can be performed for different regions in the capacity space. This method of expression can provide more comprehensive information that helps analyze and understand the characteristics and distribution of the multi-dimensional capacity space.
Based on the above, the step 3.3 includes the following specific steps:
and 3.3.1, setting the thermodynamic distribution to be expressed as n dimensions, wherein the value range of each dimension is [ a, b ]. In this step, the dimension of the thermodynamic distribution to be expressed and the range of values for each dimension can be determined. For example, if it is desired to express a three-dimensional thermodynamic distribution, n=3 may be set, and the range of values for each dimension may be determined as [ a, b ].
And 3.3.2, constructing an initial thermodynamic density function. In this step, the initial thermodynamic density function based on the gaussian kernel function can be used to describe the thermodynamic distribution of the different regions. The initial thermodynamic density function may be set according to a range of values of the multi-dimensional capability data and a range of the spatial coordinates.
And 3.3.3, correcting the initial thermodynamic density function by using a Gaussian function to obtain a smooth thermodynamic density function. In this step, the initial thermodynamic density function may be modified by adjusting parameters of the gaussian function, such as the mean and standard deviation, to obtain a smoothed and realistic thermodynamic density function. By properly adjusting parameters of the Gaussian function, the smoothing of the thermodynamic density function can be realized, so that the thermodynamic density function can describe thermodynamic distribution of different areas in the capacity space more accurately.
In the embodiment of the invention, through the steps, a smooth thermodynamic density function for expressing the thermodynamic distribution of different areas can be constructed. The method can be used in the fields of city planning, geographic information systems, environment management and the like, and helps decision makers of decision making and resource allocation to more accurately understand and analyze the thermodynamic distribution situation in the multi-dimensional capacity space.
In the field of multidimensional thermodynamic expression, genojson is a data structure for representing geographic information that contains geometric shapes (e.g., points, lines, planes) of geographic elements and attribute information. In the thermodynamic density expression method oriented to the multi-dimensional capacity space, the geometry of genojson can be converted into position information in the capacity space, and the attribute information of the genojson is used as other dimensions of a thermodynamic density function.
The data structure of genojson is integrated into a thermodynamic density expression method facing to a multi-dimensional capacity space, and can be realized by expanding input parameters of a thermodynamic density function.
Specifically, assuming that each element in genojson contains one location attribute and other attributes, the location attribute can be mapped to a coordinate dimension in the capacity space, while the other attributes can be mapped to other dimensions of the thermodynamic density function.
For example, assume that elements in genojson represent population densities of different cities, where location attribute is latitude and longitude, and other attributes are population count, average income, etc. Longitude and latitude can be mapped to the coordinate dimension of the capacity space, and attributes such as population number, average income and the like can be used as other dimensions of the thermodynamic density function.
In this way, the data structure of genojson can be utilized to describe information of positions and other dimensions in the multi-dimensional capacity space, and thus the thermodynamic density expression method is applied to analysis and visualization of geographic information. This may facilitate a better understanding and comparison of thermodynamic distributions in the multi-dimensional capacity space.
Based on this, the step of correcting the initial thermal density function with a gaussian function to obtain a smoothed thermal density function, further comprises:
if there is a dimension of genojson data map in the n dimensions of the thermodynamic distribution, the thermodynamic density is expressed as follows:
wherein ρ represents the thermodynamic density, c 1 And c 2 Is the coordinate dimension in the capability space, a 1 And a 2 Is another attribute in genojson, f is a multiple function describing how the thermal density varies with these dimensions, m 1 、m 2 、m 3 And m 4 Is the average value of each dimension, sigma 1 、σ 2 、σ 3 Sum sigma 4 Is the standard deviation of the individual dimensions.
In practice, it is assumed that there is a genojson dataset that contains multiple elements, each element having a location attribute and multiple other attributes. These data are incorporated into the thermodynamic density representation method for the multi-dimensional capacity space.
First, the location attributes in genojson need to be mapped to the coordinate dimensions in the capability space. Suppose genoThe location attributes in json are two-dimensional latitude and longitude coordinates (lat, lon) that can be mapped to two coordinate dimensions c in capacity space 1 And c 2 。
Next, other attributes in genojson are taken as other dimensions of the thermodynamic density function. Other attributes assumed to be an element are a 1 And a 2 They are then taken as two additional dimensions of the thermodynamic density function.
Then, the multidimensional thermodynamic density function can be expressed as:
ρ(c 1 ,c 2 ,a 1 ,a 2 )＝f(c 1 ,c 2 ,a 1 ,a 2 )
wherein ρ represents the thermodynamic density, c 1 And c 2 Is the coordinate dimension in the capability space, a 1 And a 2 Is another attribute in genojson, and f is a multiple function that describes how the thermodynamic density varies with these dimensions.
For the selection of a particular thermodynamic density function, a gaussian function may be used to describe the thermodynamic density. The gaussian function selected is assumed to have the form:
wherein m is 1 、m 2 、m 3 And m 4 Is the average value of each dimension, sigma 1 、σ 2 、σ 3 Sum sigma 4 Is the standard deviation of the individual dimensions.
By defining such a multi-dimensional thermodynamic density function, quantification and comparison of thermodynamic densities can be performed for different regions in capacity space. For example, the thermodynamic density distribution of different regions in the capacity space can be obtained by calculating the values of the function in different positions and attribute dimensions.
In summary, by integrating the genojson data structure into the thermodynamic density expression method facing the multi-dimensional capacity space, the distribution situation of the geographic information data in the capacity space can be described by using a multi-dimensional thermodynamic density function, and analysis and visualization can be performed. This allows a better understanding and comparison of the thermodynamic distribution in the multi-dimensional capacity space.
As a preferred embodiment of the present invention, when the thermodynamic density expression method facing the multi-dimensional capacity space is adopted, the above formula can be improved and innovated to better adapt to the actual application scenario, and before the step of correcting the initial thermodynamic density function by using a gaussian function, the method further comprises:
introducing weight factors to adjust the importance of different dimensions, assuming n dimensions, denoted as c 1 、c 2 、…、c n And has a corresponding attribute value a 1 ，a 2 ，…，a n The definition of the initial thermodynamic density function is as follows:
wherein ρ represents the thermodynamic density, w i Is a weight factor for adjusting the importance of the ith dimension. m is m i Is the mean value of the ith dimension, sigma i Is the standard deviation of the i-th dimension.
This improved approach may allow for differences in the contributions of different dimensions to the thermodynamic density. By adjusting the weight factors, the influence of different dimensions can be emphasized or weakened according to actual demands, so that the thermodynamic distribution situation in the multi-dimensional capacity space can be reflected better.
In addition, an adaptive standard deviation can be introduced to better handle data distribution characteristics in different dimensions. The standard deviation used in conventional gaussian functions is fixed, but in practical applications there may be differences in the data distribution across the different dimensions. Thus, an adaptive standard deviation may be introduced such that the standard deviation can be adjusted according to the data distribution in different dimensions.
Specifically, a kernel density estimation method may be used to estimate the data distribution in each dimension, and the estimated standard deviation is taken as the adaptive standard deviation. Therefore, the thermodynamic density function can be more flexibly adapted to the data distribution conditions in different dimensions.
In a word, by introducing the weight factors and the adaptive standard deviation, the thermodynamic density expression method facing the multi-dimensional capacity space can be improved and innovated so as to better adapt to the actual application scene. This can improve the accuracy and interpretability of thermodynamic density representation to better understand and compare thermodynamic distribution in a multi-dimensional capacity space.
As shown in fig. 3, a thermodynamic density expression system for a multi-dimensional capability space according to an embodiment of the present invention is based on the above-mentioned thermodynamic density expression method for a multi-dimensional capability space, and includes:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring a city map and geographic information data, and the geographic information data comprises position information and multidimensional capacity data corresponding to the position information;
the standardized processing module is used for carrying out standardized processing on the geographic information data to obtain a plurality of groups of multi-dimensional capacity data, and establishing the position relation between each group of multi-dimensional capacity data and the urban map based on the position information;
and the description module is used for describing the thermodynamic distribution of different areas in the capacity space according to the multiple groups of multi-dimensional capacity data and the position relation between each group of multi-dimensional capacity data and the urban map.
A further embodiment of the invention provides an electronic device comprising a memory having a computer program stored therein and a processor configured to run the computer program to perform the above-described multi-dimensional capability space oriented thermodynamic density expression method.
In a further embodiment of the invention, a readable storage medium is provided, in which a computer program is stored, the computer program comprising program code for controlling a process to execute a process comprising the above-described thermodynamic density expression method for a multi-dimensional capacity space.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The thermodynamic density expression method for the multi-dimensional capacity space is characterized by comprising the following steps of:
step 1, acquiring a city map and geographic information data, wherein the geographic information data comprises position information and multidimensional capacity data corresponding to the position information;
step 2, carrying out standardization processing on the geographic information data to obtain a plurality of groups of multi-dimensional capacity data, and establishing the position relation between each group of multi-dimensional capacity data and the urban map based on the position information;
and 3, describing the thermodynamic distribution of different areas in the capacity space according to the multiple groups of multi-dimensional capacity data and the position relation between each group of multi-dimensional capacity data and the urban map.
2. The thermodynamic density expression method for a multi-dimensional capacity space according to claim 1, wherein the step 3 comprises the following specific steps:
step 3.1, determining the space coordinates of the urban map;
step 3.2, establishing the corresponding relation between each group of multi-dimensional capacity data and the space coordinates based on the position information;
and 3.3, constructing a thermodynamic density function according to the corresponding relation between the multi-dimensional capacity data and the space coordinates so as to express thermodynamic distribution of different areas.
3. The thermodynamic density expression method for a multi-dimensional capacity space according to claim 2, wherein the step 3.3 comprises the following specific steps:
step 3.3.1, setting the thermodynamic distribution to be expressed as n dimensions, wherein the value range of each dimension is [ a, b ];
step 3.3.2, constructing an initial thermodynamic density function;
and 3.3.3, correcting the initial thermodynamic density function by using a Gaussian function to obtain a smooth thermodynamic density function.
4. A thermodynamic density expression method for a multi-dimensional capacity space according to claim 3, wherein in the step 3.3.2, the initial thermodynamic density expression is as follows:
ρ(c 1 ,c 2 ,…,c n )＝f(c 1 ,c 2 ,…,c n )
wherein ρ represents the thermodynamic density, c 1 、c 2 、…、c n Representing the values of the dimensions in the capacity space, f is a multiple function describing the state of the thermodynamic density as the dimensions change.
5. The method of claim 4, wherein in the step 3.3.3, the thermodynamic density function expression is as follows:
wherein m is 1 、m 2 、…、m n Is the average value of each dimension, sigma 1 、σ 2 、…、σ n Is the standard deviation of the individual dimensions.
6. The method of claim 5, wherein in step 3.3.3, if there is a dimension of genojson data mapping in n dimensions of the thermodynamic distribution, the thermodynamic density is expressed as follows:
wherein c 1 And c 2 Is the coordinate dimension in the capability space, a 1 And a 2 Is genojsother properties in on, f is a multiple function describing how the thermal density varies with the dimension, m 1 、m 2 、m 3 And m 4 Is the average value of each dimension, sigma 1 、σ 2 、σ 3 Sum sigma 4 Is the standard deviation of the individual dimensions.
7. The method of claim 6, further comprising, prior to the step of correcting the initial thermodynamic density function with a gaussian function in step 3.3.3:
introducing weight factors to adjust the importance of different dimensions, assuming n dimensions, denoted as c 1 、c 2 、…、c n And has a corresponding attribute value a 1 ，a 2 ，…，a n The definition of the initial thermodynamic density function is as follows:
wherein ρ represents the thermodynamic density, w i Is a weight factor for adjusting the importance of the ith dimension, m i Is the mean value of the ith dimension, sigma i Is the standard deviation of the i-th dimension.
8. A multi-dimensional capacity space oriented thermodynamic density expression system based on the multi-dimensional capacity space oriented thermodynamic density expression method according to any one of the preceding claims 1-7, characterized by comprising:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring a city map and geographic information data, and the geographic information data comprises position information and multidimensional capacity data corresponding to the position information;
the standardized processing module is used for carrying out standardized processing on the geographic information data to obtain a plurality of groups of multi-dimensional capacity data, and establishing the position relation between each group of multi-dimensional capacity data and the urban map based on the position information;
and the description module is used for describing the thermodynamic distribution of different areas in the capacity space according to the multiple groups of multi-dimensional capacity data and the position relation between each group of multi-dimensional capacity data and the urban map.
9. An electronic device based on the multi-dimensional capability space oriented thermodynamic density expression method of any one of the preceding claims 1-7, characterized by comprising a memory and a processor, the memory having stored therein a computer program, the processor being arranged to run the computer program to perform the multi-dimensional capability space oriented thermodynamic density expression method.
10. A readable storage medium, based on the multi-dimensional capability space oriented thermodynamic density expression method of any one of the preceding claims 1-7, characterized in that the readable storage medium has stored therein a computer program comprising program code for controlling a process to execute a process comprising the multi-dimensional capability space oriented thermodynamic density expression method.
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