CN117435790A - Visual data analysis method and system - Google Patents
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Abstract
The invention discloses a visual data analysis method and a visual data analysis system, wherein the visual data analysis method comprises the following steps: acquiring geographical position data of a business district, and forming a first business district map by three-dimensional modeling of the geographical position data of the business district; constructing grids in the first business district map, wherein each grid is configured with an AIoT device for detecting and acquiring a multi-dimensional influence factor data set; carrying out weighted regression statistics on different influence factor values in each grid by adopting a geographic weighted regression algorithm to obtain different influence factor values of each grid; setting a grid fusion index of a business circle block type division criterion, and fusing adjacent grids with influence factor values meeting the grid fusion index according to the grid fusion index to obtain a spatial distribution diagram corresponding to the business circle block type division.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to a visual data analysis method and a visual data analysis system.
Background
The current data analysis mode of the business circle mainly comprises manual collection and manual analysis, such as collection of the flow of people, collection of characteristic data of the flow of people, judgment of the business type of the current position, living people in the range of the business circle and the like, and the data acquisition mode needs independent manual collection and classification, so that the judgment of related data of the business circle needs to consume larger labor cost, the efficiency of the manual analysis mode is lower, larger subjective errors possibly exist, and the objective and accurate visual display of the business circle data is not facilitated. In addition, the traditional business district block judgment mode only depends on artificial subjective judgment, and comprehensive objective analysis related to geographic data is lacking.
Disclosure of Invention
One of the purposes of the invention is to provide a visual data analysis method and a visual data analysis system, wherein the method and the visual data analysis system acquire business turn-related different-dimension data in real time according to the AIoT equipment, provide the AIoT data with different dimensions after data processing, analyze the business turn in real time according to the AIoT data, execute layered visual display, and improve the business turn analysis and data display effects.
The invention further aims to provide a visual data analysis method and a visual data analysis system, wherein the method and the visual data analysis system are used for carrying out geographic data analysis on different positions of a business district according to AIoT data with different dimensions, carrying out data statistics on business district blocks according to the AIoT data by adopting a geographic weighted regression algorithm, and analyzing business class data of different blocks in the business district.
The invention further aims to provide a visual data analysis method and a visual data analysis system, wherein the method and the visual data analysis system construct a gridding area for the regional space of the business district, and grid fusion clustering is carried out on business district blocks of similar types after the geographical weighted regression algorithm analysis is carried out on the AIoT data with different dimensions, so that precisely divided business district block data can be obtained for visual display.
The invention further aims to provide a visual data analysis method and a visual data analysis system, wherein the method and the visual data analysis system are configured with dynamic parameters and static parameters aiming at a geographic weighted regression algorithm, so that a calculation analysis mode of the regression algorithm has more dimensions, and final business district block division is more objective and accurate.
In order to achieve at least one of the above objects, the present invention further provides a visual data analysis method comprising:
acquiring geographical position data of a business district, and forming a first business district map by three-dimensional or two-dimensional modeling of the geographical position data of the business district;
constructing grids in the first business district map, wherein each grid is configured with an AIoT device for detecting and acquiring a multi-dimensional influence factor data set;
carrying out weighted regression statistics on different influence factor values in each grid by adopting a geographic weighted regression algorithm to obtain different influence factor values of each grid;
setting a grid fusion index of a business circle block type division criterion, and fusing adjacent grids with influence factor values meeting the grid fusion index according to the grid fusion index to obtain a spatial distribution diagram corresponding to the business circle block type division.
According to one preferred embodiment of the present invention, the AIoT device includes at least one of a face recognition device, a distance detection device, a human body recognition analysis device, a human flow recognition analysis device, a path recognition device, a voice recognition device, and an image recognition device, and is configured to acquire a set of influence factor data of different dimensions in a grid.
According to another preferred embodiment of the present invention, each grid is provided with own geographic location information, each grid is configured with number information, and in the grid, the AIoT device is adopted to acquire data sets of different influencing factors in a preset time period.
According to another preferred embodiment of the present invention, the influencing factors include dynamic influencing factors and static influencing factors, wherein the dynamic influencing factors are acquired in real time through the AIoT device, and the static influencing factors are acquired in advance.
According to another preferred embodiment of the present invention, the method for performing geo-weighted statistics according to the influencing factors includes:
wherein Y is ij The subscript i represents different detection grids, (u) represents the geographic weighted regression value of the influencing factor under the preset business district type block judgment criterion i ,λ i ) Representing the coordinates of the position of the detection grid, the subscript j representing a different criterion, alpha 0 ，β n Regression parameters under the detection grid i, ZB l And ZB v Respectively representing a static influence factor index value and a dynamic influence factor index value, W l And W is v Respectively representing the weight values of indexes of different types of influencing factors, and p and q respectively represent the weight values of dynamic influencing factors and static influencing factors, epsilon i Is the residual.
According to another preferred embodiment of the present invention, after the static influence factor index value and the dynamic influence factor index value are obtained, the different factor values are subjected to data conversion, and the state data is converted into numerical data, so that all the influence factor values are numerical data, and numerical normalization processing is further adopted for the dynamic influence factor index value, and the calculation method includes: obtaining maximum value ZB of corresponding type influence data max And a minimum value ZB min The current influence factor value is ZBi, the normalized value is
According to another preferred embodiment of the present invention, according to the business district block type division criteria j, the static influence factor type and the dynamic influence factor type of each grid are configured, the weights of the influence factors of the corresponding types are respectively configured, and the analysis of the statistics of the data of the corresponding influence factors is acquired to obtain the corresponding business district block type judgment value Y ij 。
According to another preferred embodiment of the present invention, in the business district block type division criterion j, a grid fusion index Y is configured sj If the current grid is countedThe corresponding business district block type judgment value Y obtained after analysis ij Equal to or greater than grid fusion index Y sj Judging that the current grid meets the fusion standard of the corresponding business district block type, if the adjacent grid Y ij Equal to or greater than grid fusion index Y sj Then the adjacent grid and the current grid are fused into a block.
In order to achieve at least one of the above objects, the present invention further provides a visual data analysis system which performs one of the above visual data analysis methods.
The present invention further provides a computer readable storage medium storing a computer program for execution by a processor to implement a visual data analysis method as described above.
Drawings
FIG. 1 is a flow chart of a visual data analysis method according to the present invention.
FIG. 2 is a schematic diagram of business district block type division according to the present invention.
A first type business district block-1, a second type business district block-2, and a third type business district block-3.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
It will be understood that the terms "a" and "an" should be interpreted as referring to "at least one" or "one or more," i.e., in one embodiment, the number of elements may be one, while in another embodiment, the number of elements may be plural, and the term "a" should not be interpreted as limiting the number.
Referring to fig. 1-2, the invention discloses a visual data analysis method and a visual data analysis system, wherein the visual data analysis method mainly comprises the following steps: the preferred need exists to determine the overall geographic location range of the business turn, which may be a general range, obtained by means including, but not limited to, latitude and longitude positioning. And further building a two-dimensional or three-dimensional model for buildings, roads, shops and the like in the business district, and constructing a first business district map, wherein buildings, shops, schools, factories, shops, roads, entertainment facilities, residential areas and the like with different functions are contained in the range of the first business district map, and the different building facilities can be marked at places corresponding to the two-dimensional or three-dimensional first business district map. Further constructing grids on the first business district map, wherein the AIoT equipment is arranged in each grid, the AIoT equipment collects and analyzes data of corresponding influence factor types in a grid range of the first business district map to obtain different influence factor data analysis results in the grid range, and the invention adopts a geographic weighted regression algorithm to carry out weighted regression on each influence factor value to obtain business district block type division results meeting the requirement of business district block division criteria, and carries out visual display on the business district block type division results.
Specifically, as shown in fig. 2, square grids with the length and width ranging from 50 meters to 100 meters are constructed on the first business district map, adjacent grids are connected through grid edges, the grid areas are provided with the corresponding building, mall, school, factory, store, road, entertainment facility, residential area and other parts of facilities, and the AIoT equipment is used for judging relevant static and dynamic influence parameters existing in the current grid to serve as influence parameters of business district type block division.
The AIoT equipment comprises but is not limited to face recognition equipment, a distance detection device, human body recognition analysis equipment, people flow recognition analysis equipment, path recognition equipment, voice recognition equipment and image recognition equipment, wherein the AIoT equipment can acquire influence factor data in a business district grid and is used for judging the type of a business district of the current grid.
It is worth mentioning that the invention adopts the geographic weighted regression algorithm to correspond to the quotientThe invention sets different types of criteria for the business district block division, wherein the criteria comprise necessary static influence factors and selection of types and numbers of dynamic influence factors, for example, taking judgment criteria J1 of a business district type as an example, the geographic position of a large business district needs to be selected as one of the static influence factors ZB 1 And selecting the preset distance range of the shopping mall as another static influence factor ZB 2 The method comprises the steps of carrying out a first treatment on the surface of the People flow is taken as a dynamic influencing factor ZB 3 And the number and the position layout of the shops around the mall are taken as another dynamic influencing factor ZB 4 The people flow type (adult, infant, etc.) is taken as another dynamic influencing factor ZB 5 The selection manners of the dynamic influence factors and the static influence factors are selected according to different business district block types, and the selection of the influence factors is only illustrative, and the invention is not repeated in detail. And constructing a mall block division judgment criterion of a mall type by the preset type influence factors, presetting the weight W of each influence factor by the circle block judgment criterion, and configuring weight values p and q of dynamic influence factors and static influence factors.
In an optional preferred embodiment of the present invention, the business district type division may be divided into, but not limited to, scenic spot business district, residential area business district, amusement park business district, campus business district, etc., and static influencing factors and dynamic influencing factors of corresponding types and numbers are configured for each type of business district as business district type block division judgment criteria, where the relevant judgment criteria of the dynamic influencing factors and the static influencing factors may be used to fuse the corresponding grids together, so as to obtain an objectively divided business district type block space distribution structure.
The data acquisition modes of the static influence factors and the dynamic influence factors are acquired through AIoT equipment of corresponding types, for example, the AIoT equipment for detecting the distance can be used for measuring the linear distance between the target market and the current grid. The AIoT device which is analyzed through the people flow identification can acquire the people flow data in the current grid, and the AIoT device which is analyzed through the face recognition can acquire the age characteristics of the people flow in the current grid, so as to obtain the information of people flow groups of different age groups in the current grid. The AIoT device through image recognition can recognize the type of shops, the number of shops, etc. within the current grid. The dimension information acquired by the AIoT device is not limited to the exemplary type of the present invention, and the present invention may acquire more dimension factor data according to the specific function of the AIoT device. After the AIoT equipment in the grid acquires the influence data of all different dimensions, the influence data of the different dimensions and the corresponding grid are bound and stored.
Further, after the AIoT equipment in each grid acquires the corresponding influence factor data in a certain time, the corresponding business turn type division data of the corresponding grid is calculated by adopting the following formula:
wherein Y is ij The subscript i represents different detection grids, (u) represents the geographic weighted regression value of the influencing factor under the preset business district type block judgment criterion i ,λ i ) Representing the coordinates of the position of the detection grid, the subscript j representing a different criterion, alpha 0 ，β n Regression parameters under the detection grid i, ZB l And ZB v Respectively representing a static influence factor index value and a dynamic influence factor index value, W l And W is v Respectively representing the weight values of indexes of different types of influencing factors, and p and q respectively represent the weight values of dynamic influencing factors and static influencing factors, epsilon i Is the residual.
The method is used for carrying out normalization processing on the influence factor index ZB and is used for obtaining unified and clean numerical data, wherein the specific method for the normalization processing comprises the following steps: converting state data in the influence factor index ZB into numerical data, wherein the state data comprises, but is not limited to, a corresponding position main body division type in a business district, a business kind, a people flow age group characteristic and the like, and converting state adjustment in the business district into numerical characteristics, wherein the numerical characteristicsMay be limited to a range of 0-1 and values and weights configured for each influencing factor according to the corresponding type of business turn division criteria. Wherein, the dynamic influence factor index with data variation is required to be processed according to the following formula: obtaining maximum value ZB of corresponding type influence data max And a minimum value ZB min The current influence factor value is ZBi, the normalized value is
For example: if the selected main body position in the current business is a school, a school business type division criterion is configured, the corresponding school characteristic value can be set to be 1, the business types can be respectively endowed with different values according to the school student life learning correlation, for example, a stationery shop and a school student life correlation are high, the corresponding value can be configured to be 1, and automobile maintenance shops around the school and the school student life learning correlation are not high, and the influence factor is not configured according to the preset school business type division criterion. According to the correlation of different influence factors, the weight values of the corresponding influence factors are respectively configured, for example, in the school business circles, the stationery shop configuration weight value W can be 0.8, the corresponding restaurant configuration weight value W is 0.3 and the like according to the life and study correlation of school students, the weight value configuration modes are different in the influence factors of different correlations, and the corresponding weight values are preset in the judgment criteria of different business circle types.
That is, the above-described shop type value determination and weight determination will divide the type of buscouple selected based on the location of the subject, and other types of division may include, but are not limited to, luxury buscouple, snack street buscouple, etc. The present invention is not specifically explained for each type of business district block, and the person skilled in the art can implement the method according to the present invention.
The grid fusion index Y is configured in the business district type block division judgment criterion sj After the AIoT equipment of each grid acquires all the influence factor data, calculating a corresponding business district type block according to the geographic weighted regression algorithmGeographical weighted regression value Y for partition judgment ij As the corresponding business district block type judgment value, if the corresponding business district block type judgment value Y obtained after the current grid statistical analysis ij Equal to or greater than grid fusion index Y sj Judging that the current grid meets the fusion standard of the corresponding business district block type, if the adjacent grid Y ij Equal to or greater than grid fusion index Y sj Then the adjacent grid and the current grid are fused into a block. Each current grid is subjected to data comparison with the adjacent grids of four sides of the current grid, and as long as any one adjacent grid meets the grid fusion index Y of the business district type block division judgment criterion sj And fusing the grids of the corresponding sides into the same block.
Referring to fig. 2, a block fusion diagram of different business district types is shown, and three different business district blocks represent coverage areas of different business district types in geographic locations. It should be noted that, in the present invention, the business district dividing block performs active and objective division according to the selected main business district, so that there may be overlapping portions of different types of business districts in space and geographic locations, for example, schools are located near the large-scale mall, so that the business shops near the schools may belong to the business district block range of the large-scale mall at the same time, and also belong to the business district block range of the schools. Therefore, the invention performs layered display aiming at different business district type blocks, namely, overlapping parts are distributed to different business district type blocks, and when a business district main body needs to be selected, the geospatial distribution of the corresponding type business district blocks is respectively displayed.
The invention further adopts a three-dimensional or two-dimensional rendering mode to display the spatial distribution of business district blocks of different types, and a rendering tool can adopt a method including but not limited to V-ray and the like, so that detailed description of how to render is omitted.
The processes described above with reference to flowcharts may be implemented as computer software programs in accordance with the disclosed embodiments of the invention. Embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU). It should be noted that the computer readable medium described in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wire segments, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be understood by those skilled in the art that the embodiments of the present invention described above and shown in the drawings are merely illustrative and not restrictive of the current invention, and that this invention has been shown and described with respect to the functional and structural principles thereof, without departing from such principles, and that any modifications or adaptations of the embodiments of the invention may be possible and practical.
Claims (10)
1. A method of visual data analysis, the method comprising:
acquiring geographical position data of a business district, and forming a first business district map by three-dimensional or two-dimensional modeling of the geographical position data of the business district;
constructing grids in the first business district map, wherein each grid is configured with an AIoT device for detecting and acquiring a multi-dimensional influence factor data set;
carrying out weighted regression statistics on different influence factor values in each grid by adopting a geographic weighted regression algorithm to obtain different influence factor values of each grid;
setting a grid fusion index of a business circle block type division criterion, and fusing adjacent grids with influence factor values meeting the grid fusion index according to the grid fusion index to obtain a spatial distribution diagram corresponding to the business circle block type division.
2. The visual data analysis method according to claim 1, wherein the AIoT device includes at least one of a face recognition device, a human body recognition analysis device, a human flow recognition analysis device, a path recognition device, a voice recognition device, and an image recognition device, and is configured to acquire a set of influence factor data of different dimensions in a grid.
3. The visual data analysis method according to claim 1, wherein each grid is provided with own geographic position information, each grid is configured with number information, and in the grid, the AIoT device is adopted to acquire data sets of different influence factors in a preset time period.
4. The method of claim 1, wherein the influencing factors include dynamic influencing factors and static influencing factors, wherein the dynamic influencing factors are acquired in real time by the AIoT device, and the static influencing factors are acquired in advance.
5. A method of visual data analysis according to claim 1, wherein the method of geo-weighting statistics based on the influencing factors comprises:
wherein Y is ij The subscript i represents different detection grids, (u) represents the geographic weighted regression value of the influencing factor under the preset business district block type judgment criterion i ,λ i ) Representing the coordinates of the position of the detection grid, the subscript j representing a different criterion, alpha 0 ，β n Regression parameters under the detection grid i, ZB l And ZB v Respectively representing a static influence factor index value and a dynamic influence factor index value, W l And W is v Respectively representing the weight values of indexes of different types of influencing factors, and p and q respectively represent the weight values of dynamic influencing factors and static influencing factors, epsilon i Is the residual.
6. The visual data analysis method according to claim 1, wherein after the static influence factor index value and the dynamic influence factor index value are obtained, the different factor values are subjected to data conversion, the state data are converted into numerical data, so that all the influence factor values are numerical data, and numerical normalization processing is further adopted for the dynamic influence factor index value, and the calculation method comprises: obtaining maximum value ZB of corresponding type influence data max And a minimum value ZB min The current influence factor value is ZBi, the normalized value is
7. The visual data analysis method according to claim 1, wherein static influence factor types and dynamic influence factor types of each grid are configured according to the business district block type division criterion j, weights of corresponding type influence factors are configured respectively, and a corresponding business district block type judgment value Y is obtained after analysis of corresponding influence factor data statistics is acquired ij 。
8. The visual data analysis method according to claim 1, wherein a grid fusion index Y is configured in the business district block type division criterion j sj If the corresponding business district block type judgment value Y obtained after the current grid statistical analysis ij Equal to or greater than grid fusion index Y sj Judging that the current grid meets the fusion standard of the corresponding business district block type, and if the current grid meets the fusion standard of the adjacent grid Y ij Equal to or greater than grid fusion index Y sj Then neighboring mesh and current meshThe cells are fused into a block.
9. A visual data analysis system, wherein the system performs one of the above visual data analysis methods.
10. A computer-readable storage medium storing a computer program that is executed by a processor to implement one of the above-described visual data analysis methods.
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| US8095412B1 (en) * | 2008-11-03 | 2012-01-10 | Intuit Inc. | Method and system for evaluating expansion of a business |
| CN109118265A (en) * | 2018-06-27 | 2019-01-01 | 阿里巴巴集团控股有限公司 | Commercial circle determines method, apparatus and server |
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