Intelligent module area site selection method and apparatus

Abstract

The application provides a kind of intelligent module area site selection method and device, comprising the following steps: obtaining the grid set of the intelligent module set to be selected and the grid set of the area to be selected;Determine the environment characteristic index and the people flow characteristic index of the first grid in the grid set based on the grid set;According to the environment characteristic index, people flow characteristic index and the intelligent module set of the first grid, determine the module characteristic index of the first intelligent module in the intelligent module set corresponding to the first grid;According to the module characteristic index, the environment characteristic index, the people flow characteristic index, determine the target intelligent module of the area to be selected.The application of a kind of intelligent module area site selection method and device from the environment and people flow of two angles of area scene, consider the matching degree between intelligent module and two respectively, ensure the rationality of intelligent building site selection;And application principal component analysis method ensures the reliability and accuracy of data;Improve site selection efficiency.

Description

Technical Field

[0001] This invention relates to the field of big data, and in particular to a method and apparatus for intelligent module area location selection. Background Technology

[0002] In today's Internet of Things era, where everything is interconnected, the acquisition of regional data not only relies on measurements from cloud-based hardware and software systems, but also on the sensing terminals inherent in smart buildings themselves, which can perform data preprocessing, computation, and simple decision-making. Therefore, in order to collect and analyze effective regional data more accurately, determining the location of smart buildings has become a primary decision-making requirement in the construction of smart cities.

[0003] In existing technologies, the site selection of intelligent buildings mainly considers two aspects: construction needs and constraints. Construction needs only consider the objective environment of the site selection and do not pay enough attention to human factors such as pedestrian flow. Constraints mainly involve the analysis of the relationship between intelligent buildings. This analysis can only solve the site selection problem of intelligent building cluster mode, but it is difficult to provide effective analysis methods for non-cluster mode.

[0004] Therefore, how to comprehensively consider and determine the location of smart buildings is an urgent problem to be solved. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a method and apparatus for selecting the location of intelligent modules, so as to solve the problem that the prior art fails to fully consider and determine the location of intelligent buildings.

[0006] To achieve the above and other related objectives, the present invention provides a method and apparatus for intelligent module area location selection, comprising the following steps: obtaining a set of intelligent modules to be selected and a grid set of areas to be selected; determining environmental characteristic indicators and pedestrian flow characteristic indicators of a first grid in the grid set based on the grid set; the first grid being any grid in the grid set; determining the module characteristic indicators of a first intelligent module in the intelligent module set corresponding to the first grid based on the environmental characteristic indicators, pedestrian flow characteristic indicators, and the intelligent module set; the first intelligent module being any intelligent module in the intelligent module set; and determining the target intelligent module for the area to be selected based on the module characteristic indicators, the environmental characteristic indicators, and the pedestrian flow characteristic indicators.

[0007] In one embodiment of the present invention, the intelligent module set includes modules with building energy consumption values ​​using different energy sources; the grid set is a grid set determined based on the site selection area according to a preset division rule.

[0008] In one embodiment of the present invention, the environmental characteristic indicators include area, foundation depth, geographical location, energy information, and point of interest information; the human flow characteristic indicators include human flow level, human interest in the area, human consumption level, and human consumption tendency.

[0009] In one embodiment of the present invention, the module characteristic indicators include energy characteristic indicators, cost characteristic indicators, and revenue characteristic indicators; determining the module characteristic indicators of the first intelligent module in the intelligent module set corresponding to the first grid based on the environmental characteristic indicators, pedestrian flow characteristic indicators, and the intelligent module set includes: determining the energy characteristic indicators of the first intelligent module corresponding to the first grid based on the environmental characteristic indicators of the first grid and the building energy consumption value of the first intelligent module, applying preset statistical rules; determining the cost characteristic indicators of the first intelligent module corresponding to the first grid based on the pedestrian flow characteristic indicators of the first grid and the first intelligent module, applying preset cost calculation rules; and determining the revenue characteristic indicators of the first intelligent module corresponding to the first grid based on the environmental characteristic indicators, pedestrian flow characteristic indicators, and the first intelligent module, applying preset revenue-generating rules.

[0010] In one embodiment of the present invention, determining the target intelligent module of the proposed location area based on the module feature indicators, the environmental feature indicators, and the pedestrian flow feature indicators includes: determining a first matching degree between each indicator in the module feature indicators and each indicator in the environmental feature indicators by applying a preset module and environment matching evaluation rule based on the module feature indicators and the environmental feature indicators; determining a second matching degree between each indicator in the module feature indicators and each indicator in the pedestrian flow feature indicators by applying a preset module and pedestrian flow matching evaluation rule based on the module feature indicators and the pedestrian flow feature indicators; determining the environmental matching degree between the first intelligent module and the first grid and the pedestrian flow matching degree between the first intelligent module and the first grid by applying principal component analysis based on the first matching degree and the second matching degree; and determining the target intelligent module of the proposed location area by traversing the intelligent module set and the grid set based on the environmental matching degree and the pedestrian flow matching degree.

[0011] In one embodiment of the present invention, the division rule is to determine the interval information of the site selection area according to a preset relationship between the density and interval of the site selection area; and to divide the site selection area into equally spaced grids according to the interval information.

[0012] Correspondingly, the present invention provides an intelligent module area location device, comprising: an acquisition module, configured to acquire a set of intelligent modules to be selected and a grid set of the area to be selected; a first processing module, configured to determine environmental characteristic indicators and pedestrian flow characteristic indicators of a first grid in the grid set based on the grid set; wherein the first grid is any grid in the grid set; a second processing module, configured to determine the module characteristic indicators of a first intelligent module in the intelligent module set corresponding to the first grid based on the environmental characteristic indicators and pedestrian flow characteristic indicators of the first grid and the intelligent module set; wherein the first intelligent module is any intelligent module in the intelligent module set; and a determination module, configured to determine the target intelligent module of the area to be selected based on the module characteristic indicators, the environmental characteristic indicators, and the pedestrian flow characteristic indicators.

[0013] In one embodiment of the present invention, the intelligent module set includes modules with building energy consumption values ​​using different energy sources; the grid set is a grid set determined based on the site selection area according to a preset division rule.

[0014] The present invention provides a storage medium on which a computer program is stored, which, when executed by a processor, implements the above-described intelligent module region addressing method.

[0015] The present invention provides a regional addressing platform, including a memory for storing a computer program; and a processor for running the computer program to implement the above-described intelligent module regional addressing method.

[0016] As described above, the intelligent module area location method and apparatus of the present invention have the following beneficial effects:

[0017] (1) In the site selection process, we consider the environment and the flow of people in the area, and the matching degree between the intelligent modules and the two, so as to ensure that the intelligent building can effectively obtain a reasonable site location regardless of whether it is a cluster mode or a non-cluster mode.

[0018] (2) In processing the matching degree between intelligent modules and environment and people flow, principal component analysis is applied to ensure the reliability and accuracy of data.

[0019] (3) It can shorten the time spent on site selection and improve the site selection efficiency of intelligent buildings.

[0020] (4) By using the grid to divide the area, the area can be effectively covered, making the area division more flexible and complete. Attached Figure Description

[0021] Figure 1 The flowchart shown is an embodiment of the intelligent module region location method of the present invention.

[0022] Figure 2 The diagram shown is a structural schematic of the intelligent module area location device of the present invention in one embodiment.

[0023] Figure 3 The image shown is an example of a region location platform in one embodiment of the intelligent module region location device of the present invention.

[0024] Component designation explanation

[0025] 21 Acquisition Module

[0026] 22 First Processing Module

[0027] 23 Second Processing Module

[0028] 24. Determine the module

[0029] 31 processors

[0030] 32 Memory Detailed Implementation

[0031] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0032] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0033] The intelligent module area location selection method and apparatus of the present invention considers both the environment and pedestrian flow of the area during the location selection process, taking into account the matching degree between the intelligent module and both, thereby ensuring that the intelligent building can effectively obtain a reasonable location regardless of whether it is in cluster mode or non-cluster mode. Furthermore, in processing the matching degree between the intelligent module and the environment and pedestrian flow, principal component analysis is applied to ensure the reliability and accuracy of the data. In addition, the present invention utilizes the method of grid division to effectively cover all locations in the area, making the area division more flexible and complete. The processing method of the present invention can shorten the location selection time and improve the location selection efficiency of intelligent buildings.

[0034] like Figure 1As shown, in one embodiment, the intelligent module region location method of the present invention includes the following steps:

[0035] Step S1: Obtain the set of smart modules to be selected and the grid set of the area to be located.

[0036] Specifically, the intelligent module set includes modules with building energy consumption values ​​using different energy sources; the grid set is a set of grids determined based on the site selection area according to preset division rules.

[0037] More specifically, the candidate site area for the smart modules to be deployed is divided into multiple grids. The division rule is to determine the interval information of the candidate site area based on a preset relationship between the density and interval of the candidate site area; and then, based on the interval information, the candidate site area is divided into equally spaced grids. Here, the density of the candidate site area and the interval are inversely proportional. The density of the candidate site area refers to the number of buildings suitable for deployment within a certain area. For example, wilderness is not suitable for building deployment, so the area density is low, and the interval is set large; cities are suitable for building deployment, so the area density is high, and the interval is set small. Furthermore, after dividing into equally spaced grids, the grids can be sequentially numbered, for example, from S1 to Sn, thus allowing for more flexible classification and division of the area.

[0038] Step S2: Based on the grid set, determine the environmental feature indicators and pedestrian flow feature indicators of the first grid cell in the grid set; the first grid cell is any grid cell in the grid set.

[0039] Specifically, based on raster sets, web crawling technology is used to extract point-of-interest (POI) data for the region from the map. Then, using a Geographic Information System (GIS), environmental and pedestrian flow indicators corresponding to these POIs are collected. The environmental indicators include region area, foundation depth, geographical location, energy information, and POI information. The pedestrian flow indicators include pedestrian traffic levels, the degree of interest of the population in the region, the population's consumption level, and the population's consumption tendency. For example, after obtaining the pedestrian traffic at the POIs through GIS, the pedestrian traffic level for the region is statistically determined. Simultaneously, a higher pedestrian traffic in a region indicates a higher consumption tendency, thus the population's consumption tendency is statistically determined. Furthermore, a greater number of POIs in the region reflects a higher degree of interest in the region, thus the population's degree of interest in the region is statistically determined.

[0040] Step S3: Based on the environmental feature indicators and pedestrian flow feature indicators of the first grid and the intelligent module set, determine the module feature indicators of the first intelligent module in the intelligent module set corresponding to the first grid; the first intelligent module is any intelligent module in the intelligent module set.

[0041] Specifically, the module characteristic indicators include energy characteristic indicators, cost characteristic indicators, and revenue characteristic indicators; based on the environmental characteristic indicators of the first grid and the building energy consumption value of the first intelligent module, a preset statistical rule is applied to determine the energy characteristic indicators of the first intelligent module corresponding to the first grid; based on the pedestrian flow characteristic indicators of the first grid and the first intelligent module, a preset cost calculation rule is applied to determine the cost characteristic indicators of the first intelligent module corresponding to the first grid; based on the environmental characteristic indicators of the first grid, the pedestrian flow characteristic indicators of the first grid, and the first intelligent module, a preset revenue-generating rule is applied to determine the revenue characteristic indicators of the first intelligent module corresponding to the first grid. For example, energy characteristic indicators are determined by comprehensively calculating the building energy consumption value of the first intelligent module and the environmental characteristic indicators of the area where the first grid is located, such as geographical location, energy information, and foundation depth. For instance, if the water energy consumption value of the first intelligent module is A, then when the water energy in the area where the first grid is located is sufficient, the energy characteristic indicator of the first intelligent module corresponding to the first grid is the water energy consumption value A; when the water energy in the area where the first grid is located is insufficient, the energy characteristic indicator of the first intelligent module corresponding to the first grid needs to consider the additional energy consumption and energy consumption costs. Cost characteristic indicators are determined by calculating the human maintenance cost corresponding to the first grid based on the pedestrian flow characteristic indicators of the first grid, such as pedestrian flow level, population consumption level, and population consumption tendency. Revenue characteristic indicators are determined by virtually deploying the first intelligent module in the first grid and calculating the revenue level of the first intelligent module corresponding to the first grid based on the environmental and pedestrian flow characteristic indicators of the first grid, such as store entry rate and energy environmental benefits.

[0042] Step S4: Determine the target intelligent module for the area to be selected based on the module characteristic indicators, the environmental characteristic indicators, and the pedestrian flow characteristic indicators.

[0043] Specifically, based on the module feature indicators and the environmental feature indicators, a preset module and environment matching evaluation rule is applied to determine the first matching degree between each indicator in the module feature indicators and each indicator in the environmental feature indicators; based on the module feature indicators and the pedestrian flow feature indicators, a preset module and pedestrian flow matching evaluation rule is applied to determine the second matching degree between each indicator in the module feature indicators and each indicator in the pedestrian flow feature indicators; based on the first matching degree and the second matching degree, principal component analysis is applied to determine the environmental matching degree between the first intelligent module and the first grid, as well as the pedestrian flow matching degree between the first intelligent module and the first grid; based on the environmental matching degree and the pedestrian flow matching degree, the intelligent module set and the grid set are traversed to determine the target intelligent module for the area to be selected.

[0044] Specifically, based on module characteristic indicators and environmental characteristic indicators, a preset matching relationship between module and environmental indicators is applied to obtain the first matching degree between each indicator in the module characteristic indicators and each indicator in the environmental characteristic indicators. For example, a value of 0-1 is used to divide the values, where 0 represents no match and 1 represents a match. Similarly, based on module characteristic indicators and pedestrian flow characteristic indicators, a preset matching relationship between module and pedestrian flow indicators is applied to obtain the second matching degree between each indicator in the module characteristic indicators and each indicator in the pedestrian flow characteristic indicators. After obtaining the initial matching degree, principal component analysis (PCA) is applied to perform weighted analysis on each indicator in the module feature index and each indicator in the environmental feature index to obtain the overall environmental matching degree of the first intelligent module in the first grid. Similarly, PCA is applied to obtain the overall pedestrian flow matching degree of the first intelligent module in the first grid. Then, the overall environmental matching degree and the overall pedestrian flow matching degree of the first intelligent module in the first grid are combined and weighted to obtain the overall matching degree of the first intelligent module in the first grid. Based on the overall matching degree, the set of intelligent modules to be selected and the grid set of the site selection area are traversed to obtain the intelligent module with the highest matching degree with each grid of the site selection area, thereby determining the target intelligent module of the site selection area. In addition, since different intelligent modules have different importance in the construction process, the weight coefficients can be adjusted according to the actual situation when determining the overall matching degree of the first intelligent module in the first grid. The weight coefficients consider factors such as the importance of the intelligent module and the importance of the grid. Furthermore, in the extended scenario, the perspective of pedestrian flow feature index can be replaced by any variable related to human activities other than the objective environment, such as traffic flow. Other cases will not be elaborated further.

[0045] like Figure 2 As shown, in one embodiment, the intelligent module region addressing device of the present invention includes:

[0046] The acquisition module 21 is used to acquire the set of smart modules to be selected and the grid set of the area to be selected;

[0047] The first processing module 22 is used to determine the environmental characteristic indicators and pedestrian flow characteristic indicators of the first grid cell in the grid set based on the grid set; the first grid cell is any grid cell in the grid set;

[0048] The second processing module 23 is used to determine the module feature index of the first intelligent module in the intelligent module set corresponding to the first grid based on the environmental feature index, the pedestrian flow feature index and the intelligent module set; the first intelligent module is any intelligent module in the intelligent module set.

[0049] The determination module 24 is used to determine the target intelligent module of the area to be selected based on the module feature indicators, the environmental feature indicators, and the pedestrian flow feature indicators.

[0050] The intelligent module set includes modules that use different energy sources and have different building energy consumption values; the grid set is a set of grids determined based on the site selection area according to a preset division rule.

[0051] The technical features of the intelligent module area location device in this embodiment are basically the same as the principles of each step in the intelligent module area location method in Embodiment 1. The technical contents that are common between the method and the device will not be repeated.

[0052] The storage medium of the present invention stores a computer program, which, when executed by a processor, implements the above-described intelligent module region addressing method.

[0053] like Figure 3 As shown, in one embodiment, the regional addressing platform of the present invention includes a processor 31 and a memory 32.

[0054] The memory 32 is used to store computer programs.

[0055] The memory 32 includes various media capable of storing program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disk.

[0056] The processor 31 is connected to the memory 32 and is used to execute the computer program stored in the memory 32 so that the area addressing platform can execute the above-described intelligent module area addressing method.

[0057] Preferably, the processor 31 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0058] In summary, the intelligent module area location method and apparatus of the present invention considers both the environment and pedestrian flow of the area during the location selection process, taking into account the matching degree between the intelligent module and both, thereby ensuring that intelligent buildings can effectively obtain reasonable locations regardless of whether they are in cluster or non-cluster mode. Furthermore, principal component analysis is applied to ensure the reliability and accuracy of the data in processing the matching degree between the intelligent module and the environment and pedestrian flow. In addition, the present invention utilizes a grid-based area division method to effectively cover all locations within the area, making the area division more flexible and comprehensive. The processing method of the present invention can shorten the location selection time and improve the location selection efficiency of intelligent buildings. Therefore, the present invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0059] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for intelligent module area location selection, characterized in that, The regional site selection method includes the following steps: Obtain the set of smart modules to be selected and the grid set of the area to be located; Based on the grid set, determine the environmental characteristic indicators and pedestrian flow characteristic indicators of the first grid cell in the grid set; the first grid cell is any grid cell in the grid set; Based on the environmental characteristic indicators and pedestrian flow characteristic indicators of the first grid and the intelligent module set, the module characteristic indicators of the first intelligent module in the intelligent module set corresponding to the first grid are determined; the first intelligent module is any intelligent module in the intelligent module set; the module characteristic indicators include energy characteristic indicators, cost characteristic indicators and revenue characteristic indicators; Based on the module characteristic indicators, the environmental characteristic indicators, and the pedestrian flow characteristic indicators, the target intelligent module for the proposed site area is determined.

2. The method according to claim 1, characterized in that, The intelligent module set includes modules that use different energy sources and have different building energy consumption values; the grid set is a set of grids determined based on the site selection area and according to preset division rules.

3. The method according to claim 2, characterized in that, The environmental characteristic indicators include area, foundation depth, geographical location, energy information, and points of interest information; the human flow characteristic indicators include human flow level, human interest in the area, human consumption level, and human consumption tendency.

4. The method according to claim 3, characterized in that, The step of determining the module feature indicators of the first intelligent module corresponding to the first grid based on the environmental feature indicators, pedestrian flow feature indicators, and the intelligent module set includes: Based on the environmental characteristic indicators of the first grid and the building energy consumption value of the first intelligent module, the energy characteristic indicators of the first intelligent module corresponding to the first grid are determined by applying preset statistical rules. Based on the pedestrian flow characteristic indicators of the first grid and the first intelligent module, the cost characteristic indicators of the first intelligent module corresponding to the first grid are determined by applying the preset cost calculation rules. Based on the environmental characteristic indicators of the first grid, the pedestrian flow characteristic indicators of the first grid, and the first intelligent module, a preset revenue distribution rule is applied to determine the revenue characteristic indicators of the first intelligent module corresponding to the first grid.

5. The method according to claim 4, characterized in that, The step of determining the target intelligent module for the proposed location area based on the module characteristic indicators, the environmental characteristic indicators, and the pedestrian flow characteristic indicators includes: Based on the module feature indicators and the environmental feature indicators, a preset module and environment matching evaluation rule is applied to determine the first matching degree between each indicator in the module feature indicators and each indicator in the environmental feature indicators. Based on the module feature indicators and the pedestrian flow feature indicators, a preset module and pedestrian flow matching evaluation rule is applied to determine the second matching degree between each indicator in the module feature indicators and each indicator in the pedestrian flow feature indicators. Based on the first matching degree and the second matching degree, principal component analysis is applied to determine the environmental matching degree between the first intelligent module and the first grid, as well as the pedestrian matching degree between the first intelligent module and the first grid. Based on the environmental matching degree and the pedestrian flow matching degree, the set of intelligent modules and the set of grids are traversed to determine the target intelligent modules for the area to be selected.

6. The method according to claim 2, characterized in that, The division rule is to determine the interval information of the site selection area based on the preset relationship between the density and interval of the site selection area; and to divide the site selection area into equally spaced grids based on the interval information.

7. A smart module area location device, characterized in that, include: The acquisition module is used to acquire the set of smart modules to be selected and the grid set of the area to be selected; The first processing module is used to determine the environmental characteristic indicators and pedestrian flow characteristic indicators of the first grid cell in the grid set based on the grid set; the first grid cell is any grid cell in the grid set; The second processing module is used to determine the module feature indicators of the first intelligent module in the intelligent module set corresponding to the first grid based on the environmental feature indicators, pedestrian flow feature indicators, and the intelligent module set; the first intelligent module is any intelligent module in the intelligent module set; the module feature indicators include energy feature indicators, cost feature indicators, and revenue feature indicators. The determination module is used to determine the target intelligent module of the area to be selected based on the module characteristic indicators, the environmental characteristic indicators, and the pedestrian flow characteristic indicators.

8. The apparatus according to claim 7, characterized in that, The intelligent module set includes modules that use different energy sources and have different building energy consumption values; the grid set is a set of grids determined based on the site selection area and according to preset division rules.

9. A storage medium storing program instructions, wherein, When the program instructions are executed, they implement the steps of the intelligent module region location method as described in any one of claims 1 to 6.

10. A regional site selection platform, characterized in that: It includes a memory for storing a computer program; and a processor for running the computer program to implement the steps of the intelligent module region addressing method as described in any one of claims 1 to 6.

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