CN113361852A - Method and device for selecting field address, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a method and a device for selecting a field address, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring an application target of a field address to be selected; establishing an N-layer evaluation system according to an application target of a field address to be selected, and carrying out the following steps: acquiring a first level target and a first evaluation index set of a first layer of the N-layer evaluation system; determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set; determining a first candidate site according to the first score; acquiring an Nth level target and an Nth evaluation index set of an Nth layer of an N-layer evaluation system; repeating the above steps to determine the N candidate field address from the N-1 candidate field addresses; and determining the field address to be selected according to the Nth candidate field address. The invention can solve the problems of unreasonable space-time layout, poor layout precision and overhigh layout cost by objectively and scientifically selecting the field address, thereby improving the reliability of authenticity inspection.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for selecting a field address, electronic equipment and a storage medium.
Background
In recent years, development of spatial information technology represented by remote sensing information products provides a brand-new method and basic data support for resource environment monitoring, global change and sustainable development. In order to better evaluate the uncertainty of the remote sensing product, improve the precision of the remote sensing information product and improve the quality of the remote sensing information product, the authenticity of the remote sensing information product needs to be checked. The authenticity check of the remote sensing information product refers to the comparative analysis of the precision and uncertainty of the remote sensing information product by using relative true values, namely measured values, which can represent the characteristics of a ground target. Ground observation is one of the key steps of authenticity inspection, and how to acquire ground observation data under natural conditions of various climatic zones, complex ecological zones and different underlying surfaces is a problem to be solved urgently.
The ground observation data for remote sensing authenticity inspection is mostly based on sample point arrangement in a field comprehensive verification site and is obtained through manual or automatic observation. The remote sensing field comprehensive verification site comprises a comprehensive verification site and a comprehensive test field. The field comprehensive test field has more instruments and measuring personnel, and can acquire satellite synchronous observation data in a larger space range. Due to the large investment, the measurement of the field comprehensive test field is mostly concentrated in a certain time period, and the test data of a long-time sequence cannot be acquired. In order to acquire ground observation data of a long time scale and ensure the space-time consistency and diversity of satellite product inspection, a long-term observation technical means needs to be introduced. The comprehensive checking site for the authenticity is characterized in that a certain number of typical regions are selected in a national range, corresponding sites are built, ground data are observed and collected for a long time, and authenticity checking evaluation of satellite products is achieved. By jointly arranging the authenticity check stations, the verification of multiple scales and multiple time phases can be carried out on the remote sensing product, and basic data are provided for inversion and authenticity check verification and optimization of the remote sensing product.
At present, the field site of the authenticity inspection is usually selected by artificially determining an area and then carrying out feasibility evaluation, and certain artificial subjectivity and space-time limitation exist. The field address needs to be selected objectively and scientifically, so that the problems of unreasonable space-time layout, low pertinence and poor layout precision are solved, and the reliability of authenticity inspection is improved.
In conclusion, the selection of the remote sensing authenticity check site is limited by the existing evaluation method, and certain subjectivity and limitation exist. Therefore, there is a need for a field address selection technique to solve the above problems in the prior art.
Disclosure of Invention
Because the existing methods have the above problems, embodiments of the present invention provide a method, an apparatus, an electronic device, and a storage medium for field address selection.
In a first aspect, an embodiment of the present invention provides a method for selecting a field address, including:
acquiring an application target of a field address to be selected;
establishing an N-layer evaluation system according to the application target of the field address to be selected, and carrying out the following steps:
acquiring a first level target and a first evaluation index set of a first layer of the N-layer evaluation system; the first evaluation index set comprises a first index data set corresponding to the first evaluation index set and the weight of each evaluation index in the first evaluation index set; first evaluation indexes in the first evaluation index set correspond to first index data in the first index data set one by one; n is a positive integer greater than 1;
determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set;
determining a first candidate site according to the first score;
acquiring an Nth level target and an Nth evaluation index set of an Nth layer of the N-layer evaluation system;
repeating the above steps to determine the N candidate field address from the N-1 candidate field addresses;
and determining the field address to be selected according to the N candidate field address.
Further, the determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index set includes:
and performing weighted calculation on the first index data corresponding to each first evaluation index according to the weight of each first evaluation index in the first evaluation index set to obtain the first score.
Further, before the establishing of the N-layer evaluation system according to the application target of the field address to be selected, the method further includes:
establishing an evaluation index library according to the application target of the site to be selected; wherein the evaluation index library comprises a plurality of impact types; each influence type comprises a plurality of evaluation indexes;
acquiring an influence factor of each evaluation index in the plurality of evaluation indexes;
and determining the weight of each evaluation index according to the influence factor of each evaluation index.
Further, the evaluation index library includes 3 influence types, which are respectively: surface features, atmospheric features, and a human context.
Further, before the determining the first score of the first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set, the method further includes:
preprocessing the first index data set to normalize the first index data set.
Further, an Nth candidate field address is determined from the Nth-1 candidate field addresses through a scale grid.
In a second aspect, an embodiment of the present invention provides an apparatus for selecting a field address, including:
the acquisition module is used for acquiring an application target of a field address to be selected;
the processing module is used for establishing an N-layer evaluation system according to the application target of the field address to be selected and carrying out the following steps: acquiring a first level target and a first evaluation index set of a first layer of the N-layer evaluation system; the first evaluation index set comprises a first index data set corresponding to the first evaluation index set and the weight of each evaluation index in the first evaluation index set; first evaluation indexes in the first evaluation index set correspond to first index data in the first index data set one by one; n is a positive integer greater than 1; determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set; determining a first candidate site according to the first score; acquiring an Nth level target and an Nth evaluation index set of an Nth layer of the N-layer evaluation system; repeating the above steps to determine the N candidate field address from the N-1 candidate field addresses; and determining the field address to be selected according to the N candidate field address.
Further, the processing module is specifically configured to:
and performing weighted calculation on the first index data corresponding to each first evaluation index according to the weight of each first evaluation index in the first evaluation index set to obtain the first score.
Further, before the N-layer evaluation system is established according to the application target of the field address to be selected, the processing module is further configured to:
establishing an evaluation index library according to the application target of the site to be selected; wherein the evaluation index library comprises a plurality of impact types; each influence type comprises a plurality of evaluation indexes;
acquiring an influence factor of each evaluation index in the plurality of evaluation indexes;
and determining the weight of each evaluation index according to the influence factor of each evaluation index.
Further, the processing module is specifically configured to: the evaluation index library comprises 3 influence types which are respectively as follows: surface features, atmospheric features, and a human context.
Further, before the determining a first score for a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set, the processing module is further configured to:
preprocessing the first index data set to normalize the first index data set.
Further, the processing module is specifically configured to: and determining an N candidate field address from the N-1 candidate field addresses through a scale grid.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the method for selecting a field address according to the first aspect.
In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the method for field address selection according to the first aspect.
According to the technical scheme, the method, the device, the electronic equipment and the storage medium for selecting the field address provided by the embodiment of the invention have the advantages that the field address is selected objectively and scientifically, so that the problems of unreasonable space-time layout, weak pertinence, poor layout precision and overhigh layout cost are solved, and the reliability of authenticity check is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a system framework of a method for selecting a field address according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a method for selecting a field address according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a method for selecting a field address according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a method for selecting a field address according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a method for selecting a field address according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating a method for selecting a field address according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a method for selecting a field address according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating a method for selecting a field address according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an apparatus for selecting a field address according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The field address selection method provided in the embodiment of the present invention may be applied to a system architecture as shown in fig. 1, where the system architecture includes a field address selector 100, an N-layer evaluation system 200, and an evaluation index library 300.
Specifically, the address selector 100 is configured to determine an nth candidate address from the nth-1 candidate addresses according to the nth score.
Specifically, the N-th layer evaluation system 200 is configured to determine an nth score of an nth layer according to the nth index data set and a weight of each nth evaluation index in the nth evaluation index set.
The evaluation index library 300 in the embodiment of the present invention is used to provide an evaluation index set to the N-tier evaluation system 200.
It should be noted that fig. 1 is only an example of a system architecture according to the embodiment of the present invention, and the present invention is not limited to this specifically.
Based on the above-mentioned schematic system architecture, fig. 2 is a schematic flow chart corresponding to a method for selecting a field address according to an embodiment of the present invention, as shown in fig. 2, the method includes:
The first evaluation index set includes a first index data set corresponding to the first evaluation index set and a weight of each evaluation index in the first evaluation index set; the first evaluation indexes in the first evaluation index set correspond to the first index data in the first index data set one by one; n is a positive integer greater than 1.
And step 204, determining a first score of the first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index set.
In step 205, a first candidate site is determined according to the first score.
And step 206, acquiring an Nth-level target and an Nth evaluation index set of an Nth layer of the N-level evaluation system.
and 208, determining the field address to be selected according to the nth candidate field address.
Before step 202, the embodiment of the present invention has a step flow as shown in fig. 3, which is as follows:
Note that, the evaluation index library includes a plurality of influence types, and each influence type includes a plurality of evaluation indexes.
And step 303, determining the weight of each evaluation index according to the influence factor of each evaluation index.
In step 301, in a possible implementation manner, the evaluation index library includes 3 influence types, which are respectively: surface features, atmospheric features, and a human context.
It should be noted that, in the embodiment of the present invention, the factors potentially affecting the site selection are divided into surface features, atmospheric features, and a human environment, and in addition, the factors potentially affecting the site selection may also be vegetation features, and the like, which is not specifically limited in the embodiment of the present invention.
Further, possible evaluation indexes in each influence type are refined, and a rich dynamic selectable evaluation index library is established.
As shown in fig. 4, the surface characteristics include surface stability, surface uniformity, surface flatness, surface directionality, surface spectral characteristics, surface coverage, site type, site area, etc.; the atmospheric characteristics comprise the water vapor content of the field atmosphere, the cleanness of the field atmosphere, the annual average total cloud cover of the field, the spectral distribution of aerosol particles, the visibility of the field atmosphere, the optical thickness of the aerosol, the type of the field climate, the outline of the field atmosphere, the wind condition of the field, the days of fine days of the field and the like. The human environment comprises traffic accessibility, station population density, station personnel number, food and lodging guarantees, vehicle guarantees, network conditions, power conditions, running time and the like.
Specifically, in the embodiment of the present invention, the influence type is divided into the following evaluation dimensions from the viewpoint of the surface feature:
1. in order to expand the application range of sites, the site area comprises the main terrain categories such as plains, hills and the like, which are optimal, and mountain areas and high-altitude areas with large topographic height difference fluctuation are avoided.
2. The types of the ground features in the station are relatively single and homogeneous, the types of the ground features near the site network are rich and diverse, and various parameters of various ground features can be observed.
3. Because there is a certain time interval between the imaging time of the satellite image and the measurement of the ground data, the wave spectrum of the ground object in the field is required to have small change with time and stability.
4. The area of the site should not be too small, and the set requirements of the universal test and the sample prescription should be met.
In summary, surface characteristics such as surface uniformity, surface flatness, surface stability, surface directionality, surface spectral characteristics, surface coverage, site type, and site area need to be considered.
Further, in the embodiment of the present invention, the influence type is the atmospheric characteristic, and the evaluation dimensions are divided into the following evaluation dimensions:
1. in order to improve the effective covering times of the shooting of the transit site of the satellite, excellent illumination conditions and good atmospheric visibility are required, and an area with low cloud cover and low water vapor content throughout the year is selected.
2. Wind factors affect the observation quality of the equipment and the state of the ground objects, so that the wind speed and the wind direction also need to meet the requirements of the development of the test.
In summary, the atmospheric characteristics such as the field climate type, the field wind power condition, the field atmosphere cleanliness, the aerosol optical thickness, the aerosol particle spectrum distribution, the field atmosphere profile, the field atmosphere moisture content, the field days on sunny days, the field atmosphere visibility, and the field average total cloud cover in year need to be considered.
Further, in the embodiment of the present invention, the evaluation dimension is divided into the following evaluation dimensions from the perspective that the influence type is a human environment:
1. the site should select the area which is convenient for traffic and is close to the city, so that the transportation of materials and the test are convenient.
2. The land features of areas with dense population distribution and high development speed are changed greatly, which is not favorable for long-term use and maintenance of sites, while the areas with rare population and late development, which is unfavorable for hiring manpower because of lagged infrastructure construction, therefore, the areas with moderate population density are selected.
3. The method is far away from areas with high natural disasters such as earthquakes, floods, debris flows and the like and areas of closed natural protection areas.
In summary, human and literature environments such as traffic accessibility, station population density, station personnel number, food and lodging security, vehicle security, network conditions, power conditions, running time and the like need to be considered.
In step 203, a first level target and a first evaluation index set of a first layer of an N-layer evaluation system are obtained.
In a possible implementation mode, an evaluation system is divided into 4 layers according to an observation target and an application target of a field to be selected, a hierarchical target and an evaluation index set of each layer are determined, and an evaluation index system is customized.
For example, the primary target determines the site type and the station building target, and selects the evaluation indexes such as national and provincial administrative boundaries, climate types and ecological types for evaluation.
Specifically, the secondary target is the primary selection of the station building area, so the second layer index is the conditions which are not suitable for building stations, such as seismic zones, natural reserve zones, airport vicinities, military reserve zones and the like.
Further, the third-level target is depth optimization, and reasonable evaluation indexes are selected in an index system according to local conditions, for example, natural disasters such as high flood of nepal are needed, and an evaluation index system needs to be established according to local conditions and in consideration of local characteristics. For another example, for a plateau mountain terrain area, the degree of terrain relief and elevation are important influencing factors.
Further, in step 203, the first evaluation index set includes a first index data set corresponding to the first evaluation index set.
It should be noted that the acquisition of the index data set may be collected by using a satellite image, a remote sensing product, a statistical yearbook, a related website, or a shared service platform.
Specifically, the Global geographic information basic data can be acquired from data service websites such as a USCG (universal serial communications), a Global change scientific research data publishing system website, a resource environmental science and data center website, an open map (OSM), a Global Administrative area Database (GADM), a Geoservice and the like, and the Global surface parameters can be acquired from products such as satellite remote sensing images, medium resolution imaging spectrometers MODIS and the like and research results of Global research teams.
For example, a global Elevation Model (DEM) product has a spatial resolution of 3 arcseconds and an absolute height error of about 1 meter.
For example, the global land cover product has a spatial resolution of 30 meters and contains 30 types of ground objects such as grassland, forest land, impervious layers and water bodies.
For another example, the global administrative division data includes administrative countries, provinces, cities (districts), and counties on a global scale.
Furthermore, the global road network data covers road networks, tunnels and bridges at all levels of the world, and comprises geographical position information, administrative divisions to which the global road network data belongs and the highest speed limit.
For another example, the earthquake disaster data includes parameter data such as geographical position, seismic source depth, strike, dip, slip angle, seismic magnitude, and seismic time of 4-level or above earthquake.
As another example, climate zone data such as equatorial band, tropical, subtropical band, warm band, temperate band, cold temperate band, and plateau climate zones.
For another example, the data of the ecological function protection area includes various ecological function protection areas such as a water culvert health-preserving ecological function protection area, a water and soil conservation ecological function protection area, a wind-proof sand-fixing ecological function protection area, a species resource ecological function protection area, a marine ecological function protection area and the like.
For example, aerosol thickness data such as the MODIS global aerosol optical thickness product inversion algorithm fuses the dark pixel method and the deep blue algorithm with a spatial resolution of 3 kilometers.
Further, after the index data set is acquired, the index data set is preprocessed to normalize the index data set.
In the embodiment of the invention, index data sets are subjected to a series of batch preprocessing to obtain unified-reference normalized raster data.
Specifically, in one possible implementation, the batch processing analysis is performed on the index dataset based on a Python calling ArcGIS toolkit.
For example, the flatness of the field is characterized by calculating the gradient of the grid through DEM data. Because the basic data is in a global large range, when the data is screened, Python is used for compiling codes to screen out images in an area range according to file names, an ArcGIS toolkit is called to splice the screened images, the gradient is calculated, resampling is carried out, and finally normalization is carried out.
For example, a global 30m land cover product is used for representing the land surface cover and the ecological type of a field and determining the type of the field. Specifically, an image in an area range is screened out according to a file name by using Python, and an ArcGIS toolkit is called to splice and resample the screened image.
For example, the global road network data represents the degree of traffic convenience, establishes a proper buffer area of a primary road network or calculates the euclidean distance, and needs to be subjected to vector transformation into a grid, resampling, final normalization and the like.
And for example, the seismic point data is used for excluding earthquake high-incidence areas, and establishing a buffer zone with a proper distance, vector conversion into a grid, resampling and the like, so that an area with more stable crustal plate motion is screened out, and the method is more suitable for building stations.
In addition to the above, the spatial overlay analysis, the geostatistical analysis, the data management tool, etc. may also be used to process the index data set, and the present invention is not limited thereto.
After the index data set is preprocessed, in step 303, the embodiment of the present invention determines the weight of each evaluation index according to the influence factor of each evaluation index.
Furthermore, in the embodiment of the invention, the evaluation index set is divided into four categories according to the weight category, the weight of the evaluation index is determined by adopting an analytic hierarchy process or a gray level correlation method, and the weight of the evaluation index is dynamically adjusted according to different application targets and regional characteristics of the field to be selected.
Specifically, the factors influencing the site are divided into three main influence types, a plurality of influence factors are refined according to different types, the hierarchical structure is determined according to the domination relationship among the influence factors, then the importance of the influence factors of each bottom layer relative to each high layer is integrally compared, and finally the calculated weight of each influence factor is correspondingly compared.
According to the scheme, the complex problems are simplified and layered, so that the field address can be efficiently and accurately selected, and the reliability of authenticity inspection is improved.
Further, in the embodiment of the present invention, the index data set is divided into, after being preprocessed, according to the attribute characteristics: target attribute, spatial and temporal attribute range values, and binary of yes or no.
As shown in fig. 5, the target attributes include a climate type, a surface coverage, an administrative district, and the like. The spatial attributes include surface uniformity, surface flatness, surface stability, power conditions, surface spectral characteristics, site area, aerosol optical thickness, aerosol particle spectral distribution, atmospheric moisture content, site atmospheric profile, traffic accessibility, atmospheric cleanliness, annual average total cloud cover, population density, and the like. The time attribute includes the number of days of sunny ground, the running time, and the like. The binary property comprises site wind power conditions, natural disaster high-incidence areas, natural protection areas and the like.
Specifically, taking the selection of an agricultural station in nipal as an example, the designated target values are nipal and forest land; the spatial attributes are traffic accessibility, altitude, ground surface uniformity, average aerosol thickness, air cleanness and the like; the time attribute is 'average sunny days in the year' and the like; the two values of yes or no are 'high natural disaster area' and 'natural protection area', etc.
Further, a weight is assigned to the characteristic of each type of index data set.
For example, regarding the cleanliness of the atmosphere, the air pollution level is 0-50 as excellent, 51-100 as good, 101-200 as light, 201-300 as medium heavy and more than 300 as heavy, the higher the pollution level, the lower the weight.
For example, a binary value of 0 or 1 is set for a high-incidence natural disaster area.
In step 204, the first index data corresponding to each first evaluation index is weighted and calculated according to the weight of each first evaluation index in the first evaluation index set, so as to obtain a first score.
In the embodiment of the invention, the N candidate field address is determined from the N-1 candidate field addresses through the scale grid.
Specifically, in each level of scale grid, the index data set of each level and the corresponding weight are subjected to spatial analysis, weight evaluation and depth optimization, primary selection, screening, candidate selection and evaluation are performed step by step, and finally an expert layer is introduced to determine the final reasonable site selection position.
For example, the primary grid is a 100 × 100 km grid, and a large range of countries, climate types, ecological types and the like are initially selected; the secondary grid is a 10 x 10 km grid, and an area where the station can be built under objective conditions is screened out; and the three-level grid is a 1 x 1 km grid, scoring is carried out according to the index data set and the weight, and a high-scoring area is candidate. And finally, introducing a fourth-level expert layer, and evaluating the screened candidate area according to a knowledge base of the expert so as to determine a reasonable address selection position.
Further, fig. 6 exemplarily shows a screening result of the primary mesh, in which a pure black region is a screened region satisfying a high score; FIG. 7 shows the results of the secondary mesh screening; FIG. 8 shows the results of the screening of the three-level mesh. It can be seen that the scale grids are arranged from large to small, and the range of the screened area is smaller and smaller.
Based on the same inventive concept, fig. 9 exemplarily shows a field address selection apparatus provided by an embodiment of the present invention, which can be a flow of a field address selection method.
The apparatus, comprising:
an obtaining module 901, configured to obtain an application target of a field to be selected;
the processing module 902 is configured to establish an N-layer evaluation system according to the application target of the field to be selected and perform the following steps: acquiring a first level target and a first evaluation index set of a first layer of the N-layer evaluation system; the first evaluation index set comprises a first index data set corresponding to the first evaluation index set and the weight of each evaluation index in the first evaluation index set; first evaluation indexes in the first evaluation index set correspond to first index data in the first index data set one by one; n is a positive integer greater than 1; determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set; determining a first candidate site according to the first score; acquiring an Nth level target and an Nth evaluation index set of an Nth layer of the N-layer evaluation system; repeating the above steps to determine the N candidate field address from the N-1 candidate field addresses; and determining the field address to be selected according to the N candidate field address.
Further, the processing module 902 is specifically configured to:
and performing weighted calculation on the first index data corresponding to each first evaluation index according to the weight of each first evaluation index in the first evaluation index set to obtain the first score.
Further, before the establishing an N-layer evaluation system according to the application target of the field address to be selected, the processing module 902 is further configured to:
establishing an evaluation index library according to the application target of the site to be selected; wherein the evaluation index library comprises a plurality of impact types; each influence type comprises a plurality of evaluation indexes;
acquiring an influence factor of each evaluation index in the plurality of evaluation indexes;
and determining the weight of each evaluation index according to the influence factor of each evaluation index.
Further, the processing module 902 is specifically configured to: the evaluation index library comprises 3 influence types which are respectively as follows: surface features, atmospheric features, and a human context.
Further, before said determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set, the processing module 902 is further configured to:
preprocessing the first index data set to normalize the first index data set.
Further, the processing module 902 is specifically configured to: and determining an N candidate field address from the N-1 candidate field addresses through a scale grid.
Since the field address selection apparatus provided by this embodiment can be used to perform the field address selection method provided by the above embodiments, the operation principle and the beneficial effects thereof are similar, and will not be described in detail here.
Based on the same inventive concept, another embodiment of the present invention provides an electronic device, which specifically includes the following components, with reference to fig. 10: a processor 1001, a memory 1002, a communication interface 1003, and a communication bus 1004;
the processor 1001, the memory 1002 and the communication interface 1003 complete mutual communication through the communication bus 1004; the communication interface 1003 is used for realizing information transmission among the devices;
the processor 1001 is configured to call a computer program in the memory 1002, and the processor implements all the steps of the above method for selecting a field address when executing the computer program, for example, the processor implements the following steps when executing the computer program: acquiring an application target of a field address to be selected; establishing an N-layer evaluation system according to the application target of the field address to be selected, and carrying out the following steps: acquiring a first level target and a first evaluation index set of a first layer of the N-layer evaluation system; the first evaluation index set comprises a first index data set corresponding to the first evaluation index set and the weight of each evaluation index in the first evaluation index set; first evaluation indexes in the first evaluation index set correspond to first index data in the first index data set one by one; n is a positive integer greater than 1; determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set; determining a first candidate site according to the first score; acquiring an Nth level target and an Nth evaluation index set of an Nth layer of the N-layer evaluation system; repeating the above steps to determine the N candidate field address from the N-1 candidate field addresses; and determining the field address to be selected according to the N candidate field address.
Based on the same inventive concept, yet another embodiment of the present invention provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor implements all the steps of the above-mentioned method for field address selection, e.g., the processor implements the following steps when executing the computer program: acquiring an application target of a field address to be selected; establishing an N-layer evaluation system according to the application target of the field address to be selected, and carrying out the following steps: acquiring a first level target and a first evaluation index set of a first layer of the N-layer evaluation system; the first evaluation index set comprises a first index data set corresponding to the first evaluation index set and the weight of each evaluation index in the first evaluation index set; first evaluation indexes in the first evaluation index set correspond to first index data in the first index data set one by one; n is a positive integer greater than 1; determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set; determining a first candidate site according to the first score; acquiring an Nth level target and an Nth evaluation index set of an Nth layer of the N-layer evaluation system; repeating the above steps to determine the N candidate field address from the N-1 candidate field addresses; and determining the field address to be selected according to the N candidate field address.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a user life pattern prediction apparatus, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a user life pattern prediction apparatus, or a network device, etc.) to execute the user life pattern prediction method according to the embodiments or some parts of the embodiments.
In addition, in the present invention, terms such as "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Moreover, in the present invention, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Furthermore, in the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of site selection, comprising:
acquiring an application target of a field address to be selected;
establishing an N-layer evaluation system according to the application target of the field address to be selected, and carrying out the following steps:
acquiring a first level target and a first evaluation index set of a first layer of the N-layer evaluation system; the first evaluation index set comprises a first index data set corresponding to the first evaluation index set and the weight of each evaluation index in the first evaluation index set; first evaluation indexes in the first evaluation index set correspond to first index data in the first index data set one by one; n is a positive integer greater than 1;
determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set;
determining a first candidate site according to the first score;
acquiring an Nth level target and an Nth evaluation index set of an Nth layer of the N-layer evaluation system;
repeating the above steps to determine the N candidate field address from the N-1 candidate field addresses;
and determining the field address to be selected according to the N candidate field address.
2. The method of site selection according to claim 1, wherein said determining a first score for a first layer of the N-layer evaluation system based on the first index data set and a weight of each first evaluation index in the first evaluation index set comprises:
and performing weighted calculation on the first index data corresponding to each first evaluation index according to the weight of each first evaluation index in the first evaluation index set to obtain the first score.
3. The method for selecting a field address according to claim 1, wherein before the establishing an N-layer evaluation system according to the application target of the field address to be selected, the method further comprises:
establishing an evaluation index library according to the application target of the site to be selected; wherein the evaluation index library comprises a plurality of impact types; each influence type comprises a plurality of evaluation indexes;
acquiring an influence factor of each evaluation index in the plurality of evaluation indexes;
and determining the weight of each evaluation index according to the influence factor of each evaluation index.
4. A method for site selection according to claim 3, wherein the evaluation index library comprises 3 impact types, respectively: surface features, atmospheric features, and a human context.
5. The method of site selection according to claim 1, further comprising, prior to said determining a first score for a first layer of the N-layer evaluation system based on the first index data set and the weight of each first evaluation index in the first evaluation index set:
preprocessing the first index data set to normalize the first index data set.
6. The method according to any of claims 1 to 5, wherein the nth candidate field address is determined from the nth-1 candidate field addresses by a scaling grid.
7. An apparatus for site selection, comprising:
the acquisition module is used for acquiring an application target of a field address to be selected;
the processing module is used for establishing an N-layer evaluation system according to the application target of the field address to be selected and carrying out the following steps: acquiring a first level target and a first evaluation index set of a first layer of the N-layer evaluation system; the first evaluation index set comprises a first index data set corresponding to the first evaluation index set and the weight of each evaluation index in the first evaluation index set; first evaluation indexes in the first evaluation index set correspond to first index data in the first index data set one by one; n is a positive integer greater than 1; determining a first score of a first layer of the N-layer evaluation system according to the first index data set and the weight of each first evaluation index in the first evaluation index data set; determining a first candidate site according to the first score; acquiring an Nth level target and an Nth evaluation index set of an Nth layer of the N-layer evaluation system; repeating the above steps to determine the N candidate field address from the N-1 candidate field addresses; and determining the field address to be selected according to the N candidate field address.
8. The apparatus according to claim 7, wherein before the establishing of the N-layer evaluation system according to the application target of the field to be selected, the processing module is further configured to:
establishing an evaluation index library according to the application target of the site to be selected; wherein the evaluation index library comprises a plurality of impact types; each influence type comprises a plurality of evaluation indexes;
acquiring an influence factor of each evaluation index in the plurality of evaluation indexes;
and determining the weight of each evaluation index according to the influence factor of each evaluation index.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 6 are implemented when the processor executes the program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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