Industry business and remote sensing technology correlation degree quantitative evaluation and grading method and device

Abstract

The application discloses an industry business and remote sensing technology correlation degree quantitative evaluation and grading method and device, belongs to the field of remote sensing technology application and remote sensing information management, and comprises the following steps: carrying out industry business type analysis and collection, refining keywords corresponding to different business types; carrying out remote sensing technology analysis and collection, refining keywords corresponding to different remote sensing technologies; carrying out literature retrieval in a paper database with the aid of the keywords, classifying each paper, and counting the number of papers and the time span of paper publication; calculating the integration degree of remote sensing technology in the industry business; calculating the support degree of remote sensing technology to the industry business; taking the industry business and the remote sensing technology as nodes, constructing a relationship network and calculating the node weighted degree centrality of the network; calculating the normalized correlation degree of the industry business and the remote sensing technology; and grading based on the normalized correlation degree. The application can quantitatively analyze the use degree of remote sensing technology in the industry business application.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of remote sensing technology application and remote sensing information management, and particularly relates to an industry business and remote sensing technology correlation degree quantitative evaluation and grading method and device. BACKGROUND

[0002] With the synchronous upgrading of aerospace and information infrastructure, "space information" is generally regarded as the next round of technology wave after the Internet and mobile Internet. The number of remote sensing satellites has increased rapidly, and the spatial resolution, spectral resolution and time resolution have been improved simultaneously, which has delivered observation data for meteorology, ocean, land, agriculture and emergency industries, and has become the "heart" of the space industry chain.

[0003] After a large amount of remote sensing data, the repetition and redundancy of knowledge are rapidly amplified, resulting in "data abundance and information poverty". Although the existing technical system classifies remote sensing instruments, satellite platforms and processing algorithms, it lacks a main line of "starting from industry demand", which leads to the inability to normalize and standardize the answer to "which type of remote sensing capability can accurately solve which type of business problem". The evaluation index still stays at the macro level of "satellite number, resolution and market size", which cannot provide second-level, quantitative technical-business matching basis for decision makers or emergency responders. Different agencies are in charge of their own affairs, and the interfaces, metadata and processing procedures are incompatible, forming a number of information islands, so that remote sensing capabilities and industry scenes are always separated by an invisible wall, which ultimately reduces the overall efficiency of space information services. SUMMARY

[0004] The present application is aimed at the information "island" of the mutual isolation between industry business and remote sensing technology, and proposes an industry business and remote sensing technology correlation degree quantitative evaluation and grading method and device, which quantitatively evaluates the correlation degree between industry business and remote sensing technology, and grades according to the correlation degree.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme:

[0006] An industry business and remote sensing technology correlation degree quantitative evaluation and grading method comprises the following steps:

[0007] Step 1: Carry out industry business type analysis and summary, and extract keywords corresponding to different business types;

[0008] Step 2: Carry out remote sensing technology type analysis and summary, and extract keywords corresponding to different remote sensing technologies;

[0009] Step 3: Literature retrieval is carried out in the paper database by means of keywords, and each literature is classified, and the number of literatures and the time span of literature publication are counted;

[0010] Step 4: calculate the integration degree of remote sensing technology in the industry business;

[0011] Step 5: calculate the support degree of remote sensing technology to the industry business;

[0012] Step 6: taking the industry business and remote sensing technology as nodes, constructing a relationship network and calculating the node normalized weighted degree centrality index of the network;

[0013] Step 7: calculate the normalized correlation degree between the industry business and remote sensing technology;

[0014] Step 8: grading based on the normalized correlation degree.

[0015] An industry business and remote sensing technology correlation degree quantitative evaluation and grading device comprises:

[0016] An industry business and remote sensing technology correlation degree quantitative evaluation and grading device comprises:

[0017] An industry business and remote sensing technology correlation degree quantitative evaluation and grading device comprises:

[0018] An industry business and remote sensing technology correlation degree quantitative evaluation and grading device comprises:

[0019] An industry business and remote sensing technology correlation degree quantitative evaluation and grading device comprises:

[0020] An industry business and remote sensing technology correlation degree quantitative evaluation and grading device comprises:

[0021] An industry business and remote sensing technology correlation degree quantitative evaluation and grading device comprises:

[0022] An industry business and remote sensing technology correlation degree quantitative evaluation and grading device comprises:

[0023] An industry business and remote sensing technology correlation degree quantitative evaluation and grading device comprises:

[0024] An electronic device comprises one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0025] A computer readable storage medium has executable instructions stored thereon, which are executed by a processor to implement the method.

[0026] The present application has the following beneficial effects:

[0027] The application characterizes the coverage thickness and intervention depth of remote sensing means in various businesses by measurable means. Firstly, the scattered industry tasks and remote sensing capabilities are abstracted into business pedigree and capability pedigree respectively. Then, relying on literature review, remote sensing result landing cases are thoroughly investigated. The diversity of remote sensing means adopted by the business is measured by "integration degree", and the embedding depth of these means in the business chain is measured by "supporting degree". Finally, the two are condensed into a comprehensive score by "correlation degree". Then, with the aid of natural breakpoint algorithm, the business scene is automatically divided into several levels in terms of remote sensing application degree. Through this process, a panoramic mapping between industry business and remote sensing technology can be clearly drawn, and numbers can be used to speak, laying an operational and replicable technical foundation for remote sensing capability optimization, service efficiency improvement and intelligent pushing.

[0028] The industry business and remote sensing technology correlation degree quantitative evaluation and grading method provided by the application can sort out the industry business system and the remote sensing technology system, construct the mapping relationship between the two, quantitatively evaluate the depth and breadth of remote sensing technology in industry business application, improve the service efficiency of remote sensing technology, and promote the intelligent recommendation of remote sensing technology in business application. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The industry business and remote sensing technology correlation degree quantitative evaluation and grading method flow chart of the application;

[0030] Figure 2 The industry business and technology keyword bibliometrics and classification statistics flow chart;

[0031] Figure 3 The industry business and remote sensing technology relationship network diagram. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0033] The application provides an industry business and remote sensing technology correlation degree quantitative evaluation and grading method, which quantitatively evaluates the correlation degree of industry business and remote sensing technology, and quantitatively grades the remote sensing technology application of industry business. Taking natural resource business as an example, referring to Figure 1 , the specific steps include:

[0034] Step 1: Carry out industry business sorting, summarize and arrange different business types, and extract keywords corresponding to different remote sensing technologies.

[0035] In this embodiment, based on the macroscopic planning and layout status of each industry field, the business types are summarized and sorted, and the keywords corresponding to different business types are extracted as the bridge for linking with the technology system.

[0036] In this embodiment, the remote sensing application business in the field of natural resources is mainly divided into four business fields: land, ocean, surveying and mapping and geographic information, and geology / mineral resources. The land field is divided into 8 business directions, mainly including natural resource investigation and monitoring, natural resource asset management, natural resource right registration, natural resource ecological protection and restoration, land space big data system, land space planning, land space use control, and farmland protection supervision, etc., involving 25 types of business content and 48 business-related keywords. The ocean field includes 8 business directions, mainly including: marine resource investigation and monitoring, marine resource asset management, marine natural resource development and utilization, marine space use control, marine early warning monitoring, marine space ecological restoration, marine strategic planning and economy, and marine geographic information management, etc., involving 24 types of business content and 59 business keywords. The surveying and mapping field mainly includes 4 business directions of natural resource basic investigation, land surveying and mapping, geographic information management, and satellite remote sensing and other high-tech system construction, etc., involving 9 types of business content and 20 business keywords. The geological and mineral resources field mainly includes 3 business directions of geological survey plan, mining right management, and mineral resources protection supervision, involving 11 types of business content and 18 business keywords.

[0037] Step 2: Carry out remote sensing technology sorting and summarizing, and extract keywords corresponding to different remote sensing technologies.

[0038] In this embodiment, according to the differences of remote sensing research direction and application field, the remote sensing technology system is divided into 5 fields: remote sensing data preprocessing, thematic information extraction, remote sensing inversion and assimilation, remote sensing product authenticity verification, and remote sensing application. The classification of technologies in different fields and their corresponding keywords are further based on the classification and induction of remote sensing technologies by the Department of Geosciences of the Natural Science Foundation of China.

[0039] Remote sensing data preprocessing technology is not further classified, but 56 keywords are extracted according to remote sensing data preprocessing methods and remote sensing data-oriented.

[0040] Thematic information extraction technology is divided into three categories: remote sensing image classification technology, target recognition and information extraction technology, and change detection technology. Remote sensing image classification technology involves 49 keywords; target recognition and information extraction technology involves 38 keywords; remote sensing change detection technology involves 36 keywords.

[0041] Remote sensing retrieval and assimilation technology can be divided into six types according to the research object, including radiation and energy balance parameter remote sensing retrieval and assimilation, atmospheric parameter remote sensing retrieval and assimilation, vegetation parameter remote sensing retrieval and assimilation, soil parameter remote sensing retrieval and assimilation, water body parameter remote sensing retrieval and assimilation, and ice and frozen soil parameter remote sensing retrieval and assimilation. Remote sensing retrieval and assimilation technology of radiation and energy balance parameters involves 36 key words; remote sensing retrieval and assimilation technology of atmospheric parameters involves 58 key words. Remote sensing retrieval and assimilation technology of vegetation parameters involves 43 key words; remote sensing retrieval and assimilation technology of soil parameters involves 25 key words. Remote sensing retrieval and assimilation technology of water body parameters involves 53 key words; remote sensing retrieval and assimilation technology of ice and frozen soil parameters involves 30 key words.

[0042] Remote sensing product authenticity testing technology is not further classified and involves 28 key words.

[0043] Remote sensing application technology is divided into 14 types, including global change remote sensing application technology involving 31 key words; land and resource remote sensing application technology involving 24 key words; environmental remote sensing technology involving 62 key words; wetland remote sensing technology involving 50 key words; grassland and desert remote sensing technology involving 48 key words; hydrological remote sensing technology involving 39 key words; disaster remote sensing technology involving 30 key words; agricultural remote sensing technology involving 29 key words; forestry remote sensing technology involving 25 key words; urban remote sensing technology involving 35 key words; polar remote sensing technology involving 33 key words; marine remote sensing technology involving 27 key words; planetary remote sensing technology involving 32 key words; and social and economic remote sensing technology involving 31 key words.

[0044] Step 3: Literature retrieval in the paper database with the help of keywords, and classification of each literature, statistics of the number of literatures and the time span of literature publication, reference Figure 2 , the specific steps are as follows:

[0045] Step 3-1: Use different business type keywords and different technology type keywords as search terms, the logical relationship between keywords is “or” (“or”), and the search range is the title, abstract and keywords of the literature in the paper database.

[0046] In this example, the search range is the China National Knowledge Network database, and the search paradigm is each business keyword “or” (“or”) remote sensing technology keyword.

[0047] Step 3-2: Limit the start time and end time of literature retrieval.

[0048] In this example, the literature retrieval time range is from 2000 to 2023.

[0049] Step 3-3: Based on the first search results, carry out secondary search, set "remote sensing" as the necessary search keyword, and limit the literature to remote sensing application.

[0050] In this embodiment, based on the search results in step 3-2, set "remote sensing" as the necessary search keyword, and limit the literature to remote sensing application.

[0051] Step 3-4: Extract the original keywords in the literature that can describe the literature theme, and classify the literature according to the corresponding relationship between the industry business type and its keywords.

[0052] In this embodiment, the classification management of the literature adopts the keyword method, which is completed in MySQL. Different from the keywords in the classification system of remote sensing technology business application, the keyword method here refers to the keywords in the search results literature, title and abstract, and the theme words in the literature. In the study, the original keywords in the literature that can describe the theme of the literature are directly extracted, and the corresponding literature is classified according to the corresponding relationship between the technology / business type and its keywords in the classification system of remote sensing technology business application.

[0053] Step 3-5: Statistics of the number of literature of each type and its time span.

[0054] In this embodiment, the number of search literature in the field of natural resource business is 293421, among which the number of literature related to remote sensing is 105048. Among the remote sensing related literature, the number of papers on land business is 19129, the number of papers on marine business is 2322, the number of papers on surveying and mapping and geographic information business is 70601, and the number of papers on geological and mineral resources business is 12996.

[0055] Step 4: Calculate the integration degree of remote sensing technology in industry business ;

[0056] In this embodiment, the quantitative evaluation index of the integration degree of remote sensing technology in industry business application is The specific method and steps are as follows:

[0057] Step 4-1: First, calculate the remote sensing dependence :

[0058] ;

[0059] Among them, represents the remote sensing dependence, the value range is 0-1; represents the total number of literature of a certain business type; represents the number of remote sensing literature of a certain business type.

[0060] Step 4-2: Calculate the time span T of literature in a certain business direction:

[0061] ;

[0062] wherein, represents the last year of literature publication in a certain business direction within the research period, represents the first year of literature publication in a certain business direction within the research period;

[0063] Step 4-3: Normalize the total number of literature in different business directions , literature time span , number of types of remote sensing technologies applied , remote sensing dependence The normalization formula is as follows:

[0064] ;

[0065] wherein, f(x) is the normalization function, x represents the index that needs to be normalized, and the value range is 0-1;

[0066] Step 4-4: The number of remote sensing literature in different business types , literature time span , number of types of remote sensing technologies applied , remote sensing dependence The sum of the normalized values represents the integration degree , and the value range is 0-4. The specific formula is as follows:

[0067] ;

[0068] The average integration degree of remote sensing technology in natural resource business field is 2.00, among which the integration degree of remote sensing technology in natural resource investigation and monitoring business is 2.48, the integration degree of remote sensing technology in land space big data system business is 2.51, the integration degree of remote sensing technology in land space use control business is 1.71, the integration degree of remote sensing technology in land space planning business is 2.34, the integration degree of remote sensing technology in natural resource ecological protection and restoration business is 1.91, the integration degree of remote sensing technology in natural resource asset management business is 1.86, the integration degree of remote sensing technology in cultivated land protection supervision business is 1.88, and the integration degree of remote sensing technology in natural resource right registration business is 1.55.

[0069] The remote sensing technology integration degree of marine natural resource investigation and monitoring business is 2.72, the remote sensing technology integration degree of marine geographic information management business is 2.33, the remote sensing technology integration degree of marine early warning monitoring business is 2.21, the remote sensing technology integration degree of sea island management business is 1.99, the remote sensing technology integration degree of marine natural resource development and utilization business is 1.36, the remote sensing technology integration degree of marine space use control business is 0.02, the remote sensing technology integration degree of marine space ecological restoration business is 0.81, and the remote sensing technology integration degree of marine strategic planning and economic business is 1.80.

[0070] The remote sensing technology integration degree of mineral resource protection supervision business is 3.02, the remote sensing technology integration degree of geological survey plan business is 2.29, and the remote sensing technology integration degree of mining right management business is 1.18.

[0071] The remote sensing technology integration degree of surveying and mapping geographic information management business is 2.86, the remote sensing technology integration degree of satellite remote sensing and other high-tech system construction business is 3.85, the remote sensing technology integration degree of surveying and mapping natural resource investigation and monitoring business is 1.67, and the remote sensing technology integration degree of land surveying and mapping business is 2.64.

[0072] Step 5: Calculate the support degree of remote sensing technology to industry business;

[0073] In this embodiment, the support degree of remote sensing technology to industry business is The specific method and steps of constructing the quantitative evaluation index are as follows:

[0074] Step 5-1: Calculate the average annual number of literatures of a certain industry business direction :

[0075] ;

[0076] Wherein, represents the total number of literatures of a certain business direction, represents the last year of literature publication of a certain business direction in the research period, represents the initial year of literature publication of a certain business direction in the research period;

[0077] Step 5-2: Normalize the average annual number of literatures and the number of remote sensing technology supported business types The specific normalization formula is as follows:

[0078] ;

[0079] Wherein, f(x) is the normalization function, x represents the index that needs to be normalized, and the value range is 0-1;

[0080] Step 5-3: Annual average number of documents in a certain business direction And the number of business types supported by a certain remote sensing technology type The normalized sum of addition represents the support degree , the value range is 0-2. The specific formula of the index is as follows:

[0081] ;

[0082] The support degree of remote sensing data preprocessing technology is 1.91;

[0083] The support degree of remote sensing image classification technology is 1.83, the support degree of target recognition and information extraction technology is 1.63, and the support degree of change detection technology is 1.25;

[0084] The support degree of radiation and energy balance parameter remote sensing inversion and assimilation technology is 0.80, the support degree of atmospheric parameter remote sensing inversion and assimilation technology is 0.79, the support degree of vegetation parameter remote sensing inversion and assimilation technology is 1.22, the support degree of soil parameter remote sensing inversion and assimilation technology is 0.57, the support degree of water body parameter remote sensing inversion and assimilation technology is 0.68, and the support degree of ice and permafrost parameter remote sensing inversion and assimilation technology is 0.01;

[0085] The support degree of remote sensing product authenticity verification technology is 0.34;

[0086] The support degree of global change remote sensing technology is 0.96, the support degree of land and resource remote sensing technology is 1.64, the support degree of environmental remote sensing technology is 1.38, the support degree of wetland remote sensing technology is 0.88, the support degree of grassland and desert remote sensing technology is 1.02, the support degree of hydrology remote sensing technology is 1.34, the support degree of disaster remote sensing technology is 1.27, the support degree of agricultural remote sensing technology is 1.08, the support degree of forestry remote sensing technology is 0.69, the support degree of urban remote sensing technology is 0.81, the support degree of polar remote sensing technology is 0.23, the support degree of marine remote sensing technology is 0.80, the support degree of planetary remote sensing technology is 0.00, and the support degree of social and economic remote sensing technology is 1.50.

[0087] Step 6: Build the relationship network between industry business and remote sensing technology, and calculate the node weighted degree centrality of the network;

[0088] In this embodiment, industry business and remote sensing technology are taken as spatial nodes, and the number of documents is taken as weight. The relationship network between industry business and remote sensing technology is built in Gephi software Figure 3 , and the normalized weighted degree centrality index of the relationship network nodes is calculated . The specific algorithm is as follows:

[0089] ;

[0090] ;

[0091] wherein, represents the weight of the edge between a certain industry business or remote sensing technology (node k) and another industry business or remote sensing technology (node l), and n is the total number of nodes connected to node k, is the node weighted degree centrality index.

[0092] The normalized weighted degree centrality index of the industry business and remote sensing technology in the embodiment See the table below:

[0093]

[0094] Step 7: Calculate the correlation degree of industry business and remote sensing technology.

[0095] In the embodiment, the normalized correlation degree of the industry business The specific algorithm is:

[0096] ;

[0097] ;

[0098] wherein, is the correlation degree of the industry business.

[0099] The normalized correlation degree of the remote sensing technology The specific algorithm is:

[0100] ;

[0101] ;

[0102] wherein, is the correlation degree of the remote sensing technology.

[0103] The normalized correlation degree of the industry business and remote sensing technology is shown in the table below:

[0104]

[0105] Step 8: Classification based on normalized correlation degree.

[0106] In the embodiment, based on the normalized correlation degree, the Fisher-Jenks natural breakpoint method is used to classify the correlation degree of the industry business and remote sensing technology. The steps are:

[0107] Step 8-1: Set the correlation degrees of all industry businesses and remote sensing technologies to a numerical array A;

[0108] Array A is set as wherein, is a certain industry business or remote sensing technology, is the normalized correlation degree of the industry business or the normalized correlation degree corresponding to the remote sensing technology .

[0109] Step 8-2: Determine the number of correlation degree levels;

[0110] The number of correlation degree levels is determined as 5 levels, from high to low, level V (strong correlation degree), level IV (stronger correlation degree), level III (medium correlation degree), level II (weaker correlation degree), and level I (weak correlation degree).

[0111] Step 8-3: Start initial classification, calculate the average correlation degree of each level and the sample deviation sum of squares, and sum to get the total deviation sum of squares within the level, total deviation sum of squares The calculation formula is:

[0112] ;

[0113] wherein, is a certain element in array A, is the average value of all normalized correlation degrees in array A.

[0114] The Fisher-Jenks natural breakpoint program is written through python to calculate the total deviation sum of squares .

[0115] Step 8-4: Through dynamic programming algorithm, constantly adjust and optimize the segmentation point until the segmentation scheme that minimizes the total intra-class variance is found.

[0116] The correlation degree classification scheme of industry business and remote sensing technology is:

[0117] Level V (strong correlation degree): satellite remote sensing high-tech system construction, remote sensing data preprocessing technology, remote sensing image classification technology, land resource remote sensing technology, target recognition and information extraction technology;

[0118] Level IV (stronger correlation degree): mineral resource protection supervision, surveying and mapping geographic information management, land surveying and mapping, marine natural resource investigation and monitoring, change detection technology, vegetation parameter remote sensing inversion and assimilation technology, environmental remote sensing technology, disaster remote sensing technology, hydrological remote sensing technology, social economic remote sensing technology;

[0119] Level III (medium correlation degree): land space big data system, land natural resource investigation and monitoring, land space planning, geological survey plan, marine geographic information management, marine early warning monitoring, agricultural remote sensing technology, global change remote sensing technology;

[0120] Level II (weak correlation degree): sea island management, natural resource ecological protection and restoration, farmland protection supervision, natural resource asset management, marine strategic planning and economy, surveying and mapping natural resource investigation and monitoring, land space use control, natural resource right registration, marine natural resource development and utilization, mining right management, wetland remote sensing technology, urban remote sensing technology, marine remote sensing technology, radiation and energy balance parameter remote sensing inversion and assimilation technology, atmospheric parameter remote sensing inversion and assimilation technology, forestry remote sensing technology, water body parameter remote sensing inversion and assimilation technology, soil parameter remote sensing inversion and assimilation technology;

[0121] Level I (weak correlation degree): marine space ecological restoration, marine space use control, remote sensing product authenticity inspection technology, polar remote sensing technology, ice and permafrost parameter remote sensing inversion and assimilation technology, planetary remote sensing technology.

[0122] The application further provides an industry business and remote sensing technology correlation degree quantitative evaluation and grading device, comprising:

[0123] An industry business carding and summarizing module is used to card and summarize industry business types and extract keywords corresponding to different business types.

[0124] A remote sensing technology carding and summarizing module is used to card and summarize remote sensing technologies and extract keywords corresponding to different remote sensing technologies.

[0125] A retrieval module is used to retrieve literature in a paper database by means of the keywords, classify each piece of literature, and count the number of literatures and the time span of literature publication.

[0126] An integration degree calculation module is used to calculate the integration degree of remote sensing technologies in industry businesses.

[0127] A support degree calculation module is used to calculate the support degree of remote sensing technologies to industry businesses.

[0128] A node weighted degree centrality calculation module is used to take industry businesses and remote sensing technologies as nodes, construct a relationship network and calculate the node weighted degree centrality of the network.

[0129] A correlation degree calculation module is used to calculate the normalized correlation degree of industry businesses and remote sensing technologies.

[0130] A grading module is used to grade based on the normalized correlation degree.

[0131] The application further provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0132] The application further provides a computer readable storage medium, which stores executable instructions, and the instructions enable a processor to implement the method when executed by the processor.

[0133] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0134] The present application is described with reference to the flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus for implementing the functions specified in the flowcharts and / or block diagrams.

[0135] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus for implementing the functions specified in the flowcharts and / or block diagrams.

[0136] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus for implementing the functions specified in the flowcharts and / or block diagrams.

[0137] While the preferred embodiments of the application have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they have the benefit of the present disclosure without departing from the spirit and scope of the application. Accordingly, it is intended that the appended claims include all such modifications and variations as fall within the scope of the present application.

[0138] It is apparent that those skilled in the art can make various changes and modifications to the application without departing from the spirit and scope of the application. It is therefore intended that the present application cover all such changes and modifications that are within its scope.

Claims

1. An industry business and remote sensing technology correlation quantitative evaluation and grading method, characterized in that, The method comprises the following steps: Step 1: industry business type is sorted and summarized, and keywords corresponding to different business types are refined; Step 2: remote sensing technology type is sorted and summarized, and keywords corresponding to different remote sensing technologies are refined; Step 3: literature retrieval is performed in a paper database by means of the keywords, and each paper is classified, and the number of papers and the time span of paper publication are counted; Step 4: the integration degree of remote sensing technology in the industry business is calculated; Step 5: the support degree of remote sensing technology to the industry business is calculated; Step 6: industry business and remote sensing technology are taken as nodes, a relationship network is constructed, and a node normalized weighted degree centrality index of the network is calculated; Step 7: the normalized correlation degree of industry business and remote sensing technology is calculated; Step 8: classification is performed based on the normalized correlation degree; In step 4, the integration of remote sensing technology by industry services The specific calculation method is: Step 4-1: First, calculate the remote sensing dependency : ； wherein, represents the total number of documents of a certain business type; represents the number of remote sensing documents of a certain business type; Step 4-2: Calculate the time span of documents for a certain business direction : ； wherein, represents the last year of publication of papers in a certain business direction within the research period, represents the first year of publication of papers in a certain business direction within the research period; Step 4-3: Number of remote sensing documents for different service types , Time span of documents , Number of applied remote sensing technology types , Remote sensing dependency Normalization is performed, and the normalization formula is as follows: ； wherein f(x) is a normalization function, and x represents an index to be normalized; Step 4-4: The normalized sum of the four indicators represents the integration The indicators are specified as follows: ； In step 6, the node normalized weighted degree centrality metric The specific algorithm is: ； ； wherein, denotes the weight of the edge between a certain industry business or remote sensing technology, i.e. node k, and another industry business or remote sensing technology, i.e. node l, and n is the total number of nodes connected to node k, is the node weighted degree centrality index.

2. The method according to claim 1, wherein, Step 3 comprises the following steps: Step 3-1: the keywords of different business types and the keywords of different technology types are taken as retrieval words, the logical relationship between the keywords is OR, and the retrieval range is the title, abstract and keywords of the literature in the paper database; Step 3-2: the start time and the end time of literature retrieval are limited.

3. The method according to claim 1, wherein, In step 5, the remote sensing technology support degree index The specific calculation method is: Step 5-1: Calculate the average annual number of documents in a business direction of an industry : ； Step 5-2: Average annual number of documents and the number of remote sensing technology supporting business types The two indicators are normalized, and the normalization formula is as follows: ； wherein f(x) is a normalization function, and x represents an index to be normalized; Step 5-3: The normalized sum of the two indicators represents the support level The indicators are specified as follows: 。 4. The method according to claim 1, wherein, In step 7, the normalized correlation degree of industry business and remote sensing technology is calculated as follows: Normalized correlation of industry business The specific algorithm is: ； ； wherein, a relevance to an industry business; Normalized correlation of remote sensing technology The specific algorithm is: ； ； wherein is the degree of association of the remote sensing technique.

5. The method according to claim 1, wherein, In step 8, Fisher-Jenks natural breakpoint method is adopted to classify industry business and remote sensing technology, and the steps are as follows: Step 8-1: all the normalized correlation degrees of industry business and remote sensing technology are set as a numerical array A; Step 8-2: the number of correlation degree levels is determined; Step 8-3: Start initial grading, calculate the normalized correlation degree mean value and sample deviation square sum of each grade, and sum up to get the total deviation square sum in the grade, total deviation square sum The calculation formula is: ； wherein is an element of the array A, is the average of all elements of the array A; Step 8-4: the dynamic programming algorithm is used to continuously adjust and optimize the segmentation points until the segmentation scheme that minimizes the total intra-class variance is found.

6. An industry business and remote sensing technology correlation quantitative evaluation and grading device, which realizes the method of claim 1, characterized in that, It comprises: an industry business sorting and summarizing module, which sorts and summarizes industry business types, and refines keywords corresponding to different business types; a remote sensing technology sorting and summarizing module, which sorts and summarizes remote sensing technologies, and refines keywords corresponding to different remote sensing technologies; a retrieval module, which performs literature retrieval in a paper database by means of the keywords, classifies each paper, and counts the number of papers and the time span of paper publication; an integration degree calculation module, which calculates the integration degree of remote sensing technology in the industry business; a support degree calculation module, which calculates the support degree of remote sensing technology to the industry business; a node weighted degree centrality calculation module, which takes industry business and remote sensing technology as nodes, constructs a relationship network, and calculates the node weighted degree centrality of the network; a correlation degree calculation module, which calculates the normalized correlation degree of industry business and remote sensing technology; a classification module, which classifies based on the normalized correlation degree.

7. An electronic device, comprising: It comprises: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, An executable instruction is stored thereon, which makes the processor implement the method of any one of claims 1 to 5 when executed by the processor.

Images