Quantitative evaluation and grading method and device for association degree of industry business and remote sensing technology

Abstract

The invention discloses an industry business and remote sensing technology correlation degree quantitative evaluation and grading method and device, and belongs to the field of remote sensing technology application and remote sensing information management, and the method comprises the steps: carrying out the sorting and gathering of industry business types, and extracting keywords corresponding to different business types; remote sensing technology carding and gathering are carried out, and keywords corresponding to different remote sensing technologies are extracted; literature retrieval is carried out in a paper database by means of the keywords, each literature is classified, and the literature number and the literature publishing time span are counted; calculating the integration level of the remote sensing technology in the industry business; calculating the support degree of the remote sensing technology to the industry business; the industry business and the remote sensing technology are used as nodes, a relation network is constructed, and the node weighting centrality of the network is calculated; calculating the normalized correlation degree of the industry business and the remote sensing technology; and grading based on the normalized correlation degree. The method can quantitatively analyze the use degree of the remote sensing technology in industry business application.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing technology application and remote sensing information management, specifically relating to a method and apparatus for quantitative evaluation and classification of the correlation between industry operations and remote sensing technology. Background Technology

[0002] With the simultaneous upgrading of aerospace and information infrastructure, "space-air information" is widely regarded as the next wave of technological advancement following the internet and mobile internet. The number of remote sensing satellites has surged, with spatial, spectral, and temporal resolutions improving in tandem. These satellites continuously deliver observational data to industries such as meteorology, oceanography, land resources, agriculture, and emergency response, becoming the "heart" of the space-air industry chain.

[0003] The influx of massive amounts of remote sensing data has rapidly amplified duplication and redundancy at the knowledge level, resulting in "data abundance but information scarcity." While existing technological systems categorize remote sensing instruments, satellite platforms, and processing algorithms, they lack a guiding principle "starting from industry needs," making it impossible to provide a standardized and consistent answer to "which type of remote sensing capability can accurately solve which type of business problem." Evaluation indicators remain at a macro level, such as "number of satellites, resolution, and market size," failing to provide decision-makers or emergency responders with second-level, quantifiable technical-business matching criteria. Different institutions operate independently, with incompatible interfaces, metadata, and processing flows, creating information silos that keep remote sensing capabilities and industry scenarios separated by an invisible wall, ultimately lowering the overall efficiency of aerospace information services. Summary of the Invention

[0004] This invention addresses the information silos between industry operations and remote sensing technology by proposing a method and apparatus for quantitatively evaluating and classifying the correlation between industry operations and remote sensing technology. The method quantitatively assesses the degree of correlation between industry operations and remote sensing technology and classifies them according to the degree of correlation.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for quantitatively evaluating and classifying the correlation between industry operations and remote sensing technology includes the following steps:

[0007] Step 1: Conduct a review and summary of industry business types, and extract keywords corresponding to different business types;

[0008] Step 2: Conduct a sorting and summary of remote sensing technology types, and extract keywords corresponding to different remote sensing technologies;

[0009] Step 3: Use keywords to search for literature in the paper database, categorize each paper, and count the number of papers and the time span of their publication.

[0010] Step 4: Calculate the integration level of remote sensing technology in industry operations;

[0011] Step 5: Calculate the degree to which remote sensing technology supports industry operations;

[0012] Step 6: Using industry business and remote sensing technology as nodes, construct a relationship network and calculate the normalized weighted degree centrality index of the network's nodes;

[0013] Step 7: Calculate the normalized correlation between industry operations and remote sensing technology;

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

[0015] A device for quantitatively evaluating and classifying the correlation between industry operations and remote sensing technology, comprising:

[0016] The industry business analysis and summary module allows for the analysis and summary of industry business types, and the extraction of keywords corresponding to different business types.

[0017] The remote sensing technology sorting and summarizing module sorts and summarizes remote sensing technologies and extracts keywords corresponding to different remote sensing technologies.

[0018] The search module uses keywords to search for literature in the thesis database, categorizes each article, and counts the number of articles and the time span of their publication.

[0019] Integration degree calculation module, which calculates the integration degree of remote sensing technology in industry operations;

[0020] The support level calculation module calculates the degree to which remote sensing technology supports industry operations.

[0021] The node weighted degree centrality calculation module uses industry business and remote sensing technology as nodes to construct a relationship network and calculate the node weighted degree centrality of the network.

[0022] The correlation calculation module calculates the normalized correlation between industry business and remote sensing technology.

[0023] The hierarchical module classifies data based on normalized correlation degree.

[0024] An electronic device includes: one or more processors; and 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 cause the one or more processors to implement the method.

[0025] A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the method described thereon.

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

[0027] This invention uses measurable methods to characterize the coverage and depth of remote sensing technologies across various business operations. First, it abstracts dispersed industry tasks and remote sensing capabilities into business and capability systems, respectively. Then, based on literature review, it comprehensively gathers all cases of remote sensing results being implemented. Next, it uses "integration" to measure the diversity of remote sensing technologies adopted by the business, and "support" to measure the depth of these technologies' embedding in the business chain. Finally, "relevance" condenses these two into a comprehensive score. Then, using a natural breakpoint algorithm, it automatically classifies business scenarios into several levels based on the degree of remote sensing application. Through this process, it can clearly depict the panoramic mapping between industry operations and remote sensing technology, and provide data-driven insights, laying an operable and replicable technical foundation for optimal remote sensing capability selection, improved service efficiency, and intelligent push notifications.

[0028] The present invention provides a quantitative evaluation and classification method for the correlation between industry business and remote sensing technology, which 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 applications, improve the service efficiency of remote sensing technology, and promote the realization of intelligent recommendation of remote sensing technology in business applications. Attached Figure Description

[0029] Figure 1 This is a flowchart of a method for quantitative evaluation and classification of the correlation between industry business and remote sensing technology according to the present invention;

[0030] Figure 2 A flowchart for bibliometrics and classification statistics of industry business and technology keywords;

[0031] Figure 3 This is a network diagram showing the relationship between industry operations and remote sensing technology. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0033] This invention proposes a quantitative evaluation and classification method for the correlation between industry operations and remote sensing technology. It quantitatively evaluates the degree of correlation between industry operations and remote sensing technology, and quantitatively classifies the application of remote sensing technology in industry operations. Taking natural resource operations as an example, refer to... Figure 1 The specific steps include:

[0034] Step 1: Conduct industry business analysis, summarize and organize different business types, and extract keywords corresponding to different remote sensing technologies.

[0035] In this embodiment, based on the current status of macro-level coordination and planning in various industry sectors, business types are summarized and organized, and keywords corresponding to different business types are extracted as a bridge to link with the technology system.

[0036] In this embodiment, remote sensing applications in the natural resources field are mainly divided into four business areas: land, ocean, surveying and mapping and geographic information, and geology / mineral resources. The land area is further divided into eight business directions, including natural resource survey and monitoring, natural resource asset management, natural resource rights confirmation and registration, natural resource ecological protection and restoration, land spatial big data system, land spatial planning, land spatial use control, and farmland protection supervision, involving 25 business categories and 48 business-related keywords. The ocean area includes eight business directions, including marine resource survey and monitoring, marine resource asset management, marine natural resource development and utilization, marine spatial use control, marine early warning monitoring, marine spatial ecological restoration, marine strategic planning and economy, and marine geographic information management, involving 24 business categories and 59 business keywords. The surveying and mapping area mainly includes four business directions: basic natural resource survey, land surveying and mapping, geographic information management, and the construction of high-tech systems such as satellite remote sensing; involving nine business categories and 20 business keywords. The field of geology and mineral resources mainly includes three business areas: geological survey planning, mining rights management, and mineral resource protection and supervision, involving a total of 11 business contents and 18 business keywords.

[0037] Step 2: Conduct a review and summary of remote sensing technologies, and extract keywords corresponding to different remote sensing technologies.

[0038] In this embodiment, based on the differences in remote sensing research directions and application fields, the remote sensing technology system is divided into five major areas: remote sensing data preprocessing, thematic information extraction, remote sensing inversion and assimilation, remote sensing product authenticity verification, and remote sensing applications. The technical classifications of different areas and their corresponding keyword entries further draw on the classification and summarization of remote sensing technologies by the Geographical Sciences Department of the National Natural Science Foundation of China.

[0039] The remote sensing data preprocessing technology was not further classified, but rather 56 keywords were extracted based on remote sensing data preprocessing methods and remote sensing data orientation.

[0040] Thematic information extraction technologies are 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; and remote sensing change detection technology involves 36 keywords.

[0041] Remote sensing inversion and assimilation techniques are categorized into six types based on the research object: radiation and energy balance parameter remote sensing inversion and assimilation, atmospheric parameter remote sensing inversion and assimilation, vegetation parameter remote sensing inversion and assimilation, soil parameter remote sensing inversion and assimilation, water body parameter remote sensing inversion and assimilation, and ice, snow, and permafrost parameter remote sensing inversion and assimilation. The radiation and energy balance parameter remote sensing inversion and assimilation technique involves 36 keywords; atmospheric parameter remote sensing inversion and assimilation involves 58 keywords; vegetation parameter remote sensing inversion and assimilation involves 43 keywords; soil parameter remote sensing inversion and assimilation involves 25 keywords; water body parameter remote sensing inversion and assimilation involves 53 keywords; and ice, snow, and permafrost parameter remote sensing inversion and assimilation involves 30 keywords.

[0042] The authenticity verification technology for remote sensing products was not further categorized, involving 28 keyword entries.

[0043] Remote sensing applications are categorized into 14 types, including: global change remote sensing applications (31 keywords); land and resources remote sensing applications (24 keywords); environmental remote sensing (62 keywords); wetland remote sensing (50 keywords); grassland and desert remote sensing (48 keywords); hydrological remote sensing (39 keywords); disaster remote sensing (30 keywords); agricultural remote sensing (29 keywords); forestry remote sensing (25 keywords); urban remote sensing (35 keywords); polar remote sensing (33 keywords); marine remote sensing (27 keywords); planetary remote sensing (32 keywords); and socio-economic remote sensing (31 keywords).

[0044] Step 3: Use keywords to search for literature in academic databases, categorize each article, and count the number of articles and their publication time span. Figure 2 The specific steps are as follows:

[0045] Step 3-1: Use keywords of different business types and different technology types as search terms. The logical relationship between each keyword is "or". The search scope is the document titles, abstracts and keywords in the thesis database.

[0046] In this embodiment, the search scope is the CNKI database, and the search paradigm is the "or" of various business keywords and remote sensing technology keywords.

[0047] Step 3-2: Set the start and end times for the literature search.

[0048] In this embodiment, the literature search period is from 2000 to 2023.

[0049] Step 3-3: Based on the initial search results, conduct a second search, setting "remote sensing" as a necessary keyword to limit the literature to remote sensing applications.

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

[0051] Steps 3-4: Extract the original keywords from the documents that describe the subject matter, and classify the documents according to their correspondence with industry business types and their keywords.

[0052] In this embodiment, the classification and management of documents adopts the keyword method, which is completed in MySQL. Unlike the keywords in the classification system for operational applications of remote sensing technology, the keyword method here refers to directly extracting key words from the original documents that describe the document's theme, based on the keywords and subject terms in the titles and abstracts of the search results. These words are then categorized by technical and operational type according to their correspondence with the technical / operational types and keywords in the classification system for operational applications of remote sensing technology.

[0053] Steps 3-5: Count the number of documents of each type and their time span.

[0054] In this embodiment, 293,421 documents were retrieved in the field of natural resources business, of which 105,048 documents were related to remote sensing. Among the remote sensing-related documents, there were 19,129 papers on land resources business, 2,322 papers on marine business, 70,601 papers on surveying and mapping and geographic information business, and 12,996 papers on geology and mineral resources business.

[0055] Step 4: Calculate the integration level of remote sensing technology in industry operations. ;

[0056] In this embodiment, the integration of remote sensing technology into industry business applications is... The specific methods and steps for constructing quantitative evaluation indicators are as follows:

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

[0058] ;

[0059] in, This represents the degree of remote sensing dependence, with a value range of 0-1. This indicates the total number of documents for a specific business type. This indicates the number of remote sensing documents for a specific business type.

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

[0061] ;

[0062] in, This refers to the last year in which literature was published in a specific business area within the research period. Indicates the first year in which literature was published in a certain business area within the research period;

[0063] Step 4-3: Total number of documents for different business directions Time span of the literature Number of types of remote sensing technologies applied Remote sensing dependence The four indicators were normalized using the following formula:

[0064] ;

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

[0066] Step 4-4: Number of remote sensing documents for different business types Time span of the literature Number of types of remote sensing technologies applied Remote sensing dependence The sum after normalization represents the integration degree. The value range is 0-4. The specific formula for the indicator is as follows:

[0067] ;

[0068] The average integration level of remote sensing technology in the natural resources business field is 2.00. Among them, the integration level of remote sensing technology in natural resources survey and monitoring is 2.48, in land and space big data system is 2.51, in land and space use control is 1.71, in land and space planning is 2.34, in natural resources ecological protection and restoration is 1.91, in natural resources asset management is 1.86, in farmland protection supervision is 1.88, and in natural resources rights confirmation and registration is 1.55.

[0069] The remote sensing technology integration level for marine natural resource survey and monitoring is 2.72; for marine geographic information management, it is 2.33; for marine early warning and monitoring, it is 2.21; for sea area and island management, it is 1.99; for marine natural resource development and utilization, it is 1.36; for marine space use control, it is 0.02; for marine space ecological restoration, it is 0.81; and for marine strategic planning and economic activities, it is 1.80.

[0070] The remote sensing technology integration level for mineral resource protection and supervision is 3.02, for geological survey planning is 2.29, and for mining rights management is 1.18.

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

[0072] Step 5: Calculate the degree to which remote sensing technology supports industry operations;

[0073] In this embodiment, the degree of support of remote sensing technology for industry operations is... The specific methods and steps for constructing quantitative evaluation indicators are as follows:

[0074] Step 5-1: Calculate the average annual number of publications in a specific industry business area. :

[0075] ;

[0076] in, This represents the total number of documents in a specific business area. This refers to the last year in which literature was published in a specific business area within the research period. Indicates the first year in which literature was published in a certain business area within the research period;

[0077] Step 5-2: Compare the average number of documents per year And the number of business types supported by remote sensing technology The two indicators are normalized using the following formula:

[0078] ;

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

[0080] Step 5-3: Average annual number of publications in a specific business area The number of business types supported by a certain remote sensing technology The sum after normalization represents the support. The value range is 0-2. The specific formula for the indicator is as follows:

[0081] ;

[0082] The support level for remote sensing data preprocessing technology is 1.91;

[0083] The support score for remote sensing image classification technology is 1.83, for target recognition and information extraction technology it is 1.63, and for change detection technology it is 1.25.

[0084] The support scores for remote sensing inversion and assimilation techniques for radiation and energy balance parameters are 0.80, for atmospheric parameters 0.79, for vegetation parameters 1.22, for soil parameters 0.57, for water parameters 0.68, and for ice, snow, and permafrost parameters 0.01.

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

[0086] The support scores for global change remote sensing technology are 0.96, land and resources remote sensing technology is 1.64, environmental remote sensing technology is 1.38, wetland remote sensing technology is 0.88, grassland and desert remote sensing technology is 1.02, hydrological remote sensing technology is 1.34, disaster remote sensing technology is 1.27, agricultural remote sensing technology is 1.08, forestry remote sensing technology is 0.69, urban remote sensing technology is 0.81, polar remote sensing technology is 0.23, ocean remote sensing technology is 0.80, planetary remote sensing technology is 0.00, and socio-economic remote sensing technology is 1.50.

[0087] Step 6: Construct a relationship network between industry business and remote sensing technology, and calculate the weighted degree centrality of the nodes in the network;

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

[0089] ;

[0090] ;

[0091] in, This represents the weight of the edge between one industry business or remote sensing technology (node ​​k) and another industry business or remote sensing technology (node ​​l), where n is the total number of nodes connected to node k. The weighted degree centrality index for nodes.

[0092] In this embodiment, the normalized weighted degree centrality index of industry business and remote sensing technology is used. See the table below:

[0093]

[0094] Step 7: Calculate the correlation between industry operations and remote sensing technology.

[0095] In this implementation case, the normalized correlation of industry business The specific algorithm is as follows:

[0096] ;

[0097] ;

[0098] in, The degree of relevance to industry business.

[0099] Normalized correlation coefficient of remote sensing technology The specific algorithm is as follows:

[0100] ;

[0101] ;

[0102] in, The correlation of remote sensing technology.

[0103] The normalized correlation between industry operations and remote sensing technology is shown in the table below:

[0104]

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

[0106] In this implementation case, based on normalized correlation, the Fisher-Jenks natural break method is used to classify the correlation between industry operations and remote sensing technology. The steps are as follows:

[0107] Step 8-1: Set the correlation between all industry operations and remote sensing technology into a numerical array A;

[0108] Array A is set to ,in, For a specific industry business or remote sensing technology, Normalized correlation of business in this industry Or the normalized correlation coefficient corresponding to this remote sensing technology .

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

[0110] The number of correlation levels is determined to be 5, from high to low: Level V (strong correlation), Level IV (relatively strong correlation), Level III (medium correlation), Level II (relatively weak correlation), and Level I (weak correlation).

[0111] Step 8-3: Begin the initial classification, calculate the mean correlation degree and the sum of squared sample biases for each level, and sum them to obtain the total sum of squared biases within each level. The calculation formula is:

[0112] ;

[0113] in, For a given element in array A, This is the average of all normalized correlations in array A.

[0114] Write a Fisher-Jenks natural breakpoint program in Python to calculate the total sum of squared deviations. .

[0115] Step 8-4: Using a dynamic programming algorithm, continuously adjust and optimize the split points until the splitting scheme that minimizes the total intra-class variance is found.

[0116] The classification scheme for the correlation between industry business and remote sensing technology is as follows:

[0117] Level V (Strong Correlation): Construction of high-tech systems such as satellite remote sensing, remote sensing data preprocessing technology, remote sensing image classification technology, land and resources remote sensing technology, target recognition and information extraction technology;

[0118] Level IV (Stronger Relevance): Mineral resource protection and 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, socio-economic remote sensing technology;

[0119] Level III (Medium Relevance): Territorial Spatial Big Data System, Territorial Natural Resources Survey and Monitoring, Territorial Spatial Planning, Geological Survey Plan, Marine Geographic Information Management, Marine Early Warning and Monitoring, Agricultural Remote Sensing Technology, Global Change Remote Sensing Technology;

[0120] Level II (Weaker Relevance): Marine and 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 Use Control, Natural Resource Ownership Registration, Marine Natural Resource Development and Utilization, Mining Rights 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): marine spatial ecological restoration, marine spatial use control, remote sensing product authenticity verification technology, polar remote sensing technology, ice and snow permafrost parameter remote sensing inversion and assimilation technology, planetary remote sensing technology.

[0122] This invention also provides a device for quantitative evaluation and classification of the correlation between industry operations and remote sensing technology, comprising:

[0123] The industry business analysis and summary module allows for the analysis and summary of industry business types, and the extraction of keywords corresponding to different business types.

[0124] The remote sensing technology sorting and summarizing module sorts and summarizes remote sensing technologies and extracts keywords corresponding to different remote sensing technologies.

[0125] The search module uses keywords to search for literature in the thesis database, categorizes each article, and counts the number of articles and the time span of their publication.

[0126] Integration degree calculation module, which calculates the integration degree of remote sensing technology in industry operations;

[0127] The support level calculation module calculates the degree to which remote sensing technology supports industry operations.

[0128] The node weighted degree centrality calculation module uses industry business and remote sensing technology as nodes to construct a relationship network and calculate the node weighted degree centrality of the network.

[0129] The correlation calculation module calculates the normalized correlation between industry business and remote sensing technology.

[0130] The hierarchical module classifies data based on normalized correlation degree.

[0131] Another aspect of the present invention provides an electronic device, comprising: one or more processors; and 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 cause the one or more processors to implement the method.

[0132] Another aspect of the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the method described thereon.

[0133] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0134] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0138] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for quantitative evaluation and classification of the correlation between industry operations and remote sensing technology, characterized in that, Includes the following steps: Step 1: Conduct a review and summary of industry business types, and extract keywords corresponding to different business types; Step 2: Conduct a sorting and summary of remote sensing technology types, and extract keywords corresponding to different remote sensing technologies; Step 3: Use keywords to search for literature in the paper database, categorize each paper, and count the number of papers and the time span of their publication. Step 4: Calculate the integration level of remote sensing technology in industry operations; Step 5: Calculate the degree to which remote sensing technology supports industry operations; Step 6: Using industry business and remote sensing technology as nodes, construct a relationship network and calculate the normalized weighted degree centrality index of the network's nodes; Step 7: Calculate the normalized correlation between industry operations and remote sensing technology; Step 8: Classify based on normalized correlation degree.

2. The method for quantitative evaluation and classification of the correlation between industry operations and remote sensing technology according to claim 1, characterized in that, Step 3 consists of the following steps: Step 3-1: Use keywords of different business types and different technology types as search terms. The logical relationship between each keyword is OR. The search scope is the document titles, abstracts and keywords in the thesis database. Step 3-2: Set the start and end times for the literature search.

3. The method for quantitative evaluation and classification of the correlation between industry operations and remote sensing technology according to claim 1, characterized in that, In step 4, the degree of integration of remote sensing technology into industry operations. The specific calculation method is as follows: Step 4-1: First, calculate the remote sensing dependence. : ； in, This indicates the total number of documents for a specific business type. This indicates the number of remote sensing documents for a specific service type. Step 4-2: Calculate the time span of literature in a specific business area : ； in, This refers to the last year in which literature was published in a specific business area within the research period. Indicates the first year in which literature was published in a certain business area within the research period; Step 4-3: Count the number of remote sensing documents for different business types Time span of the literature Number of types of remote sensing technologies applied Remote sensing dependence Normalization is performed, and the specific formula for normalization is as follows: ； Where f(x) is the normalization function, and x represents the index that needs to be normalized; Step 4-4: The sum of the normalized values ​​of the four metrics represents the integration degree. The specific formula for the indicator is as follows: 。 4. The method for quantitative evaluation and classification of the correlation between industry operations and remote sensing technology according to claim 1, characterized in that, In step 5, the remote sensing technology support index The specific calculation method is as follows: Step 5-1: Calculate the average annual number of publications in a specific industry business area. : ； Step 5-2: Analyze the average number of documents per year And the number of business types supported by remote sensing technology The two indicators are normalized using the following formula: ； Where f(x) is the normalization function, and x represents the index that needs to be normalized; Step 5-3: The sum of the normalized values ​​of the two indicators represents the support level. The specific formula for the indicator is as follows: 。 5. The method for quantitative evaluation and classification of the correlation between industry operations and remote sensing technology according to claim 1, characterized in that, In step 6, the node normalized weighted degree centrality index The specific algorithm is as follows: ； ； in, This represents the weight of the edge between node k (a business or remote sensing technology in one industry) and node l (a business or remote sensing technology in another industry), where n is the total number of nodes connected to node k. The weighted degree centrality index for nodes.

6. The method for quantitative evaluation and classification of the correlation between industry operations and remote sensing technology according to claim 1, characterized in that, In step 7, the method for calculating the normalized correlation between industry operations and remote sensing technology is as follows: Normalized correlation of industry business The specific algorithm is as follows: ； ； in, The degree of relevance to industry business; Normalized correlation coefficient of remote sensing technology The specific algorithm is as follows: ； ； in, The correlation of remote sensing technology.

7. The method for quantitative evaluation and classification of the correlation between industry operations and remote sensing technology according to claim 1, characterized in that, In step 8, the Fisher-Jenks natural break method is used to classify industry operations and remote sensing technologies. The steps are as follows: Step 8-1: Set the normalized correlation between all industry operations and remote sensing technologies to a numerical array A; Step 8-2: Determine the number of correlation levels; Step 8-3: Begin the initial classification, calculate the normalized mean correlation coefficient and the sum of squared sample biases for each level, and sum them to obtain the total sum of squared biases within each level. The calculation formula is: ； in, For a given element in array A, This is the average value of all elements in array A; Step 8-4: Using a dynamic programming algorithm, continuously adjust and optimize the split points until the splitting scheme that minimizes the total intra-class variance is found.

8. A device for quantitatively evaluating and classifying the correlation between industry operations and remote sensing technology, characterized in that, include: The industry business analysis and summary module allows for the analysis and summary of industry business types, and the extraction of keywords corresponding to different business types. The remote sensing technology sorting and summarizing module sorts and summarizes remote sensing technologies and extracts keywords corresponding to different remote sensing technologies. The search module uses keywords to search for literature in the thesis database, categorizes each article, and counts the number of articles and the time span of their publication. Integration degree calculation module, which calculates the integration degree of remote sensing technology in industry operations; The support level calculation module calculates the degree to which remote sensing technology supports industry operations. The node weighted degree centrality calculation module uses industry business and remote sensing technology as nodes to construct a relationship network and calculate the node weighted degree centrality of the network. The correlation calculation module calculates the normalized correlation between industry business and remote sensing technology. The hierarchical module classifies data based on normalized correlation degree.

9. An electronic device, characterized in that, include: 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 cause the one or more processors to implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, cause the processor to perform the method described in any one of claims 1 to 7.

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