A method for constructing a management and service system for remote sensing application technology achievements

Abstract

The application provides a management and service system construction method of remote sensing application technology achievements, comprising the following steps: step S101, carding a remote sensing application technology achievement knowledge system, first defining a knowledge extraction process from application case disassembly and knowledge element modeling, and on this basis, forming a knowledge classification and grading framework system from the aspects of type, field and grade; step S102, forming a remote sensing application knowledge base through disassembly of a target case set; step S103, iteratively updating the technology achievement base, and recording original contributions through a blockchain technology to form a remote sensing application technology achievement update iteration system; the iterative update of the technology achievement base is carried out based on the structured modeling of knowledge elements in the technology achievement base, and is completed through the making and inputting of a new case package; and step S104, constructing the management and service system of remote sensing application technology achievements through atlas display, statistical analysis and blockchain smart contract functions.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of remote sensing application, and particularly relates to a management and service system construction method for remote sensing application technical achievements. BACKGROUND

[0002] Remote sensing information is a natural information space-time framework. It provides a basis for fully depicting social and economic activities and their impact on the natural environment through all-time and all-directional observation of the earth system, and plays a huge social and economic benefit in national economic and social development, ecological construction, livelihood security and national security.

[0003] With the in-depth development of remote sensing application, the previous data intelligence demand has gradually changed to cognitive intelligence demand. Intelligent discovery and management of knowledge, and use of logic and tools to answer causal questions have become the direction of current data science and artificial intelligence revolution. The development of data science and the deepening of ontology research bring new impetus to digital governance of technical achievements and efficient discovery and transfer of remote sensing application knowledge. China's remote sensing field has the characteristics of multiple application scenarios, multiple innovation elements, multiple practitioners and large market size. It has inherent needs to break the traditional mode of "one issue, one discussion" in the current application process, and to build an intelligent service system for remote sensing application and promote the cross-layer development of remote sensing application through group collaboration.

[0004] However, the current remote sensing application has the problems of coexistence of comprehensiveness and fragmentation, prominent contradiction between repetition and idling, prominent contradiction between technical reuse demand and sharing difficulty, etc.

[0005] Remote sensing has multi-element information representation and carrying capacity, and its comprehensive application is an effective aid to support the business integration of industry departments and realize collaborative development. Under the background of national high-quality development, it has potential prospects and demand. At present, remote sensing application has been deeply involved in natural resources, emergency management, public safety, national defense construction, ecological environment, agriculture and rural areas, transportation, meteorology and water conservancy, ocean and many other fields. In the long-term business operation, various scientific research departments have undertaken various application tasks in industry and field, and have helped to accumulate a large amount of remote sensing industry application achievements. The demand for cross-field and cross-industry interaction and sharing of data and technology is becoming more and more vigorous.

[0006] However, due to historical accumulation and industry differences, different fields and departments in China have various forms of remote sensing information description, specifications, grades, content organization, etc. Related applications are mostly based on independent data of their own business chain. At the same time, these achievements are often limited to the business field they face or serve the relevant work of the department they belong to, or even remain in the relevant experience of the research and development researchers, and are difficult to reuse. Data and technology sharing can activate existing achievements and provide a basis for realizing comprehensive application of remote sensing. However, due to the lack of trust sources, security difficulties, and lack of information control, the exchange and sharing of information also face difficulties such as difficulty, fear, and unwillingness. SUMMARY

[0007] The technical scheme of the present application is a management and service system construction method for remote sensing application technology achievements, comprising the following steps:

[0008] Step S101, the knowledge system of remote sensing application technology achievements is sorted out. First, the knowledge extraction process is defined from two aspects of application case disassembly and knowledge element modeling. On this basis, a classification and grading framework system of knowledge is formed from the aspects of type, field and level;

[0009] Step S102, the remote sensing application knowledge base is formed by disassembling the target case set;

[0010] Step S103, the technology achievement library is iteratively updated, and the original contribution is recorded through the blockchain technology to form a remote sensing application technology achievement updating and iteration system. The iterative update of the technology achievement library is based on the structured modeling of the knowledge elements in the technology achievement library, and is completed through the making and inputting of new case packages;

[0011] Step S104, the management and service system of remote sensing application technology achievements is constructed through the functions of atlas display, statistical analysis and blockchain smart contract.

[0012] Further, the knowledge extraction process includes two aspects of case disassembly and knowledge modeling. Case disassembly mainly defines knowledge elements contained in the case, defines knowledge points, determines knowledge packages, and formulates a tag system. The knowledge elements formed after case disassembly are modeled to form metadata documents and unique identification codes, so as to realize standardized and normalized expression of knowledge elements.

[0013] Further, the knowledge framework system is constructed from three aspects of knowledge type, field and level. The knowledge type includes project information, process information, algorithm information and team information. The field covers natural resources, ecological environment, disaster emergency, agriculture and rural areas. The level includes algorithm level, data level, correlation level and correlation frequency.

[0014] Further, the target case is disassembled from the two dimensions of scientific research achievements and project management to form knowledge elements including data, algorithms, technical processes and documents, and to establish a case knowledge package; the knowledge elements in the application achievement case package are included in the knowledge framework system and are subjected to upper chain processing, and finally a remote sensing application technology achievement knowledge base is constructed.

[0015] Further, the knowledge element upper chain processing refers to storing all knowledge elements in the technology achievement library based on the block chain technology, and the knowledge element upper chain will generate a block header and a block body; wherein the block header includes block number, current block chain hash, block parent hash, block data_hash value and access quantity; the block body includes access id, access proposal_hash value, access payload value, access timestamp block and access data; a knowledge use ledger is established through the block chain to record the whole process of knowledge generation, use and circulation.

[0016] Further, the technology achievement iteration system includes two iteration modes, based on the structured modeling results of knowledge elements, a case package and a knowledge element entry template are formed, new introduced cases are stored and subjected to upper chain processing; and based on the knowledge elements covered in the existing case package, a new case generation template based on cross reference is provided, and new knowledge elements in the generated case are stored and subjected to upper chain processing, and a block is added to the existing knowledge element chain to record its contribution.

[0017] Further, the atlas display establishes knowledge association based on the label system of knowledge elements, forms a whole display for remote sensing technology achievements, and provides knowledge guide for users; the statistical analysis function is based on the block chain record data to statistically analyze the use of knowledge, provide knowledge recommendation service for users, evaluate the contribution of users, and use the smart contract function of the block chain to assist in establishing the contact between knowledge producers and users.

[0018] Further, the atlas display establishes the association relationship of knowledge elements through labels, and systematically displays the knowledge elements in the technology achievement library from the aspects of knowledge element type, field and level;

[0019] The statistical analysis and evaluation mainly statistically analyze the information of the source, use frequency and service object of knowledge, and summarize the different user preferences, to provide convenience for users to accurately obtain the required information;

[0020] The smart contract mainly uses protocols and user interfaces to complete all steps of the contract process, allows users to realize personalized code logic on the block chain, updates the ledger data through chain code, records service information, and provides a basis for knowledge transaction.

[0021] Beneficial effects:

[0022] The present application introduces digitization and intelligent technology, and standardizes the expression, integration and application of existing data and technical achievements, which is a problem to be solved in remote sensing comprehensive application. The present application breaks the traditional sharing scheme of concentrating information into a unified data center, introduces decentralization, and clearly protects intellectual property rights, which is the key to promoting information sharing and assisting remote sensing comprehensive application. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 : Method flowchart of the present application;

[0024] Figure 2 : Management and service system block diagram of the present application;

[0025] Figure 3 : Multi-source knowledge data chaining process schematic diagram of the present application;

[0026] Figure 4 : Block chain contract platform block diagram of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0028] In view of the problems existing in the sharing and reuse of remote sensing application technical achievements, the embodiments of the present application take the management and service of remote sensing application achievements as the target, link the achievements knowledge through the remote sensing application business framework system, adopt the knowledge graph technology to construct the remote sensing application knowledge engineering, and introduce the block chain technology to carry out the intellectual property protection. Under the logic chain from knowledge mining to knowledge management, and then to knowledge reuse and service, the expert wisdom and the block chain technology are fused to manage the remote sensing application technical achievements, and the iterative updating capability is formed, and finally the remote sensing application technical achievement service platform is constructed.

[0029] In the embodiments of the present application, the remote sensing application technical achievement management and service system carries out the remote sensing application technical achievement knowledge structure carding through the expert knowledge, constructs and updates the iterative knowledge base on this basis, combines the block chain technology to carry out the decentralized management of the knowledge, and finally adopts the knowledge graph and the block chain smart contract to provide the knowledge service. The technology can solve the problems of coexistence of comprehensiveness and fragmentation, coexistence of repetition and idling, and contradiction between strong technical reuse demand and sharing difficulty in the remote sensing application process, and provides technical support for activating remote sensing application.

[0030] The details of the embodiments of the present disclosure are described in detail below through specific embodiments.

[0031] As shown in Figure 1 FIG. 1 is a flowchart of a method for constructing management and service of remote sensing application technology achievements, which includes the following steps:

[0032] Step S101, the knowledge system of remote sensing application technology achievements is combed, first, the knowledge extraction process is defined from two aspects of application case disassembly and knowledge element modeling, and on this basis, a classification and grading framework system of knowledge is formed from the aspects of type, field, and level;

[0033] Step S102, a remote sensing application knowledge base is formed through disassembly of a target case set;

[0034] Step S103, the technology achievement library is iteratively updated, and the original contribution is recorded through the blockchain technology to form a remote sensing application technology achievement updating iteration system; the iterative updating of the technology achievement library is based on the structured modeling of the knowledge elements in the technology achievement library, and is completed through the making and inputting of new case packages;

[0035] Step S104, the management and service system of remote sensing application technology achievements is constructed through atlas display, statistical analysis, and blockchain smart contract functions.

[0036] Specifically, referring to Figure 2 In step S101, the knowledge system of remote sensing application technology achievements is combed, first, the knowledge extraction process is defined from two aspects of application case disassembly and knowledge element modeling, and on this basis, a classification and grading framework system of knowledge is formed from the aspects of type, field, and level, which provides a conceptual basis for the construction of the knowledge base.

[0037] The knowledge extraction process includes two aspects of case disassembly and knowledge modeling, the case disassembly mainly defines the knowledge elements contained in the case, defines the knowledge points, determines the knowledge package, and formulates the tag system, etc.; the knowledge elements formed after the case disassembly are modeled to form metadata documents and unique identification codes, so as to realize the standardized and normalized expression of the knowledge elements. The case disassembly is to decompose the technical process of the case to form multiple steps associated with each other, each step includes core algorithms, input data, output results, etc. Knowledge modeling refers to defining knowledge from three aspects of business system, technical system, and product system. Among them, the business system refers to the industry field that the application case can support, including natural resources, ecological environment, agriculture and rural areas, disaster emergency, etc.; the technical system refers to remote sensing data preprocessing, remote sensing inversion and assimilation, remote sensing product authenticity verification, thematic information extraction, remote sensing application service, etc.; the product system mainly includes remote sensing data, common products, thematic products, service products, etc. For each subfield, there are corresponding keywords as tags to further define the knowledge.

[0038] The knowledge framework system is mainly constructed from three aspects of knowledge type, field, and level. The knowledge type includes project information, process information, algorithm information, and team information, etc. The field covers natural resources, ecological environment, disaster emergency, agriculture and rural areas, etc. The level includes algorithm level, data level, correlation level, and correlation frequency, etc.

[0039] In step S102, the remote sensing application technology achievement knowledge base is formed by disassembling the target case set. The target case is disassembled from two dimensions of scientific research achievements and project management to form knowledge elements including data, algorithms, technical processes, documents, etc. to establish a case knowledge package. For example, taking landslide disaster remote sensing monitoring as an example, the following disassembly is carried out:

[0040] 1. Project management dimension, including case basic situation and case supporting project situation:

[0041] Case basic situation:

[0042] Field-oriented: geological disaster emergency management;

[0043] Region-oriented: northern region of Guizhou Province;

[0044] Problem solved: early identification and monitoring evaluation of geological disaster hazards;

[0045] Case supporting project situation:

[0046] Project source: XX;

[0047] Funds: xx ten thousand yuan;

[0048] Execution period: XX year;

[0049] 2. Scientific research achievements dimension, including technical process, data, algorithm, and document:

[0050] Technical process: technical process description;

[0051] 1) This project uses Sentinel-1 radar image data. First, the image data is registered, and the MT-InSAR multi-temporal interferometric measurement method is used to obtain the surface deformation distribution map and the surface deformation rate distribution map. Based on remote sensing image, the landslide disaster area is extracted to obtain the landslide point distribution map and coordinate data;

[0052] 2) The landslide point data are fused with topographic data, vegetation coverage NDVI, river, road data, land use data, and geological disaster investigation data as input, and a landslide susceptibility evaluation algorithm based on deep learning is used to obtain the landslide susceptibility evaluation zoning map;

[0053] Data: data name, data format, latitude and longitude range, administrative region, time range, spatial resolution, data source, data quality requirement;

[0054] 1) Sentinel-1 (C-band) radar satellite image data, interferometric wide swath mode SLC, 106°-107°N, 27°-28°E, northern Guizhou Province, June 2020-June 2021, 20-meter resolution, ESA, standard format data;

[0055] 2) topographic data, digital elevation dem.tif, 106°-107°N, 27°-28°E, northern Guizhou Province, 30m, NASA;

[0056] 3) vegetation coverage NDVI, tif, 106°-107°N, 27°-28°E, northern Guizhou Province, 50m, ESA;

[0057] 4) rivers, roads, shp, 106°-107°N, 27°-28°E, northern Guizhou Province, river level, road level, etc., land and resources;

[0058] 5) land use data, tif, 106°-107°N, 27°-28°E, northern Guizhou Province, 1000m, land and resources;

[0059] 6) geological disaster investigation data, shp, 106°-107°N, 27°-28°E, northern Guizhou Province, land and resources;

[0060] Algorithm: algorithm / operator name, function or role, processing speed, programming language;

[0061] 1) satellite radar image registration, image registration, 24h, python;

[0062] 2) MT-InSAR multi-temporal interferometric measurement, long-time sequence of ground deformation information, 24h-72h, python;

[0063] 3) landslide disaster area extraction based on remote sensing image, python;

[0064] 4) deep learning-based susceptibility assessment, landslide susceptibility assessment, 10h, python;

[0065] Document: papers, patents, awards, project applications, etc.

[0066] The knowledge elements in the application technology achievement case knowledge package are included in the knowledge framework system, and are processed in chain, and finally the remote sensing application technology achievement knowledge base is built.

[0067] The knowledge element on-chain processing refers to storing all knowledge elements in the technology achievement library based on the block chain technology. The knowledge element on-chain will generate block header and block body. The block header includes block number, current block chain hash, block parent hash, block data_hash value and access quantity. The block body includes access id, access proposal_hash value, access payload value, access timestamp block and access data. The knowledge use ledger is established through the block chain to record the whole process of knowledge generation, use and circulation. Specifically, the block chain on-chain hash data is combined with the technical means of distributed network storage file. For knowledge elements such as text, picture and file, they are stored in the network server, and the hash value of the file is calculated through the hash algorithm, and the calculated hash value is chained. Due to the large volume of multi-source knowledge data files, it is not suitable to directly chain the data, so the data needs to be stored in another server, and the block chain only records the hash. In order to manage the data more conveniently, a data management platform is built to visualize the data display and management. The key operations of the data are recorded in the block chain in the form of smart contract. The smart contract is the key to realize the diversity of block chain function. In order to facilitate the management of transaction data, the format needs to be standardized before the data is chained to facilitate the contract writing. In order to realize the demand of knowledge data traceability, related functions can be written in the contract to obtain transaction information, record resource access times, obtain proposal hash, range query resource information and other functions, such as Figure 3 as shown.

[0068] In step S103, the technology achievement library is iteratively updated, and the original contribution is recorded through the block chain technology, such as the technical achievement description, scheme, algorithm, model and intellectual property provided by the achievement provider, to form a remote sensing application technology achievement updating iteration system. The iterative update of the technology achievement library is based on the structured modeling of the knowledge elements in the technology achievement library, which is completed through the preparation and input of new case packages.

[0069] The technology achievement iteration system mainly includes two modes. On the one hand, based on the structured modeling results of knowledge elements, case packages and knowledge element input templates are formed to process the new introduced cases. The case package is composed of basic data, algorithm model, technical process and other knowledge elements. The input templates of different knowledge elements are different. The basic data mainly includes data name, data format, latitude and longitude range, administrative region, time range, spatial resolution, data source, data quality requirement and the like. The algorithm model includes function or role, processing speed, programming language, running environment and the like. The technical process includes input data, algorithm model, output result and the like. On the other hand, based on the knowledge elements covered in the existing case package, a new case generation template based on cross reference is provided, including the case element name and the source case information. The new knowledge elements in the generated case are stored and chained, and the block record is added to the existing knowledge element chain to record its contribution. Through knowledge reuse, the knowledge elements from different case packages can realize new functions after recombination.

[0070] In step S104, a management and service system of remote sensing application technology achievements is constructed through functions such as atlas display, statistical analysis, and smart contract of block chain. The atlas display establishes knowledge association based on the label system of knowledge elements. The standardized label names of knowledge, its belonging category and field, key technology used, belonging personnel or team and the like are taken as main entities in the knowledge graph, which are expressed as nodes in the graph. The connection edge between two nodes is taken as semantic relationship description, so as to form knowledge association expressed in the form of graph network, to form overall display of remote sensing technology achievements, and to provide knowledge guide for users. The statistical analysis function is based on the record data of block chain, and the use of knowledge is statistically analyzed to provide knowledge recommendation service for users, and to evaluate the contribution of users. The smart contract function of block chain is used to assist in establishing the contact between knowledge producers and users, to promote knowledge service on the basis of protecting intellectual property rights.

[0071] The contract function and implementation are specifically described as follows: the technology management department signs and notarizes the knowledge achievements of the producers, and automatically sends the arrival task through the network. The functions such as automatic registration of arrival request, timing of arrival task, automatic registration of arrival result, automatic storage of arrival receipt, automatic acquisition of arrival result and the like are realized, as shown in FIG. 8. Figure 4

[0072] The atlas display establishes knowledge element association relationship through labels, and systematically displays the knowledge elements in the technology achievement library from the aspects of knowledge element type, field, level and the like.

[0073] ​The statistical analysis evaluation mainly carries out statistical analysis on information such as the source of knowledge, the frequency of use, the service object and the like, and summarizes different user preferences and the like, so as to provide convenience for users to accurately obtain required information.

[0074] The smart contract mainly utilizes a protocol and a user interface to complete all steps of a contract process, allows users to realize personalized code logic on a blockchain, updates ledger data through chain code, records service information and provides a basis for knowledge transaction.

[0075] Although the above describes the specific embodiments of the present application in a demonstrative manner, so as to facilitate the understanding of the present application by the person skilled in the art, and it should be clear that the present application is not limited to the scope of the specific embodiments, and for the person skilled in the art, as long as various changes are within the spirit and scope of the present application defined and determined by the appended claims, all the application creations utilizing the concept of the present application are within the protection.

Claims

1. A method for constructing a management and service system for remote sensing application technology achievements, characterized in that, Includes the following steps: Step S101: Organize the knowledge system of remote sensing application technology achievements. First, define the knowledge extraction process from two aspects: application case decomposition and knowledge element modeling. On this basis, form a knowledge classification and grading framework system from the perspectives of type, domain and level. Step S102: A remote sensing application knowledge base is formed by decomposing the target case set; Step S103: Iteratively update the technology achievement database and record the original contributions through blockchain technology to form a remote sensing application technology achievement update and iteration system; the iterative update of the technology achievement database is based on the structured modeling of the knowledge elements in the technology achievement database and is completed through the creation and input of new case packages; Step S104: Construct a management and service system for remote sensing application technology achievements through map display, statistical analysis, and blockchain smart contract functions; The target case is broken down into two dimensions: scientific research results and project management, forming knowledge elements including data, algorithms, technical processes, and documents, and a case knowledge package is established. The knowledge elements in the case package are incorporated into the knowledge framework system and processed on the blockchain, ultimately constructing a knowledge base for remote sensing application technology achievements. The on-chain processing refers to storing all knowledge elements in the technology achievement repository based on blockchain technology. Uploading knowledge elements to the blockchain will generate a block header and a block body. The block header includes the block number, current blockchain hash, block parent hash, block data_hash value, and access count. The block body includes the access ID, access proposal_hash value, access payload value, access timestamp block, and access data. A knowledge usage ledger is established through the blockchain to record the entire process of knowledge generation, use, and circulation. The technology achievement update and iteration system includes two iteration modes: based on the structured modeling results of knowledge elements, a case package and knowledge element input template are formed, and newly introduced cases are processed for storage and on-chain processing; and based on the knowledge elements covered in the existing case package, a new case generation template based on cross-reference is provided, and new knowledge elements in the generated cases are stored and on-chain, while blocks are added to the existing knowledge element chain to record their contributions. The map display establishes knowledge associations based on a tagging system of knowledge elements, presenting a holistic view of remote sensing technology achievements and providing users with knowledge navigation; the statistical analysis function uses blockchain-recorded data to statistically analyze the use of knowledge, providing users with knowledge recommendation services, while also evaluating user contributions. It also uses the smart contract function of blockchain to help establish connections between knowledge producers and users. The map display establishes relationships between knowledge elements through tags, and systematically displays the knowledge elements in the technology achievement database from the aspects of knowledge element type, field, and level; The statistical analysis includes statistical analysis of information on knowledge sources, usage frequency, and service targets. At the same time, it summarizes and generalizes information on different user preferences to facilitate users in accurately obtaining the information they need. The smart contract mainly uses protocols and user interfaces to complete all steps of the contract process, allowing users to implement personalized code logic on the blockchain, update ledger data through chaincode, record service information, and provide a foundation for knowledge transactions; The knowledge system is constructed from three aspects: knowledge type, domain, and level. The knowledge type is divided into project information, process information, algorithm information, and team information; the domain covers natural resources, ecological environment, disaster emergency response, and agriculture and rural areas; the level includes algorithm level, data level, association level, and association frequency. The knowledge extraction process includes two aspects: case decomposition and knowledge modeling. Case decomposition mainly involves defining the knowledge elements contained in the case, defining the knowledge points, determining the knowledge package, and formulating the tag system. After case decomposition, the knowledge elements are transformed into metadata documents and unique identification codes through knowledge modeling, thereby realizing the standardized and normalized expression of knowledge elements.

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