CN117634997A - Deep neural network method for asset positioning and drawing of enterprise organization value chain - Google Patents
Deep neural network method for asset positioning and drawing of enterprise organization value chain Download PDFInfo
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Abstract
A deep neural network method for asset positioning and mapping of an enterprise organization value chain comprises the following steps: identifying business units and asset entities of the enterprise value chain, and establishing an enterprise value chain description data dictionary; extracting business units of an enterprise value chain, names, types, addresses and rights information of assets based on a deep neural network language processing model, forming a full-flow business logic model taking physical assets as cores in an enterprise organization value chain, and checking and correcting asset entities; establishing a large language model based on deep neural network physical asset logic model data analysis, carrying out space address matching of physical assets by combining an online position service platform, extracting space coordinates of enterprise business units and assets, and determining the geometric types of targets such as the enterprise business units and the assets; on the basis, obtaining an image boundary, and dividing a business unit and an asset entity on a remote sensing image based on a deep neural network model; drawing synthesis is carried out on boundaries of the business units and the assets, positioning data are output to a database, and classification mapping and management are carried out. The invention is oriented to the requirement of the enterprise sustainable information disclosure process on quantitative assessment of the climate risk of the physical asset, and based on the language model and the image segmentation model of the deep neural network, the semantic information extraction, the address matching and the remote sensing image segmentation of the business units and the assets of the enterprise organization value chain are realized, so that the problem of data missing in the quantitative assessment of the climate risk of the enterprise management activities in a large range is overcome, the efficient and high-precision positioning and drawing of the assets of the large-scale enterprise organization value chain are realized, and the accurate and credible sustainable information is provided for the ESG assessment and disclosure of the enterprise and the credit and bond issuing.
Description
Technical Field
The invention discloses a deep neural network method for asset positioning and drawing of an enterprise organization value chain, and belongs to the fields of sustainable development and ESG, artificial intelligence and big data application.
Background
Enterprises are the basic unit of social operation, and enterprise-level sustainability metering and disclosure becomes a key issue for government regulatory authorities, sustainable supply chain management, and capital market concerns for credit, insurance, bonds, stocks, and the like. The current sustainability evaluation of enterprises is mainly based on a sustainability evaluation framework at the enterprise entity level proposed by a sustainability accounting guidelines committee (Sustainability Accounting Standards Board, SASB), a global reporting initiative (Global Reporting Initiative, GRI) and an international sustainability guidelines council (International Sustainability Standards Board, ISSB), and data collection, analysis and mining are performed around three dimensions of an Environment (E), society (society, S) and a Governance (G) to generate sustainable development reports, and enterprise ESG metering and disclosure are realized to interested relatives such as government regulatory departments, stakeholders, financial markets, upstream and downstream clients of an enterprise value chain under the guidance of certain rules.
The quantitative evaluation of the environmental dimension of the enterprise ESG comprises the fields of climate change, natural resources, pollution and environmental protection, biological diversity protection and the like, wherein the risk evaluation of the climate change is an important content of the quantitative evaluation of the enterprise ESG, and the climate-related financial information disclosure work group (Task Force on Climate-Related Financial Disclosures, TCFD) constructs an analysis and disclosure framework for evaluating the influence of the climate change on the future operation of the enterprise so as to evaluate the possible influence of the climate change on the aspects of income, cost expenditure, assets, liabilities and the like of the enterprise. To quantitatively evaluate the economic impact of climate change on enterprise operation, it is necessary to locate assets involved in the enterprise value chain to spatial locations, and then quantitatively estimate future potential risks and opportunities for the enterprise using a scenario analysis and loss model of climate change. Asset identification, positioning and mapping in an enterprise value chain are the basis for evaluating the climate risk of enterprise management, and high-efficiency and high-precision asset identification, positioning and mapping data provide basic support for evaluating financial risk and opportunity of enterprises in future climate change scenes. In business practice, the prior art is difficult to efficiently and accurately acquire asset positioning and entity space data on an enterprise value chain. The deep neural network is an artificial intelligence implementation mode and is widely applied to the fields of natural language understanding, image recognition and understanding and scene reconstruction generation at present. Therefore, a natural language processing model and an image processing model based on a deep neural network are adopted to realize efficient and accurate asset positioning and drawing of the enterprise organization value chain, and the method has important significance for quantitative assessment of climate risks of enterprise business activities.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a deep neural network method for asset positioning and drawing of an enterprise organization value chain, which aims to meet the requirements of quantitative assessment on the climate risk of business activities in ESG assessment and disclosure of enterprises, is based on space-sky remote sensing image data, vector maps and enterprise operation facility data, utilizes natural language understanding and image recognition algorithm to realize efficient and high-precision asset positioning and drawing of the enterprise value chain, solves the problem of efficient and accurate positioning of asset combinations in quantitative assessment on the climate risk and financial influence of opportunity in the ESG sustainability metering and disclosure process of enterprises, provides sustainability information based on scientific data and calculation model for ESG assessment and disclosure and sustainability credit and bond release, and ensures the scientificity and credibility of the climate risk influence assessment.
In order to achieve the above purpose, the deep neural network method for asset positioning and mapping of the enterprise organization value chain comprises the following steps:
s1, identifying business units and asset entities of an enterprise value chain according to enterprise organization architecture, business processes and upstream and downstream customer description information, and establishing an enterprise value chain description data dictionary;
s2, extracting name, type, address and ownership information of the business units and the assets of the enterprise value chain based on the deep neural network language processing model by utilizing the enterprise value chain description data dictionary established in the S1, associating physical assets in the data dictionary to form a full-flow business logic model taking the physical assets as cores in the enterprise organization value chain, and checking and correcting asset entities;
s3, establishing a large language model based on physical asset logic model data analysis of the deep neural network through fine tuning optimization, carrying out space address matching of physical assets by combining an online position service platform under the support of the large language model, extracting space coordinates of enterprise business units and assets, and determining geometric types of targets such as the enterprise business units and the assets;
s4, utilizing the space coordinates and the target geometric types of the business units and the assets obtained in the S3 to obtain image boundaries, and dividing the business units and the asset entities on a remote sensing image based on a deep neural network model;
s5, drawing and integrating the boundaries of the business units and the assets by utilizing the segmentation result in S4, outputting positioning data to a database, and carrying out classification mapping and management.
The invention relates to a deep neural network method for asset positioning and drawing of an enterprise organization value chain, which is used for extracting business units and asset semantic information of the enterprise organization value chain based on a language model of a deep neural network architecture and carrying out address matching through an online position service platform; the remote sensing images of the areas where the business units and the assets are located are selected by utilizing address matching, and the spatial positions of the business units and the asset entities are acquired based on a deep neural network remote sensing image segmentation method, so that the high-efficiency and high-precision positioning and drawing of the assets of the large-scale enterprise organization value chain are realized, and basic data is provided for quantitative assessment of the climate risks of the large-scale enterprise management activities.
The beneficial effects of the invention are as follows:
(1) The invention combines the business units of enterprises, the description files of assets, remote sensing images and addresses provided by location services, and solves the problem of data missing in quantitative assessment of climate risk of large-scale enterprise business activities;
(2) The invention realizes the semantic information extraction, address matching and remote sensing image segmentation of the business units and the assets of the enterprise organization value chain based on the language model and the image segmentation model of the deep neural network, and can efficiently and accurately realize the asset positioning and drawing of the large-scale enterprise organization value chain.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a deep neural network method workflow for asset location and mapping in an enterprise organization value chain.
Detailed Description
As shown in FIG. 1, a deep neural network method for asset positioning and mapping of an enterprise organization value chain comprises the following steps:
s1, identifying business units and asset entities of an enterprise value chain according to enterprise organization architecture, business processes and upstream and downstream customer description information, and establishing an enterprise value chain description data dictionary; s2, extracting name, type, address and ownership information of the business units and the assets of the enterprise value chain based on the deep neural network language processing model by utilizing the enterprise value chain description data dictionary established in the S1, associating physical assets in the data dictionary to form a full-flow business logic model taking the physical assets as cores in the enterprise organization value chain, and checking and correcting asset entities; s3, establishing a large language model based on physical asset logic model data analysis of the deep neural network through fine tuning optimization, carrying out space address matching of physical assets by combining an online position service platform under the support of the large language model, extracting space coordinates of enterprise business units and assets, and determining geometric types of targets such as the enterprise business units and the assets; s4, utilizing the space coordinates and the target geometric types of the business units and the assets obtained in the S3 to obtain image boundaries, and dividing the business units and the asset entities on a remote sensing image based on a deep neural network model; s5, drawing and integrating the boundaries of the business units and the assets by utilizing the segmentation result in S4, outputting positioning data to a database, and carrying out classification mapping and management.
The step S1 of the deep neural network method for locating and drawing the assets of the enterprise organization value chain comprises the following steps:
s11, collecting enterprise organization architecture information, including enterprise organization structure diagrams, determining business functions of each organization unit, S12, obtaining business processes of production, sales, service and the like of enterprise organizations, investigating the relationship between the upper and lower clients, and confirming key activities and stages in the business processes; s13, identifying and extracting assets in enterprise organizations, assets at the upstream and downstream of a value chain, including all physical assets (production facilities, production operation sites, storage sites, transport paths) related to links such as transportation, production operation and sales, and the like; s14, establishing a data description dictionary of the physical assets in the enterprise value chain, wherein dictionary contents surround the physical assets, and providing attribute information including business units and business processes, asset entity types, names, addresses, rights and the like to which the physical assets belong.
The step S2 of the deep neural network method for locating and drawing the assets of the enterprise organization value chain comprises the following steps:
s21, extracting information from the assets, names, types, addresses and rights of the assets by using a language processing model based on a deep neural network by using the physical asset description data dictionary in the enterprise value chain established in the S1; s22, associating physical assets in the data dictionary to form a full-flow business logic model taking the physical assets as cores in an enterprise organization value chain; s23, manually checking and correcting the data information of the physical asset logic model in the generated enterprise full business process.
The step S3 of the deep neural network method for locating and drawing the assets of the enterprise organization value chain comprises the following steps:
s31, performing prompt word fine tuning optimization on a large language model based on a deep neural network, and establishing a field model suitable for data information analysis of the physical asset logic model in S2; s32, analyzing semantic address information in a physical asset logic model under the support of a fine-tuning optimized large language model based on address matching APIs of online location service platforms such as Google, baidu and Tencentrated map, automatically accessing the address matching APIs of the location service platform, and obtaining spatial longitude and latitude coordinates of the physical asset; s33, classifying the shapes of the assets in the space positions according to the points, the lines, the planes and the derivative geometric types thereof according to the asset types and the description information in the physical asset logic model; s34, recording information such as spatial longitude and latitude, morphological category and the like of the physical asset, associating the physical asset with a business unit to which the physical asset belongs, and adding the business unit to an enterprise physical asset logic model as a new record field for storage and warehousing.
The step S4 of the deep neural network method for locating and drawing the assets of the enterprise organization value chain comprises the following steps:
s41, extracting the space coordinates and the geometric type information of the business unit and the physical asset generated in the S3 by using a large language model, generating a space boundary outsourcing moment of the physical asset, forming a buffer zone polygon of the physical asset outsourcing moment, and outputting the buffer zone polygon into a space data format, such as ESRI shape, geojson and other formats; s42, selecting a remote sensing image type with proper resolution according to the buffer zone polygon of the physical asset, and obtaining a remote sensing image of which the range can be covered up to date; s43, adopting a pre-trained deep neural network image information extraction model, carrying out optimization and fine adjustment aiming at downstream tasks such as target detection, semantic segmentation and the like of remote sensing images, and carrying out adjustment and optimization through a manual intervention training model to construct a deep neural network image information extraction model for extracting physical asset image targets of the types such as buildings, roads and the like; s44, extracting the spatial boundary of the physical asset by combining the physical asset spatial buffer area obtained in S41 and the remote sensing image obtained in S42 in a mode of combining target detection and semantic segmentation by using the deep neural network model trained in S43; s45, performing precision evaluation on the physical asset space boundary extracted based on the remote sensing image by adopting a manual auxiliary semi-automatic mode, and outputting a precision evaluation report and space boundary data.
The step S5 of the deep neural network method for locating and drawing the assets of the enterprise organization value chain comprises the following steps:
s51, drawing and integrating business units and physical asset boundary segmentation data based on the enterprise physical asset space boundary extracted in the S4 and combining the remote sensing image, the asset type information and the physical asset form category acquired in the S3 to form space data forms of points, lines, planes and the like; s52, unifying the space information in the business unit, the physical asset boundary segmentation data and the drawing comprehensive data into coordinates, and outputting the coordinates into a ESRI Shapefile Geojson standard space data format; s53, classifying the service unit description data and the physical assets according to the space form, generating metadata description data production flow and version control information, and outputting the metadata description data production flow and version control information to a space data management system.
In summary, the invention provides the method for extracting the space coordinates and boundaries of the business units and the physical assets in the enterprise organization value chain based on the deep neural network model to meet the requirement of the enterprise sustainable information disclosure process on the quantitative assessment of the climate risk of the physical assets, thereby providing the high-precision position information of the physical assets for the influence assessment of the climate risk. The asset positioning and drawing scheme of the enterprise organization value chain is based on a deep neural network, a network model of physical assets in the enterprise value chain is extracted and constructed through a large language model, and automatic address matching is carried out through an online position service platform to obtain space coordinates and geometric types of the physical assets; the remote sensing images of the areas where the business units and the assets are located are selected by utilizing address matching, the space information of the business units and the physical asset entities is extracted based on deep neural network remote sensing image target detection and semantic segmentation, and the positioning information and boundary space data of the assets of the enterprise organization value chain are obtained by drawing and integrating the business units and the physical asset extraction data, so that the high-efficiency and high-precision positioning and drawing of the assets of the large-scale enterprise organization value chain are realized, and accurate and reliable sustainable information is provided for enterprise ESG evaluation and disclosure, credit and bond issuing.
The implementation flow described in detail above includes several implementation details, but other modifications are possible, for example, the logic flows depicted in the figures do not need to follow the particular order shown, or sequential order, in order to achieve desirable results. Other steps or steps may be provided in the flow, and other components may be added or removed from the system. Other embodiments may be within the scope of the following claims.
Claims (6)
1. A deep neural network method for asset positioning and drawing of an enterprise organization value chain comprises the following steps:
step S1, identifying business units and asset entities of an enterprise value chain according to enterprise organization architecture, business processes and upstream and downstream customer description information, and establishing an enterprise value chain description data dictionary;
s2, extracting names, types, addresses and rights information of business units and assets of the enterprise value chain based on the deep neural network language processing model by utilizing the enterprise value chain description data dictionary established in the S1, associating physical assets in the data dictionary to form a full-flow business logic model taking the physical assets as cores in an enterprise organization value chain, and checking and correcting asset entities;
step S3, establishing a large language model based on physical asset logic model data analysis of the deep neural network through fine tuning optimization, carrying out space address matching of physical assets by combining an online position service platform under the support of the large language model, extracting space coordinates of enterprise business units and assets, and determining the geometric types of targets such as the enterprise business units and the assets;
s4, obtaining an image boundary by using the service unit and the space coordinates and the target geometric types of the assets obtained in the S3, and dividing the service unit and the asset entity on a remote sensing image based on a deep neural network model;
and S5, drawing and integrating boundaries of the business units and the assets by utilizing the segmentation result in the S4, outputting positioning data to a database, and carrying out classification mapping and management.
2. The method for deep neural network of asset location and mapping of enterprise organization value chain according to claim 1, wherein in step S1, enterprise organization architecture information is collected, including enterprise organization structure diagram, and business functions of each organization unit are determined; identifying and extracting assets in enterprise organizations, assets at the upstream and downstream of a value chain, including all physical assets (production facilities, production operation sites, storage sites, transport paths) related to links such as transportation, production operation and sales, and the like; and establishing a data description dictionary of the physical assets in the enterprise value chain, wherein dictionary contents surround the physical assets, and providing attribute information including business units and business processes, asset entity types, names, addresses, rights and the like to which the physical assets belong.
3. The method for locating and drawing the assets of the enterprise organization value chain according to claim 1, wherein in the step S2, information extraction is carried out on the assets, names, types, addresses and rights of the assets by using a language processing model based on the deep neural network by using a physical asset description data dictionary in the enterprise value chain established in the step S1; associating physical assets in the data dictionary to form a full-flow business logic model taking the physical assets as cores in the enterprise organization value chain; and manually checking and correcting the data information of the physical asset logic model in the generated enterprise full-service flow.
4. The deep neural network method for asset positioning and drawing of the enterprise organization value chain according to claim 1, wherein in step S3, a domain model adapted to data information analysis of the physical asset logic model in S2 is established by performing prompt word fine tuning optimization on a large language model based on the deep neural network; based on the address matching APIs of online location service platforms such as Google, baidu, tencentrated map and the like, analyzing the semantic address information in the physical asset logic model under the support of a fine-tuning optimized large language model, automatically accessing the address matching APIs of the location service platform, and obtaining the spatial longitude and latitude coordinates of the physical asset; classifying the forms of the assets in the space positions according to the points, the lines, the planes and the derivative geometric types thereof according to the asset types and the description information in the physical asset logic model; and recording the information such as the spatial longitude and latitude, the morphological category and the like of the physical asset, associating the physical asset with the business unit to which the physical asset belongs, and adding the business unit to the enterprise physical asset logic model as a new record field for storage and warehousing.
5. The method for deep neural network of asset location and drawing of enterprise organization value chain according to claim 1, wherein in step S4, the business unit generated in S3, the space coordinates and geometry type information of the physical asset are extracted by using a large language model, the space boundary outsourcing moment of the physical asset is generated, the buffer zone polygon of the physical asset outsourcing moment is formed and output as space data format, such as ESRI shape, geojson and other formats; selecting a remote sensing image type with proper resolution according to the buffer zone polygon of the physical asset, and obtaining a remote sensing image of which the range can be covered up to date; adopting a pre-trained deep neural network image information extraction model, carrying out optimization and fine adjustment aiming at downstream tasks such as target detection, semantic segmentation and the like of remote sensing images, carrying out adjustment and optimization through a manual intervention training model, and constructing a deep neural network image information extraction model for extracting physical asset image targets such as buildings, roads and the like; the training deep neural network model is utilized, the obtained physical asset space buffer area and the remote sensing image are combined, and the space boundary of the physical asset is extracted in a mode of combining target detection and semantic segmentation; and performing precision evaluation on the physical asset space boundary extracted based on the remote sensing image by adopting a manual auxiliary semi-automatic mode, and outputting a precision evaluation report and space boundary data.
6. The method for deep neural network of asset location and mapping of enterprise organization value chain according to claim 1, wherein in step S5, based on the spatial boundary of the enterprise physical asset extracted in S4, and based on the remote sensing image, the asset type information and the physical asset form category obtained in S3, mapping and integrating service units and physical asset boundary segmentation data to form spatial data forms of point, line, surface and the like; the space information unified coordinates in the business unit, the physical asset boundary segmentation data and the drawing comprehensive data are output into a ESRI Shapefile Geojson standard space data format; classifying the service unit description data and the physical assets according to the space form, generating metadata description data production flow and version control information, and outputting the metadata description data production flow and version control information to a space data management system.
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| US20220343433A1 (en) * | 2020-12-10 | 2022-10-27 | The Dun And Bradstreet Corporation | System and method that rank businesses in environmental, social and governance (esg) |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110472884A (en) * | 2019-08-20 | 2019-11-19 | 深圳前海微众银行股份有限公司 | ESG index monitoring method, device, terminal device and storage medium |
| CN111798151A (en) * | 2020-07-10 | 2020-10-20 | 深圳前海微众银行股份有限公司 | Enterprise fraud risk assessment method, device, equipment and readable storage medium |
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