Meteorological data multi-model fusion value evaluation system and method based on block chain

Abstract

The invention belongs to the technical field of meteorological data value evaluation, and discloses a block chain-based meteorological data multi-model fusion value evaluation system and method, and the system comprises an infrastructure layer, a data processing layer, a block chain layer, a multi-model fusion calculation layer and an application layer. The value evaluation method comprises the following steps: S1, collecting and processing meteorological data; s2, data uplink and right confirmation are carried out; s3, multi-model fusion value evaluation; and S4, triggering the smart contract. The block chain technology is introduced into the meteorological data transaction to solve the transaction data caching, copying and retaining problems of the data asset transaction platform, the privacy and security of the data are protected, the rights and interests of the data asset traders are prevented from being infringed and occupied by the data asset transaction platform, the mechanisms such as ownership authentication and data secrecy are realized, and the data transaction efficiency is improved. And data right confirmation traceability is embodied.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological data value evaluation, and in particular to a meteorological data multi-model fusion value evaluation system and method based on blockchain. Background Art

[0002] Data is a key element of the contemporary digital economy and also the core production factor in the new era. Among them, meteorological data can be used to prepare for extreme weather events in advance, guide agricultural production, assist urban planning, and also support energy dispatching, insurance pricing, and environmental protection policy formulation. Therefore, accurate, real-time, and extensive meteorological data has become an indispensable information resource in modern society.

[0003] Currently, meteorological data has the following characteristics: strong spatio-temporality and high timeliness requirements. Meteorological data has both time and space dimensions, and the update frequencies of different types of data vary, but most are composed of minute-level data; large data volume and high diversity. The sources of meteorological data are diverse, such as surface meteorological stations (including national meteorological stations, regional meteorological stations, traffic meteorological stations, marine meteorological stations, etc.), satellites, radars, etc., and the data formats are also diverse, such as basic data stream format, numerical forecast format, satellite cloud image format, warning signal and forecast product text format, etc.; coexistence of public and commercial attributes. Most meteorological basic data are freely open and shared by government agencies, but customized and professional service product data need to be traded to achieve large-scale and market-oriented applications.

[0004] With the rapid development of information technology, the value of data has been increasingly emphasized. Among them, the value of meteorological data is also conforming to the development of the market. However, the traditional meteorological data trading method has the following problems: low security of meteorological data and easy to be tampered with; it is difficult to guarantee the integrity and credibility of meteorological data; the boundary between the right of meteorological data and copyright is unclear; lack of a scientific neutral trading system, the pricing basis of meteorological data is insufficient and lacks a unified standard specification, which is easy to cause disputes.

[0005] As a decentralized distributed database technology, the core of blockchain technology lies in the data storage method, namely the distributed ledger. In the distributed ledger, each node has a complete ledger data, and the nodes ensure the consistency and security of the data through a consensus mechanism. This decentralized data storage method can not only improve the reliability and security of data, but also reduce the costs of data storage and maintenance. Based on this, the present invention proposes a blockchain-based multi-model fusion value evaluation system and method for meteorological data, introducing blockchain technology in meteorological data transactions to solve the problems of caching, copying, and retaining transaction data on the data asset trading platform, protecting the privacy and security of data, ensuring that the rights and interests of data asset traders are not encroached by the data asset trading platform, realizing mechanisms such as ownership authentication and data confidentiality, and reflecting the traceability of data rights confirmation. Summary of the Invention

[0006] The present invention aims to provide a blockchain-based multi-model fusion value evaluation system and method for meteorological data to solve the problems in the background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A blockchain-based multi-model fusion value evaluation system for meteorological data includes an infrastructure layer, a data processing layer, a blockchain layer, a multi-model fusion calculation layer, and an application layer; The infrastructure layer is used for the collection of meteorological data; The data processing layer optimizes the meteorological data collected by the infrastructure layer; The blockchain layer performs data uploading, smart contract, and rights confirmation verification on the meteorological data optimized by the data processing layer; The multi-model fusion calculation layer takes the data stored in the data processing layer as input, outputs the data evaluation value result through multi-model fusion calculation, and uploads and stores it on the chain; The application layer provides a reference basis for the data value of listing transactions and provides traceability verification in the whole process of production and processing of meteorological data products through value evaluation, listing transactions, and traceability verification.

[0008] Further, the infrastructure layer includes one or more of meteorological observation stations, radar stations, satellites, computing servers, storage servers, and network security devices.

[0009] Further, the optimization processing of the meteorological data by the data processing layer includes one or more of multi-source data collection, data cleaning, data processing, data standardization, data distributed storage, and data transmission encryption.

[0010] Furthermore, the methods of the blockchain layer for data uploading, smart contracts, and confirmation verification are as follows: First, perform an operation to upload the data to the blockchain; then, combine the signature of the data provider and the timestamp to generate a unique hash value and then perform distributed storage to ensure the security and credibility of the data and achieve the function of confirmation verification; finally, conduct transactions through the transaction logic set by the smart contract.

[0011] Furthermore, the multi-model fusion calculation content of the multi-model fusion calculation layer includes cost model calculation and dynamic weight model calculation; among them, the cost model includes direct cost, indirect cost, historical cost, and data life cycle cost.

[0012] A multi-model fusion value evaluation method for meteorological data based on blockchain includes the following steps: S1. Meteorological data collection and processing: Collect meteorological basic IOT facility datasets, meteorological basic IT facility datasets, meteorological high-value product transaction datasets, and meteorological basic energy consumption datasets; and perform two cleaning operations on these data, namely removing outliers and filling missing values, to ensure the reliability, accuracy, and authority of the data; after cleaning, perform normalization and standardization processing on these data. S2. Data uploading and confirmation: Package the hash value, timestamp, and metadata of the data processed in step S1 into a meteorological block to generate a unique data fingerprint, and use a cryptographic algorithm to connect the data blocks into a chain data structure in chronological order, so as to ensure the integrity and authenticity of the meteorological dataset and improve the anti-tampering, anti-counterfeiting, and confirmable traceability capabilities of the blockchain system; and based on an efficient and stable networking protocol, build a meteorological data blockchain distributed node link network to quickly, timely, and accurately discover neighbor nodes, realize the privacy protection of the meteorological dataset on the blockchain, and ensure that the original text of the meteorological dataset is not illegally stolen and leaked during transmission and storage. S3. Multi-model fusion value evaluation: Evaluate the value of meteorological data by combining the cost model and the dynamic weight model, and compare the evaluation results with the real-time price in the blockchain trading system. Then, evaluate the effectiveness of this value evaluation method based on the comparison results. When the evaluation effectiveness is insufficient, return to the process to optimize the dynamic parameters until the evaluation effectiveness passes, and then output the final value evaluation data value. S4. Smart contract trigger: The smart contract is a custom logic code stored on the blockchain and described in computer language. It solidifies the established trading logic of meteorological high-value product data in the form of code in the blockchain system to reduce the possible deviation of the execution result of the established trading logic of meteorological high-value product data caused by human interference. The blockchain system triggers the smart contract by executing the corresponding preset logic according to the contract transaction affairs, realizing traceable and irreversible operations on the ledger data, so as to improve the fairness, impartiality and credibility of the execution of the trading logic of meteorological high-value product data.

[0013] Further, in S1, the meteorological basic IOT facility dataset includes one or more of the asset investment data, operation and maintenance cost data, and equipment operation years data of multi-source IOT devices such as national meteorological basic station observation equipment, regional meteorological observation station equipment, traffic meteorological observation station equipment, ocean buoy stations, meteorological radars, and satellites.

[0014] Further, in S1, the meteorological basic IT facility dataset includes one or more of the IT-related asset investment data, business support and maintenance cost data, equipment operation years data, business system physical resource operation data, business system virtual resource operation data, and network bandwidth traffic data of computing and storage devices, network switching devices, and data center basic environment support devices.

[0015] Further, in S1, the meteorological high-value product trading dataset collects the meteorological high-value product data in the catalog based on various high-value product service catalogs in the meteorological field; specifically includes one or more of the 1KM resolution product data of multi-source fusion real-time analysis in the Chinese region, the hourly product data of Chinese intelligent grid real-time analysis, and the real-time product data of Chinese wind profiler radar.

[0016] Further, in S1, the meteorological basic energy consumption dataset is based on the energy consumption data of the equipment in daily operation, including water fees and electricity fees.

[0017] Further, in S3, the cost model includes direct cost A1, indirect cost A2, historical cost A3, and data life cycle cost A4; Among them, the cost directly related to the hardware support for the production of meteorological data products or the provision of services is included in the calculation of the direct cost A1 algorithm, including the procurement or depreciation cost data of meteorological station equipment, the meteorological network bandwidth cost data, and the capital investment data related to server resources; The calculation method of the meteorological network bandwidth cost data is: ; In the formula, B 1 is the single GB data network cost; B11 is the annual total traffic of the import and export network data of the meteorological blockchain platform cluster, in GB; B 12 is the annual total traffic of the import and export bandwidth of the meteorological data center, in GB; B 13 is the annual service fee for the network bandwidth of the meteorological data center; The server resource cost is divided into two cases. One is that the meteorological blockchain platform consists of a physical server cluster, and its calculation method is: ; In the formula, B 2 is the physical resource cost per GB of data service, B 21 is the total net value of the physical service equipment, B 22 is the total amount of meteorological data products, in GB; Considering that server equipment belongs to high-iteration equipment in the field of computing technology, the actual value will decline faster in the first two years. Therefore B 21 the accelerated depreciation method is used for calculation: ; In the formula, N 1 is the value of the equipment procurement assets, N 2 is the salvage rate, N 3 is the remaining service life in years, N 4 is the sum of the years; The second is that the blockchain platform consists of a virtual server cluster, and its calculation method is: ; In the formula, B 3 is the virtual resource cost per GB of data service, B 31 is the total amount of virtual server resources of the platform, B 32 is the total amount of virtual server resources of the meteorological data center, B 21 and B 22 are the total net value of the above physical service equipment and the total amount of meteorological data products, in GB; The direct cost A 1 is: or ; Costs that cannot be directly attributed to a single product but need to be allocated to meteorological data products are included in the calculation of indirect cost A2 algorithm, including water and electricity fees, equipment maintenance and repair fees, and technical staff service fees; the calculation method is as follows: ; In the formula, C 1 is the annual water cost per GB, C 11 is the annual water consumption of the meteorological data center, C 12 is the annual water fee of the building where the meteorological data center is located, C 13 is the total annual water consumption of the building where the meteorological data center is located, C 14 is the total amount of data in the meteorological data center, in GB; ; C 2 is the annual electricity cost per GB, C 21 is the annual electricity consumption of the meteorological data center, C 22 is the annual electricity fee of the building where the meteorological data center is located, C 23 is the total annual electricity consumption of the building where the meteorological data center is located; The indirect cost A 2 is expressed as: ; In the formula, C 3 represents the annual maintenance cost of per GB meteorological product data and related business system equipment; C 4 represents the annual technical staff service cost of per GB meteorological product data and related business system; Include the capital expenditures and historical data maintenance-related costs in the past three years into the historical cost A3 algorithm, and calculate them in a weighted manner. Different weights are assigned to the historical costs according to the time distance. Set the weight of the cost in the most recent half year to 60%, the weight of the cost in the most recent one year to 20%, the weight of the cost in the most recent two years to 15%, and the weight of the cost in the most recent three years to 5%. The calculation method is as follows: ; In the formula, A 3 represents the historical cost of per GB meteorological product data and related business system; represents the weight value defined according to time, The historical cost of single-GB meteorological product data and related business systems for the corresponding period; Include the development costs of meteorological data product-related models, the development of API interfaces for meteorological business systems, and the cost of module function upgrades in the A4 algorithm for data life cycle costs. The calculation method is as follows: ; In the formula, A 4 Is the data life cycle cost of single-GB meteorological products, E 1 Is the total data life cycle cost of meteorological products, E 2 Is the total annual data service volume of meteorological data products, GB / time.

[0018] Furthermore, in S3, the method of the dynamic weight model automatically adjusting the weights of various factors according to real-time data and environmental changes; by introducing a time decay factor, adjusting the weights according to the change speed of market factors, making the value evaluation and listing pricing of meteorological high-value product data more realistic during the listing and trading process. The calculation method is as follows: ; In the formula, Is the market dynamic factor, including supply-demand ratio, competitor price, and policy index; Is the dynamic weight function, Is the calculation of the market factor volatility by the sliding window, Is the time decay adjustment, indicating that factors with large recent fluctuations will obtain higher weights, Is the standardized market factor; , And The calculation method is as follows: .

[0019] Furthermore, in S3, the calculation method of meteorological data fusion value evaluation is as follows: ; In the formula, Is the coefficient determined by verifying the goodness of fit through R ² and residual analysis, Is the market sensitivity coefficient, which needs to consider the time series characteristics of market data, model objectives, and evaluation indicators, and determine the optimal value by balancing sensitivity and stability; and by training multiple different types of models or algorithms, assigning a weight to each model or algorithm; fusing the outputs of multiple models or algorithms, and representing the importance of each model or algorithm in the entire system through weights, and dynamically adjusting the weights according to the performance and accuracy indicators of the model or algorithm.

[0020] The beneficial effects of the technical solution are: 1. The blockchain-based meteorological data multi-model fusion value assessment system provided by the present invention comprehensively collects relevant information data in the entire process of meteorological product data through various equipment such as meteorological observation stations, radar stations, satellites, etc. at the infrastructure layer, and provides a sufficient basic value element data set for the value assessment of meteorological product data; The data processing layer is used to optimize the collected meteorological data by performing multi-source data collection, data cleaning, data processing, data standardization, etc., which improves the quality and availability of the data, and enhances the security and privacy protection of the data through data distribution storage and data transmission encryption; The blockchain layer hashes the processed key data information and uploads it to the chain, and generates a unique hash value based on the data provider's signature and timestamp for distributed storage, which solves the contradiction between the large volume of meteorological data and the high cost of blockchain storage, while improving the security, transparency and credibility of the data. The multi-model fusion calculation layer adopts cost model calculation and dynamic weight model calculation, which comprehensively considers the basic cost required for meteorological data products in the production process and the impact of market dynamic factors on the value of meteorological data products.

[0021] In summary, the blockchain-based meteorological data multi-model fusion value assessment system provided by the present invention achieves the improvement of the accuracy, security and credibility of the value assessment of meteorological data products through comprehensive data collection, efficient data processing and storage optimization, accurate value assessment and dynamic adjustment, innovative transaction and copyright protection mechanisms, and comprehensive application support and services, and provides strong technical support for the circulation and transaction of meteorological data products.

[0022] 2. The blockchain-based meteorological data multi-model fusion value assessment method provided by the present invention cleans, normalizes and standardizes the collected meteorological data to improve the reliability and accuracy of the data; constructs a distributed node link network of the meteorological data blockchain to achieve privacy protection of the meteorological data set on the blockchain, ensuring that the original meteorological data set is not illegally stolen or leaked during transmission and storage.

[0023] By combining the cost model and the dynamic weight model to evaluate the value of meteorological data, and considering the impact of market dynamic factors on the value of meteorological data products, the weights are updated in real time to make the value assessment results more robust and scientific; using the hash value, timestamp, metadata encapsulation and other technical means of blockchain technology, a unique data fingerprint is generated to ensure the integrity and authenticity of the meteorological data set; through cryptographic algorithms, data blocks are connected into a chain data structure in chronological order, improving the blockchain system's anti-tampering, anti-counterfeiting and traceability capabilities.

[0024] Through smart contract technology, the established trading logic of high-value meteorological product data is solidified in the form of code in the blockchain system, reducing the deviation of the execution result of the trading logic caused by human interference.

[0025] In summary, the method for evaluating the value of multi-model fusion of meteorological data based on blockchain provided by the present invention establishes a method for evaluating the value of meteorological data fusion, improves the robustness of value evaluation in the process of listing and trading of high-value meteorological product data, and has more scientific pricing. In the current situation where there is no substantial application of blockchain technology and the trading price is unclear in the field of meteorological data trading, it has very important reference and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the architecture diagram of the system for evaluating the value of multi-model fusion of meteorological data based on blockchain of the present invention; Figure 2 is the flowchart of the method for evaluating the value of multi-model fusion of meteorological data based on blockchain of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The present invention will be further described in detail below with reference to the accompanying drawings: Embodiment 1: As Figure 1 shown, the system for evaluating the value of multi-model fusion of meteorological data based on blockchain includes an infrastructure layer, a data processing layer, a blockchain layer, a multi-model fusion calculation layer, and an application layer; Among them, the infrastructure layer includes meteorological infrastructure such as meteorological observation stations, radar stations, satellites, computing servers, storage servers, and network security devices, which are used to collect relevant data in the production process of meteorological data products; The data processing layer includes multi-source data collection (provided by the devices in the above infrastructure layer), data cleaning, data processing, data standardization, data distributed storage, and data transmission encryption, which are used to optimize the relevant data in the infrastructure layer; The blockchain layer includes data uploading to the chain, smart contracts, and rights confirmation and verification, which are used to perform the operation of uploading the data optimized by the data processing layer to the chain, combine information such as the signature of the data provider and the time stamp, generate a unique hash value and then perform distributed storage to ensure the security and credibility of the data, implement the rights confirmation and verification function, and finally conduct transactions through the trading logic set by the smart contract; The multi-model fusion calculation layer includes a direct cost algorithm, an indirect cost algorithm, a historical cost algorithm, a data life cycle cost algorithm, and a dynamic weight algorithm, which use the data stored in the data processing layer as input, and output the data evaluation value result through multi-model fusion calculation and upload it to the chain for storage; The application layer includes value assessment, listing and trading, and traceability verification, which are used to provide a reference basis for the data value of listing and trading, and to provide the traceability verification function of meteorological data products in the whole process of production and processing.

[0028] In this embodiment, relevant information data in the whole process of meteorological product data are collected, providing a sufficient basic value element dataset for the value assessment of meteorological product data; after the original multi-source basic value element dataset is processed, the required key data information is hashed and chained, solving the contradiction between the large volume of meteorological data and the high storage cost of the blockchain, and at the same time improving the security, transparency and credibility of the data.

[0029] Embodiment 2: As Figure 2 shown, a method for evaluating the value of meteorological data using the blockchain-based multi-model fusion value evaluation system for meteorological data in Embodiment 1 includes the following steps: S1. Meteorological data collection and processing: Collect meteorological basic IOT facility datasets, meteorological basic IT facility datasets, meteorological high-value product trading datasets, and meteorological basic energy consumption datasets; for each type of data in the above four datasets, according to the relevant data quality control specifications of different meteorological elements in the meteorological industry, perform data cleaning operations such as removing outliers and filling missing values to ensure the reliability, accuracy, and authority of the data used in this calculation method; and perform normalization and standardization processing on the cleaned data for subsequent algorithm model application and prediction implementation; The data is subjected to two data cleaning operations of removing outliers and filling missing values to ensure the reliability, accuracy, and authority of the data; after cleaning, these data are subjected to normalization and standardization processing; Among them, based on various types of observation instrument equipment in the meteorological field, including but not limited to asset investment data, operation and maintenance cost data, equipment operation years, etc. of multi-source IOT devices such as national meteorological basic station observation equipment, regional meteorological observation station equipment, traffic meteorological observation station equipment, ocean buoys, meteorological radars, satellites, etc., a meteorological basic IOT facility dataset is formed; Based on various computing server equipment in the meteorological data center, including but not limited to IT-related asset investment data, business support and maintenance cost data, equipment operation years data, as well as business system physical resource operation data (computing power and storage, etc.), business system virtual resource operation data (computing power and storage, etc.), network bandwidth traffic, etc. of computing storage equipment, network switching equipment, data center basic environment support equipment, etc., a meteorological basic IT facility dataset is formed; Based on the catalog of various high-value product services in the meteorological field (data that can be traded on the market), collect the meteorological high-value product data in the catalog, including but not limited to product data of 1KM resolution of multi-source integrated real-time analysis in the Chinese region, hourly product data of Chinese intelligent grid real-time analysis, real-time product data of Chinese wind profile radar, etc., to form a dataset for meteorological high-value product transactions; Based on the energy consumption data during the daily operation of the above equipment, including but not limited to relevant data such as water fees and electricity fees, form a dataset for meteorological basic energy consumption; S2. Data on-chain and rights confirmation: Package the data processed in step S1, including hash value, timestamp, and metadata (including but not limited to device SN number, longitude, latitude, etc.), into a meteorological block to generate a unique data fingerprint, and connect the data blocks in chronological order into a chain data structure using cryptographic algorithms to ensure the integrity and authenticity of the meteorological dataset, so as to improve the anti-tampering, anti-counterfeiting, and rights confirmation and traceability capabilities of the blockchain system; and based on an efficient and stable networking protocol, construct a distributed node link network for the meteorological data blockchain to quickly, timely, and accurately discover neighbor nodes, realize the privacy protection of the meteorological dataset on the blockchain, and ensure that the original text of the meteorological dataset is not illegally stolen and leaked during transmission and storage; S3. Multi-model fusion value evaluation: Evaluate the value of meteorological data by combining the cost model and the dynamic weight model, compare the evaluation results with the real-time price in the blockchain trading system, and then evaluate the effectiveness of this value evaluation method based on the comparison results. When the evaluation effectiveness is insufficient, return to the process to optimize the dynamic parameters until the evaluation effectiveness passes, and then output the final value evaluation data value; Among them, the cost model includes direct cost A1, indirect cost A2, historical cost A3, and data life cycle cost A4; incorporate the cost expenses directly related to the hardware support for the production of meteorological data products or the provision of services into the calculation of the direct cost A1 algorithm, including data on the procurement or depreciation expenses of meteorological station equipment, data on meteorological network bandwidth expenses, and data on capital investment related to server resources; The calculation method of meteorological network bandwidth expense data is: ; In the formula, B 1 is the cost of a single GB data network; B 11 is the total annual traffic of the import and export network data of the meteorological blockchain platform cluster, in GB; B 12 is the total annual traffic of the import and export bandwidth of the meteorological data center, in GB; B 13 is the annual service fee for the network bandwidth of the meteorological data center; The server resource cost is divided into two cases. One is that the meteorological blockchain platform consists of a physical server cluster, and its calculation method is: ; In the formula, B 2 is the physical resource cost of single-GB data service, B 21 is the total net value of physical service equipment, B 22 is the total amount of meteorological data products, in GB; Considering that server equipment belongs to high-iteration equipment in the field of computing technology, the actual value will decline faster in the first two years. Therefore B 21 the accelerated depreciation method is used for calculation: ; In the formula, N 1 is the asset value of equipment procurement, N 2 is the residual value rate, N 3 is the remaining service life in years, N 4 is the sum of years; The second is that the blockchain platform consists of a virtual server cluster, and its calculation method is: ; In the formula, B 3 is the virtual resource cost of single-GB data service, B 31 is the total amount of virtual server resources of the platform, B 32 is the total amount of virtual server resources of the meteorological data center, B 21 and B 22 are the total net value of the above physical service equipment and the total amount of meteorological data products, in GB; The direct cost A 1 is: or ; The costs that cannot be directly attributed to a single product but need to be allocated to meteorological data products are included in the indirect cost A2 algorithm for calculation, including water and electricity fees, equipment maintenance fees, and technical personnel service fees; Its calculation method is: ; In the formula, C 1 is the annual water cost per GB,C 11 is the annual water consumption of the meteorological data center, C 12 is the annual water fee of the building where the meteorological data center is located, C 13 is the total annual water consumption of the building where the meteorological data center is located, C 14 is the total amount of meteorological data center data, GB; ; C 2 is the electricity cost per GB, C 21 is the annual electricity consumption of the meteorological data center, C 22 is the annual electricity fee of the building where the meteorological data center is located, C 23 is the total annual electricity consumption of the building where the meteorological data center is located; The indirect cost is obtained A 2 It is expressed as: ; In the formula, C 3 represents the annual maintenance cost of single-GB meteorological product data and related business system equipment; C 4 represents the annual technical personnel service cost of single-GB meteorological product data and related business systems; Incorporate the capital expenditures and historical data maintenance-related costs of the past three years into the historical cost A3 algorithm, and calculate using a weighted method. Different weights are assigned to historical costs according to the time distance. Set the weight of the most recent half-year cost at 60%, the weight of the most recent one-year at 20%, the weight of the most recent two-year at 15%, and the weight of the most recent three-year at 5%. Its calculation method is: ; In the formula, A 3 represents the historical cost of single-GB meteorological product data and related business systems; represents the weight value defined according to time, is the historical cost of single-GB meteorological product data and related business systems for the corresponding period; Incorporate the meteorological data product-related model development costs, meteorological business system API interface development, and module function upgrade costs into the data life cycle cost A4 algorithm. Its calculation method is: ; In the formula, A 4is the life cycle cost of a single GB of meteorological product data, E 1 is the total life cycle cost of meteorological product data, E 2 is the total annual data service volume of meteorological data products, in GB / times; The dynamic weight model is a method for automatically adjusting the weights of various factors according to real-time data and environmental changes; by introducing a time decay factor, the weights are adjusted according to the change speed of market factors (e.g., if the recent market changes are large, the weight is higher; or if certain factors such as supply and demand suddenly change, the weight can increase dynamically), making the value assessment and listing pricing of high-value meteorological product data more realistic during the listing and trading process. Its calculation method is: ; In the formula, is the market dynamic factor, including the supply-demand ratio, competitor price, and policy index; is the dynamic weight function, is the calculation of the market factor volatility by a sliding window, is the time decay adjustment, indicating that factors with large recent fluctuations will obtain higher weights, is the standardized market factor; , and The calculation methods are: ; The calculation method for the value assessment of meteorological data fusion is:

[0030] In the formula, is the coefficient determined by verifying the goodness of fit through R ² and residual analysis, is the market sensitivity coefficient, which needs to be determined as the optimal value by considering the time series characteristics of market data, model objectives, and evaluation indicators, and achieving a balance between sensitivity and stability; and by training multiple different types of models or algorithms, assigning a weight to each model or algorithm; fusing the outputs of multiple models or algorithms, and dynamically adjusting the weights according to the performance, accuracy indicators of the models or algorithms to represent the importance of each model or algorithm in the entire system; S4. Smart contract trigger: A smart contract is custom logic code stored on a blockchain and described in a computer language. It solidifies the established trading logic of high-value meteorological product data in the form of code in the blockchain system to reduce the possible deviation in the execution result of the established trading logic of high-value meteorological product data caused by human interference. The blockchain system triggers the smart contract by executing the corresponding preset logic according to the contract transaction affairs, realizing traceable and irreversible operations on the ledger data, so as to improve the fairness, credibility and reliability of the execution of the trading logic of high-value meteorological product data.

[0031] Based on blockchain technology, the establishment of the meteorological data fusion value evaluation method in this embodiment can improve the robustness of the value evaluation of high-value meteorological product data in the process of listing and trading, and the pricing is more scientific. In the current situation where there is no substantial application of blockchain technology and the trading price is unclear in the field of meteorological data trading, it has very important reference and application value.

[0032] The above are only embodiments of the present invention, and common general technical solutions or characteristics in the solution are not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be subject to the content of its claims, and the specific implementation manners and the like recorded in the specification can be used to interpret the content of the claims.

Claims

1. The multi-model fusion value assessment system of meteorological data based on blockchain is characterized by: It includes infrastructure layer, data processing layer, blockchain layer, multi-model fusion computing layer and application layer; The infrastructure layer is used for collecting meteorological data; The data processing layer optimizes and processes the meteorological data collected by the infrastructure layer; The blockchain layer uploads the meteorological data optimized and processed by the data processing layer to the chain, performs smart contracts and rights verification; The multi-model fusion calculation layer will use the data stored in the data processing layer as input, output the data evaluation value results after multi-model fusion calculation and store them on the chain; The application layer is used to provide a reference for the data value of listed transactions through value assessment, listed transactions and traceability verification, as well as to provide traceability verification for meteorological data products in the entire production and processing process.

2. The blockchain-based meteorological data multi-model fusion value assessment system according to claim 1 is characterized in that: The infrastructure layer includes one or more of a meteorological observation station, a radar station, a satellite, a computing server, a storage server, and a network security device.

3. The blockchain-based meteorological data multi-model fusion value assessment system according to claim 2 is characterized in that: The data processing layer optimizes the processing of meteorological data by including one or more of multi-source data collection, data cleaning, data processing, data standardization, data distribution storage and data transmission encryption.

4. The blockchain-based meteorological data multi-model fusion value assessment system according to claim 3 is characterized in that: The method of the blockchain layer for data chaining, smart contracts and property rights verification is as follows: first, the data is chained; then, a unique hash value is generated in combination with the signature and timestamp of the data provider, and then distributed storage is performed to ensure data security and reliability and realize property rights verification function; finally, transactions are conducted through the transaction logic set by the smart contract.

5. The blockchain-based meteorological data multi-model fusion value assessment system according to claim 4 is characterized in that: The multi-model fusion calculation content of the multi-model fusion calculation layer includes cost model calculation and dynamic weight model calculation; wherein the cost model includes direct cost, indirect cost, historical cost and data life cycle cost.

6. The method for evaluating the value of meteorological data by the blockchain-based meteorological data multi-model fusion value evaluation system according to claim 5 is characterized in that: The following steps are involved: S1. Meteorological data collection and processing: Collect meteorological basic IOT facility data sets, meteorological basic IT facility data sets, meteorological high-value product transaction data sets, and meteorological basic energy consumption data sets; perform two cleaning operations on these data, namely, remove outliers and fill in missing values, to ensure the reliability, accuracy, and authority of the data; perform normalization and standardization on these data after cleaning; S2. Data on-chain and ownership confirmation: The data processed in step S1 is hashed, timestamped, and metadata are encapsulated into a meteorological block to generate a unique data fingerprint, and the data blocks are connected into a chain data structure in chronological order using a cryptographic algorithm to ensure the integrity and authenticity of the meteorological data set, thereby improving the blockchain system's anti-tampering, anti-counterfeiting, and traceability capabilities; and based on an efficient and stable networking protocol, a distributed node link network of the meteorological data blockchain is constructed to quickly, timely, and accurately discover neighbor nodes, realize privacy protection of the meteorological data set on the blockchain, and ensure that the original meteorological data set is not illegally stolen or leaked during transmission and storage; S3. Multi-model fusion value assessment: Combine the cost model and the dynamic weight model to evaluate the value of meteorological data, and compare the evaluation results with the real-time prices in the blockchain transaction system. The comparison results are then used to evaluate the effectiveness of the value evaluation method. If the evaluation results are insufficient, return to the process to adjust the dynamic parameters until the evaluation results are passed, and then output the final value evaluation data value; S4. Smart contract trigger: Smart contracts are custom logic codes described in computer language and stored on the blockchain. They solidify the established transaction logic of high-value meteorological product data in the blockchain system in the form of code to reduce the possible deviation in the execution results of the established transaction logic of high-value meteorological product data due to human interference. The blockchain system triggers smart contracts by executing corresponding preset logic based on contractual transactions, thereby achieving traceable and irreversible operations on ledger data, thereby improving the fairness, justice and credibility of the execution of transaction logic for high-value meteorological product data.

7. The method for evaluating the value of meteorological data by the blockchain-based meteorological data multi-model fusion value evaluation system according to claim 6 is characterized in that: In S1, the meteorological basic IOT facility data set includes one or more of the asset investment data, operation and maintenance cost data, and equipment operating life data of multi-source IOT equipment such as national meteorological basic station observation equipment, regional meteorological observation station equipment, transportation meteorological observation station equipment, ocean buoy stations, meteorological radars, and satellites.

8. The method for evaluating the value of meteorological data by the blockchain-based meteorological data multi-model fusion value evaluation system according to claim 6 is characterized in that: In S1, the meteorological basic IT facilities data set includes one or more of IT-related asset investment data such as computing storage equipment, network switching equipment, and data center basic environment support equipment, business support maintenance cost data, equipment operating years data, business system physical resource operation data, business system virtual resource operation data, and network bandwidth flow data.

9. The method for evaluating the value of meteorological data by the blockchain-based meteorological data multi-model fusion value evaluation system according to claim 6 is characterized in that: In S1, the meteorological high-value product transaction data set is based on various high-value product service catalogs in the meteorological field, and collects meteorological high-value product data in the catalog; specifically, it includes one or more of China's regional multi-source fusion real-time analysis 1KM resolution product data, China's intelligent grid real-time analysis hourly product data, and China's wind profiler radar real-time product data.

10. The method for evaluating the value of meteorological data by the blockchain-based meteorological data multi-model fusion value evaluation system according to claim 6 is characterized in that: In S1, the meteorological basic energy consumption data set is based on the energy consumption data of the equipment in daily operation, including water charges and electricity charges.

11. The method for evaluating the value of meteorological data by the blockchain-based meteorological data multi-model fusion value evaluation system according to claim 6 is characterized in that: In S3, the cost model includes direct cost A1, indirect cost A2, historical cost A3, and data life cycle cost A4; Among them, the costs directly related to the production of meteorological data products or the hardware support of service provision are included in the direct cost A1 algorithm, including meteorological site equipment procurement or depreciation cost data, meteorological network bandwidth cost data, and server resource-related capital investment data; The calculation method of the meteorological network bandwidth fee data is: ； In the formula, B 1 is the network cost per GB of data; B 11 is the total annual traffic of import and export network data of the meteorological blockchain platform cluster, GB; B 12 is the total annual import and export bandwidth flow of the meteorological data center, GB; B 13 Annual service fee for the weather data center network bandwidth; The server resource cost is divided into two cases. One is that the meteorological blockchain platform is composed of a physical server cluster, and its calculation method is: ； In the formula, B 2 is the physical resource cost of a single GB data service. B 21 is the total net value of the physical service equipment, B 22 is the total amount of meteorological data products, GB; considering that server equipment is a high-iteration equipment in the field of computing technology, the actual value in the first two years will decline faster, so B 21 Calculate using accelerated depreciation method: ； In the formula, N 1 is the equipment purchase asset value, N 2 is the residual value rate, N 3 is the remaining useful life, N 4 is the sum of the years; Second, the blockchain platform is composed of a virtual server cluster, and its calculation method is: ； In the formula, B 3 is the virtual resource cost of a single GB data service. B 31 is the total amount of platform virtual server resources, B 32 is the total amount of virtual server resources in the meteorological data center, B 21 and B 22 The net value of the above-mentioned physical service equipment and the total volume of meteorological data products, GB; Get direct costs A 1 is: or ; The costs that cannot be directly attributed to a single product but need to be allocated to meteorological data products are included in the indirect cost A2 algorithm for calculation, including water and electricity costs, equipment repair and maintenance costs, and technician service fees; the calculation method is: ； In the formula, C 1 is the annual water cost per GB, C 11 is the annual water consumption of the Meteorological Data Center, C 12 The annual water fee for the building where the meteorological data center is located. C 13 is the total annual water consumption of the building where the meteorological data center is located, C 14 is the total amount of data in the meteorological data center, GB; ； C 2 is the electricity cost per GB, C 21 is the annual electricity consumption of the meteorological data center, C 22 The annual electricity bill for the building where the meteorological data center is located. C 23 The total annual electricity consumption of the building where the meteorological data center is located; Get indirect costs A 2 is expressed as: ； In the formula, C 3 represents the annual maintenance cost of a single GB of meteorological product data and related business system equipment; C 4 represents the annual technical personnel service fee for a single GB of meteorological product data and related business systems; The capital expenditures and historical data maintenance-related expenses of the past three years are included in the historical cost A3 algorithm, and are calculated in a weighted manner. Different weights are assigned to historical costs according to the time distance. The weight of the cost in the last six months is set at 60%, the weight of the last year is set at 20%, the weight of the last two years is set at 15%, and the weight of the last three years is set at 5%. The calculation method is: ； In the formula, A 3 represents the historical cost of a single GB of meteorological product data and related business systems; Indicates the weight value defined according to time, The historical cost of a single GB of meteorological product data and related business systems for the corresponding period; The cost of developing models related to meteorological data products, the cost of API interface development of meteorological business systems, and the cost of upgrading module functions are included in the A4 algorithm of data life cycle cost, and the calculation method is as follows: ； In the formula, A 4 is the life cycle cost of a single GB meteorological product data. E 1 is the total life cycle cost of meteorological product data, E 2 is the total annual data service volume of meteorological data products, GB / time.

12. The method for evaluating the value of meteorological data by the blockchain-based meteorological data multi-model fusion value evaluation system according to claim 11 is characterized in that: In S3, the dynamic weight model automatically adjusts the weight of each factor according to real-time data and environmental changes; by introducing the time decay factor method, the weight is adjusted according to the speed of change of market factors, making the value assessment and listing pricing of high-value meteorological product data more realistic during the listing and trading process. The calculation method is: ； In the formula, Market dynamic factors, including supply-demand ratio, competitor prices, and policy index; is the dynamic weight function, Calculate the market factor volatility for the sliding window, Adjusted for time decay, which means factors with large recent fluctuations will receive higher weights. is the standardized market factor; , and The calculation method is: 。 13. The method for evaluating the value of meteorological data by the blockchain-based meteorological data multi-model fusion value evaluation system according to claim 12 is characterized in that: In S3, the calculation method for meteorological data fusion value assessment is: ； In the formula, To pass R ² and residual analysis to verify the coefficients determined by the goodness of fit, The market sensitivity coefficient is determined by combining the time series characteristics of market data, model objectives and evaluation indicators, and considering the balance between sensitivity and stability. A weight is assigned to each model or algorithm by training a variety of different types of models or algorithms. The outputs of multiple models or algorithms are integrated with each other, and the importance of each model or algorithm in the entire system is represented by weights. The weights are adjusted dynamically according to the performance and accuracy indicators of the models or algorithms.

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