WO2023236478A1 - 基于区块链的企业碳排能耗数据管理运营系统及方法 - Google Patents

基于区块链的企业碳排能耗数据管理运营系统及方法 Download PDF

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WO2023236478A1
WO2023236478A1 PCT/CN2022/138374 CN2022138374W WO2023236478A1 WO 2023236478 A1 WO2023236478 A1 WO 2023236478A1 CN 2022138374 W CN2022138374 W CN 2022138374W WO 2023236478 A1 WO2023236478 A1 WO 2023236478A1
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data
level
enterprise
regional
indicators
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French (fr)
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邹宏亮
王雪燕
左小草
叶烨
李登雕
蒋永亮
方燕翔
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台州宏创电力集团有限公司科技分公司
国网浙江省电力有限公司台州供电公司
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Priority to US18/242,579 priority Critical patent/US20230410127A1/en
Publication of WO2023236478A1 publication Critical patent/WO2023236478A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/018—Certifying business or products
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
    • G06F16/2365—Ensuring data consistency and integrity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2477—Temporal data queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/26—Visual data mining; Browsing structured data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Definitions

  • the invention belongs to data processing technology, and specifically refers to a blockchain-based enterprise carbon emission and energy consumption data management and operation system and method.
  • Carbon emissions are an important type of pollution in environmental pollution. Carbon emissions bring about environmental problems such as the greenhouse effect.
  • the present invention provides a blockchain-based enterprise carbon emission and energy consumption data management and operation method.
  • Blockchain-based enterprise carbon emission and energy consumption data management and operation methods include:
  • the enterprise port collects operational data and sends the operational data to the data central control.
  • the operational data includes the enterprise's water data, electricity consumption data, coal consumption data, gas consumption data, heat consumption data, output value data and equipment data;
  • S2 Data central control substitutes operational data into the intelligent production accounting model to calculate enterprise-level production indicators, accumulates all enterprise-level production indicators in the region to obtain regional-level production indicators, and substitutes regional-level production indicators into the prediction algorithm to calculate regional-level forecasts.
  • Data among which, production indicators include carbon emission indicators, energy consumption indicators and economic indicators, and region refers to the collection of all enterprise ports within a certain geographical range;
  • S3 The data central control is pre-set with regional-level target data. It determines whether the regional-level forecast data exceeds the regional-level target data. If so, the difference between the regional-level production data and the regional-level forecast data is substituted into the planning algorithm to calculate the regional level. Energy consumption reduction tasks and regional carbon emission reduction tasks;
  • S4 Data central control calculates the enterprise-level energy consumption reduction potential value and enterprise-level carbon emission reduction potential value based on enterprise-level production indicators and regional-level production indicators. Based on the enterprise-level energy consumption reduction potential value and enterprise-level carbon emission reduction potential value, the Regional-level energy consumption reduction tasks and regional-level carbon emission reduction tasks are allocated to obtain enterprise-level assessment indicators. The enterprise-level assessment indicators are sent to the corresponding enterprise ports, and regional-level production indicators, regional-level forecast data, and regional-level energy reduction tasks are , regional-level carbon emission reduction tasks and enterprise-level assessment indicators are sent to the regional-level data display port;
  • the enterprise port visually displays enterprise-level operating data and enterprise-level assessment indicators.
  • the regional-level data display port displays regional-level production indicators, regional-level forecast data, regional-level energy consumption reduction tasks, regional-level carbon emission reduction tasks, and enterprise-level Assessment indicators are visually displayed.
  • the step S2 specifically includes the following steps:
  • S21 Write the production calculation formula into a production calculation formula in the form of smart contract code.
  • the production calculation formula includes a carbon emission calculation formula, an energy consumption calculation formula and an economic calculation formula;
  • S22 Compile the production calculation formula in the form of smart contract code into the smart contract to obtain the smart production accounting model.
  • the smart production accounting model includes signatures, timestamps and file hash values;
  • S23 Substitute operational data into the intelligent production accounting model to calculate enterprise-level production indicators, and upload the enterprise-level production indicators to the blockchain network.
  • the blockchain network includes blockchain nodes, and the blockchain nodes include enterprise ports and data. Central control and regional data display port.
  • the smart contract code uses a Turing-complete programming language.
  • the carbon emission calculation formula is
  • E refers to the total greenhouse gas emissions of the enterprise
  • i refers to the type of fossil fuel
  • NCV i refers to the average low calorific value of the i-th fossil fuel
  • FC i refers to the i-th fossil fuel
  • the net consumption of CC i refers to the carbon content per unit calorific value of the i-th fossil fuel
  • OF i refers to the carbon oxidation rate of the i-th fossil fuel
  • m refers to the type of greenhouse gas
  • ETD m refers to is the leakage amount of the mth greenhouse gas
  • AD electricity refers to the net purchased electricity used by the enterprise
  • EF electricity refers to the annual average power supply emission factor of the regional power grid
  • AD heat refers to the net purchased electricity of the enterprise. The amount of heat used.
  • the step S3 specifically includes the following steps:
  • the regional data display port also visually displays a sliding time progress bar
  • the regional-level data display port can display regional-level production indicators and regional-level forecast data at different times.
  • the following steps are specifically included:
  • the data central control determines whether the regional-level forecast data at a certain time node exceeds the regional-level forecast value at the corresponding time node.
  • it also includes:
  • S6 The data central control determines whether the enterprise's production indicators have enterprise assessment indicators at the same time. If so, it determines whether the enterprise's production indicators are not less than the enterprise assessment indicators at the same time. If so, it determines that the enterprise has received policy rewards.
  • it also includes:
  • the data central control is equipped with a new energy equipment database, which retrieves new energy equipment data in the new energy equipment database based on operational data and production indicators.
  • the new energy equipment database is a collection of several new energy equipment data.
  • New energy equipment The data includes the model, quantity and price of new energy equipment;
  • the energy saving and emission reduction report includes new energy equipment data, new energy equipment investment, new energy Equipment configuration capacity, new energy equipment configuration scale, annual energy consumption value saved per unit investment of new energy equipment, annual carbon emission reduction value saved per unit investment of new energy equipment, energy consumption ratio before and after new energy equipment investment , the carbon emission ratio before and after the investment of new energy equipment, the return ratio of new energy equipment investment, the predicted annual rate of return after the investment of new energy equipment, and the payback period after the investment of new energy equipment;
  • the enterprise port provides a visual display of energy saving and emission reduction reports.
  • the new energy equipment database is provided to the data central control through the supplier port.
  • the outstanding and beneficial technical effects of the present invention are:
  • the enterprise display end comprehensively collects various data during enterprise operations, and then performs unified calculations through the intelligent production accounting model to obtain enterprise-level production indicators, avoiding the cumbersome process of manual calculation and realizing data Automated continuous processing improves the efficiency of data management operations and ensures data quality.
  • the enterprise display terminal, data central control and regional data display port can update data in real time, so that users can obtain the latest and most reliable carbon emissions, energy consumption and other data.
  • enterprise ports, data central control and regional data display ports constitute a blockchain network.
  • Data such as enterprise production indicators are uploaded to the blockchain network, and are stored in each node of the blockchain network.
  • the data can be viewed, avoiding problems such as data tampering and data loss. Therefore, this blockchain-based enterprise carbon emissions and energy consumption data management and operation method has the advantages of high data credibility, high authenticity, full traceability, and transparency. .
  • the intelligent production accounting model is constructed using smart contracts, so that data accounting is in a closed execution state, providing a highly trustful environment, allowing accounting without a third party, and intelligent production
  • the accounting model is coded, and the entire accounting process is automatic and efficient without human intervention, reducing measurement and accounting errors and improving data accuracy.
  • the government side, power grid side, new energy enterprise side, and industrial enterprise side can interact and communicate with each other through the blockchain nodes of the blockchain network, solving the problem of data interaction on each side.
  • Figure 1 is a schematic flow diagram of the steps of the present invention
  • Figure 2 is a schematic diagram of the frame structure of the system of the present invention.
  • a blockchain-based enterprise carbon emission and energy consumption data management and operation method includes:
  • the enterprise port collects operational data and sends the operational data to the data central control.
  • the operational data includes the enterprise's water data, electricity consumption data, coal consumption data, gas consumption data, heat consumption data, output value data and equipment data;
  • the enterprise port is set up in the enterprise.
  • the enterprise port is not only used to collect operational data, but also can be used for the enterprise to retrieve data on the blockchain.
  • Enterprises are generally machinery and equipment manufacturing enterprises, which include metal products manufacturing, general equipment manufacturing, special equipment manufacturing, automobile manufacturing, railway, shipbuilding, aerospace and other transportation equipment manufacturing, and electrical machinery and equipment manufacturing.
  • Operational data also includes corporate information, which includes corporate name, corporate address, and organization code.
  • the enterprise port includes the equipment layer, which can be used to collect the operation status of the enterprise.
  • the operation status includes the operation status in the design, production, processing, assembly and testing of the enterprise's products.
  • the equipment layer includes smart water meters, smart electricity meters, smart gas meters, smart switches, smart gateways, and temperature and humidity sensors.
  • smart water meters can collect an enterprise's water usage data
  • smart gas meters can collect an enterprise's gas usage
  • smart switches and smart gateways can collect an enterprise's electricity usage.
  • the equipment in the equipment data refers to the existing production equipment.
  • the equipment data includes the model, quantity, standard technical parameters and supplier contact information of the company's existing equipment.
  • S2 Data central control substitutes operational data into the intelligent production accounting model to calculate enterprise-level production indicators, accumulates all enterprise-level production indicators in the region to obtain regional-level production indicators, and substitutes regional-level production indicators into the prediction algorithm to calculate regional-level forecasts.
  • production indicators include carbon emission indicators, energy consumption indicators and economic indicators;
  • data central control refers to the device used to manage and operate data.
  • a region refers to the collection of all enterprise ports within a certain geographical range, which is equivalent to the collection of all enterprises within a certain geographical range. Users can pre-set areas in the data center.
  • Production indicators include the company's carbon emission indicators, energy consumption indicators and economic indicators.
  • the carbon emission indicators include the company's total carbon emissions
  • the energy consumption indicators include the company's total energy consumption
  • the economic indicators include the company's total output value.
  • Regional-level production indicators represent the sum of production indicators for all enterprises within a specific geographical area.
  • Regional forecast data refers to the carbon emission indicators, energy consumption indicators and economic indicators that the region is expected to achieve at a certain time in the future.
  • S3 The data central control is pre-set with regional-level target data to determine whether the regional-level forecast data exceeds the regional-level target data. If so, the regional-level energy consumption reduction is planned based on the difference between the regional-level production data and the regional-level forecast data. Tasks and regional carbon emission reduction tasks;
  • the regional target data refers to the carbon emission indicators, energy consumption indicators and economic indicators that the region is expected to achieve at a certain time in the future.
  • a time in the future can be the following year.
  • Users can set regional target data on the data center in advance.
  • regional-level target data can be set according to the national carbon peaking policy. For example, assuming that the regional setting is Beijing, from 2022 to 2025, Beijing's carbon peak policy is to reduce the total carbon dioxide emissions of enterprises by 18% and reduce the total energy consumption of enterprises by 13.5%.
  • the regional-level energy consumption reduction task includes the difference between the energy consumption indicators in regional-level production data and the energy consumption indicators in regional-level forecast data.
  • Regional-level energy consumption reduction tasks include the difference between the carbon emission indicators in regional-level production data and the carbon emission indicators in regional-level forecast data.
  • S4 Data central control calculates the enterprise-level energy consumption reduction potential value and enterprise-level carbon emission reduction potential value based on enterprise-level production indicators and regional-level production indicators. Based on the enterprise-level energy consumption reduction potential value and enterprise-level carbon emission reduction potential value, the Regional-level energy consumption reduction tasks and regional-level carbon emission reduction tasks are allocated to obtain enterprise-level assessment indicators. The enterprise-level assessment indicators are sent to the corresponding enterprise ports, and regional-level production indicators, regional-level forecast data, and regional-level energy reduction tasks are , regional-level carbon emission reduction tasks and enterprise-level assessment indicators are sent to the regional-level data display port;
  • the enterprise-level energy reduction potential value is positively correlated with the ratio of the enterprise's energy consumption index and the regional energy consumption index
  • the enterprise-level carbon emission potential value is positively correlated with the ratio of the enterprise's carbon emission index and the regional carbon emission index.
  • the enterprise port visually displays enterprise-level operating data and enterprise-level assessment indicators.
  • the regional-level data display port displays regional-level production indicators, regional-level forecast data, regional-level energy consumption reduction tasks, regional-level carbon emission reduction tasks, and enterprise-level Visual display of assessment indicators;
  • the enterprise port also includes an enterprise visual device.
  • the enterprise visual device may be a display screen, which is used to display enterprise-level operational data, enterprise-level assessment indicators and other data.
  • the regional-level data display port includes a regional-level data visualization device.
  • the regional-level data visualization device can also be a display screen, which is used to display regional-level production indicators, regional-level forecast data, regional-level energy consumption reduction tasks, and regional-level carbon emission reduction. Data such as tasks and enterprise-level assessment indicators.
  • the enterprise port can also visually display energy saving and emission reduction reports.
  • the energy saving and emission reduction report includes the configuration capacity of new energy equipment, the configuration scale of energy saving equipment, the annual energy consumption value saved per unit investment, the carbon reduction value of new energy input per unit, and the value before and after investment. Energy consumption value comparison, carbon emission value comparison before and after investment, investment return ratio, predicted annual yield, payback cycle and other information.
  • the steps of S2 specifically include the following steps:
  • S21 Write the production calculation formula into a production calculation formula in the form of smart contract code.
  • the production calculation formula includes a carbon emission calculation formula, an energy consumption calculation formula and an economic calculation formula;
  • the production calculation formula is made to comply with the code writing requirements and bare metal requirements of the smart contract, so that the subsequent data obtained has universality, confidentiality and unity, and avoids errors caused by code inconsistencies in various blockchain nodes.
  • S22 Compile the production calculation formula in the form of smart contract code into the smart contract to obtain an intelligent production accounting model.
  • the intelligent production accounting model includes signatures, timestamps and file hash values;
  • the above-mentioned intelligent production accounting model is used to conduct unified accounting of carbon emissions, energy consumption and economy in the entire operation of the enterprise, improving the efficiency and accuracy of accounting.
  • S23 Substitute operational data into the intelligent production accounting model to calculate enterprise-level production indicators, and upload the enterprise-level production indicators to the blockchain network.
  • the blockchain network includes blockchain nodes, and the blockchain nodes include enterprise ports and data. Central control and regional data display port.
  • the enterprise-level production indicators are uploaded to the blockchain network, so that the enterprise-level production indicators cannot be tampered with and are permanently saved, thereby improving the data obtained by this blockchain-based enterprise carbon emission and energy consumption data management and operation method. authenticity and credibility.
  • the smart contract code uses a Turing-complete programming language.
  • enterprise-level operational data, enterprise-level assessment indicators, and regional-level data display ports can also use S21 for regional-level production indicators, regional-level forecast data, regional-level energy consumption reduction tasks, regional-level carbon emission reduction tasks, and enterprise-level assessment indicators.
  • -Step 23 to upload the blockchain network.
  • the data central control also builds smart contracts, which specifically include the following steps:
  • P1 Build a smart contract core system in the blockchain network, use a Turing-complete programming language to write smart contract code, substitute the smart contract code into a single hash function to calculate the contract file address, and add the initiator's account address and contract name Substitute a single hash function to calculate the contract installation address, and store the correspondence between the contract installation address and the contract file address in the blockchain network;
  • P2 Update the smart contract core system in the blockchain network, substitute the updated smart contract code into the single hash function to calculate the updated contract file address, and substitute the updated initiator's account address and contract name into the single hash function to calculate Obtain the updated contract installation address, covering the previous correspondence between the contract installation address and the contract file address stored in the blockchain network.
  • E refers to the total greenhouse gas emissions of the enterprise
  • i refers to the type of fossil fuel
  • NCV i refers to the average low calorific value of the i-th fossil fuel
  • FC i refers to the i-th fossil fuel
  • the net consumption of CC i refers to the carbon content per unit calorific value of the i-th fossil fuel
  • OF i refers to the carbon oxidation rate of the i-th fossil fuel
  • m refers to the type of greenhouse gas
  • ETD m refers to is the leakage amount of the mth greenhouse gas
  • AD electricity refers to the net purchased electricity used by the enterprise
  • EF electricity refers to the annual average power supply emission factor of the regional power grid
  • AD heat refers to the net purchased electricity of the enterprise. The amount of heat used.
  • the steps of S3 specifically include the following steps:
  • the regional data display port also visually displays a sliding time progress bar
  • the entire time progress bar can be visualized as a small square on the enterprise visual device. If the regional-level data display port visually displays regional-level production indicators, regional-level forecast data, regional-level energy consumption reduction tasks, and regional-level carbon emission reduction The time progress bar is only visually displayed when task and enterprise-level assessment indicators are specified.
  • the regional-level data display port can display regional-level production indicators and regional-level forecast data at different times.
  • the regional-level data visualization device is a touch display screen.
  • the user touches and slides his hand on the time accuracy bar, which causes the time accuracy bar to slide on the regional data visualization device.
  • the time accuracy bar slides linearly on enterprise visualization devices. For example, if the user slides the time progress bar to the left, the regional-level data display port visually displays regional-level production indicators and regional-level forecast data at an earlier time. If the user slides the time progress bar to the right, the regional-level data display port will visually display regional-level production indicators and regional-level forecast data at a later time.
  • the data central control determines whether the regional-level forecast data at a certain time node exceeds the regional-level forecast value at the corresponding time node.
  • a certain time node and the corresponding time node are the same time.
  • the efficiency of data processing is improved.
  • Regional-level energy consumption reduction tasks and regional-level carbon emission reduction tasks thereby improving the accuracy of judgment.
  • This blockchain-based enterprise carbon emission and energy consumption data management and operation method also includes:
  • S6 The data central control determines whether the enterprise's production indicators have enterprise assessment indicators at the same time. If so, it determines whether the enterprise's production indicators are not less than the enterprise assessment indicators at the same time. If so, it determines that the enterprise has received policy rewards.
  • the data central control determines that the enterprise has received a policy reward, it will send the award information to the enterprise port for visual display, thereby reminding the enterprise to receive the policy reward.
  • the enterprise can apply to the government through the enterprise port to receive the policy reward. Otherwise, it is determined that the enterprise will not receive policy incentives.
  • Policy rewards can include cash payments, tax incentives, honorary titles, etc.
  • the government can review the data through the enterprise port or data central control or regional data display port. If the review is passed, the government will issue policy incentives to the enterprise.
  • This blockchain-based enterprise carbon emission and energy consumption data management and operation method also includes:
  • the data central control is set up with new energy equipment data, and the new energy equipment data in the new energy database is retrieved based on operational data and production indicators.
  • the new energy equipment database is a collection of several new energy equipment data.
  • the new energy equipment data Including the model, quantity and price of new energy equipment;
  • new energy equipment includes photovoltaic equipment, wind turbine equipment and energy storage equipment.
  • An objective function is established between the operating data and production indicators and the new energy equipment database.
  • the data central control substitutes the operating data and production indicators into the objective function to retrieve the corresponding new energy equipment data.
  • the energy saving and emission reduction report includes new energy equipment data, new energy equipment investment, new energy Equipment configuration capacity, new energy equipment configuration scale, annual energy consumption value saved per unit investment of new energy equipment, annual carbon emission reduction value saved per unit investment of new energy equipment, energy consumption ratio before and after new energy equipment investment , the carbon emission ratio before and after the investment of new energy equipment, the return ratio of new energy equipment investment, the predicted annual rate of return after the investment of new energy equipment, and the payback period after the investment of new energy equipment;
  • the investment amount of new energy equipment is the total amount required to purchase new energy equipment, which is calculated by the product of the quantity and price of new energy equipment.
  • the configuration capacity of new energy equipment refers to the total amount of configuration memory of new energy equipment, which is calculated based on the number and model of new energy equipment.
  • the configuration scale of new energy equipment refers to the overall performance of new energy equipment, and is also calculated based on the number and model of new energy equipment.
  • the annual energy consumption value saved per unit investment of new energy equipment refers to the difference between the annual energy consumption value of new energy equipment investment and the annual energy consumption index of the enterprise divided by the number of new energy equipment. First, calculate the annual energy consumption value of the investment in new energy equipment based on the model and quantity of the new energy equipment.
  • the annual carbon emission reduction value saved per unit investment of new energy equipment refers to the difference between the annual carbon emission value of new energy equipment investment and the annual corporate carbon emission indicator divided by the number of new energy equipment.
  • the model and quantity of new energy equipment are calculated to obtain the annual carbon emission value of the new energy equipment investment, and then the difference between the annual carbon emission value of the new energy equipment investment and the annual corporate carbon emission index is finally divided by the new energy equipment The quantity is calculated.
  • the energy consumption ratio before and after the investment of new energy equipment refers to the ratio between the annual energy consumption value of the new energy equipment investment and the annual energy consumption index of the enterprise. According to the annual energy consumption value of the new energy equipment investment and the annual The ratio between the energy consumption indicators of enterprises is calculated.
  • the carbon emission ratio before and after the investment of new energy equipment refers to the ratio between the company's carbon emission indicators before the investment of new energy equipment and the expected company's carbon emission indicators after the investment of new energy equipment.
  • the enterprise port provides a visual display of energy saving and emission reduction reports
  • the enterprise can browse the energy conservation and emission reduction report through the enterprise port, and then determine whether to purchase the corresponding new energy equipment. If so, the company can get in touch with the supplier through the supplier contact information in the new energy equipment data.
  • the enterprise port and the supplier port are interconnected, and the enterprise contacts the supplier using the supplier port through the enterprise port.
  • the new energy equipment database is provided to the data central control through the supplier port, so that the supplier can construct and update the new energy database through the supplier port.
  • the equipment data collected by the enterprise will be updated, and then the equipment data in the data central control will also be updated.
  • the present invention also discloses a blockchain-based enterprise carbon emission and energy consumption data management and operation system, which is used to execute the above-mentioned blockchain-based enterprise carbon emission and energy consumption data management and operation method.
  • the enterprise carbon emissions and energy consumption data management and operation system based on the blockchain includes enterprise ports, data central control, regional data display ports and supplier ports, and communication connections between the enterprise ports, data central control, regional data display ports and supplier ports. Together, the enterprise port, data central control and regional data display port are part of the blockchain network.
  • the enterprise port is used to collect operational data and visually display data.
  • the data includes operational data, production indicators, enterprise-level operational data and enterprise-level assessment indicators.
  • Data central control is used to process and manage data, including production data.
  • the regional-level data display port is used to visually display data, which includes regional-level production indicators, regional-level forecast data, regional-level energy consumption reduction tasks, regional-level carbon emission reduction tasks, and enterprise-level assessment indicators.
  • the supplier port is used to provide the new energy equipment database to the data central control, and the supplier can build and update the new energy equipment database through the supplier port.

Abstract

本发明公开了一种基于区块链的企业碳排能耗数据管理运营系统及方法，其方法包括如下步骤：S1：企业端口采集运营数据；S2：数据中控根据运营数据计算得到区域级生产指标和区域级预测数据；S3：数据中控判断区域级预测数据是否超过区域级目标数据，若是则根据区域级生产数据和区域级预测数据计算得到区域级减能耗任务和区域级减碳排任务；S4：数据中控根据企业级生产指标和区域级生产指标向企业分配企业级考核指标；S5：企业端口和区域级数据展示端口分别对数据进行可视化展示。

Description

基于区块链的企业碳排能耗数据管理运营系统及方法 技术领域
本发明属于数据处理技术，特指一种基于区块链的企业碳排能耗数据管理运营系统及方法。
背景技术
随着经济的发展，社会上越来越重视环境污染和能源消耗的问题。而碳排放是环境污染中的一种重要的污染类型，碳排放带来了温室效应等环境问题。
因此亟需搭建一个连通各侧并且可实现各侧信息数据交互、各侧信息数据分析、各侧信息数据共享以及集成信息数据分析的数据平台。
发明内容
为克服现有技术的不足及存在的问题，本发明提供一种基于区块链的企业碳排能耗数据管理运营方法。
为实现上述目的，本发明采用如下技术方案：
基于区块链的企业碳排能耗数据管理运营方法，包括：
S1：企业端口采集运营数据，将运营数据发送至数据中控，其中，运营数据包括企业的用水数据、用电数据、用煤数据、用气数据、用热数据、产值数据和设备数据；
S2：数据中控将运营数据代入智能生产核算模型计算得到企业级生产指标，将区域内所有的企业级生产指标进行累加获得区域级生产指标，将区域级生产指标代入预测算法计算得到区域级预测数据，其中，生产指标包括碳排放指标、能耗指标和经济指标，区域指的是由一定地域范围内所有企业端口组成的集合；
S3：数据中控上预先设置有区域级目标数据，判断区域级预测数据是否超过区域级目标数据，若是则将区域级生产数据和区域级预测数据之间的差值代入规划算法计算得到区域级减能耗任务和区域级减碳排任务；
S4：数据中控根据企业级生产指标和区域级生产指标计算得到企业级减能耗潜力值和企业级减碳排潜力值，根据企业级减能耗潜力值和企业级减碳排潜力值对区域级减能耗任务和区域级减碳排任务进行分配得到企业级考核指标，将企业级考核指标发送至对应的企业端口，将区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标发送至区域级数据展示端口；
S5：企业端口对企业级运营数据和企业级考核指标进行可视化展示，区域级数据展示端口对区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标进行可视化展示。
作为优选，所述S2的步骤中，具体包括如下步骤：
S21：将生产计算公式编写成智能合约代码形式下的生产计算公式，其中，生产计算公式包括碳排放计算公式、能耗计算公式和经济计算公式；
S22：将智能合约代码形式下的生产计算公式编译在智能合约中得到智能生产核 算模型，智能生产核算模型包括签名、时间戳和文件哈希值；
S23：将运营数据代入智能生产核算模型计算得到企业级生产指标，将企业级生产指标上传至区块链网络，其中，区块链网络包括区块链节点，区块链节点包括企业端口、数据中控和区域级数据展示端口。
作为优选，所述智能合约代码采用的是图灵完备的编程语言。
作为优选，所述碳排放计算公式为
Figure PCTCN2022138374-appb-000001
其中，E 总指的是企业的温室气体排放总量，i指的是化石燃料的种类，NCV i指的是第i种化石燃料的平均低位发热量，FC i指的i是第种化石燃料的净消耗量，CC i指的是第i种化石燃料的单位热值含碳量，OF i指的是第i种化石燃料的碳氧化率，m指的是温室气体的种类，ETD m指的是第m种温室气体的泄漏量，AD 电力指的是企业的净购入使用的电量，EF 电力指的是区域内电网的年平均供电排放因子，AD 热力指的是企业的净购入使用的热量。
作为优选，所述S3的步骤中，具体包括如下步骤：
S31：区域级数据展示端口还可视化展示有可滑动的时间进度条；
S32：若时间进度条在区域级数据展示端口上滑动时，区域级数据展示端口可展示不同时间的区域级生产指标和区域级预测数据。
作为优选，判断区域级预测数据是否超过区域级目标数据时，具体包括如下步骤：
数据中控判断某一时间节点上的区域级预测数据是否超过对应时间节点上的区域级预测值。
作为优选，还包括：
S6：数据中控判断企业生产指标是否存在同一时间的企业考核指标，若是则判断企业生产指标是否不小于同一时间的企业考核指标，若是则判定企业获得政策奖励。
作为优选，还包括：
S7：数据中控设置有新能源设备数据库，根据运营数据和生产指标调取新能源设备数据库中的新能源设备数据，其中，新能源设备数据库为若干新能源设备数据组成的集合，新能源设备数据包括新能源设备的型号、数量和价格；
S8：根据运营数据、生产指标和新能源设备数据生成节能减排报告，将节能减排报告发送至企业端口，其中，节能减排报告包括新能源设备数据、新能源设备的投入额、新能源设备的配置容量、新能源设备的配置规模、新能源设备的单位投入每年度节约的能耗值、新能源设备的单位投入每年度节约的减碳排值、新能源设备投入前后的能耗比、新能源设备投入前后的碳排放比、新能源设备投入回报比、新能源设备投入后的预测年收益率、新能源设备投入后的回本周期；
S9：企业端口对节能减排报告进行可视化展示。
作为优选，所述新能源设备数据库通过供应商端口提供至数据中控。本发明相比现有技术突出且有益的技术效果是：
(1)在本发明中，企业展示端对企业运营时各项数据进行全面采集，再通过的智能生产核算模型进行统一计算，得到企业级生产指标，避免了人工计算的繁琐过程，实现了数 据自动化的持续处理，提高了数据管理运营的效率，也保证了数据的质量。
(2)在本发明中，企业展示端、数据中控和区域级数据展示端口可实时对数据进行更新，以便于用户获得最新和最可靠的碳排放、能耗等数据。
(3)在本发明中，企业端口、数据中控和区域级数据展示端口等构成了区块链网络，企业生产指标等数据被上传至区块链网络，在区块链网络的各个节点均可查看数据，避免了数据被篡改以及避免数据丢失等问题，因此本基于区块链的企业碳排能耗数据管理运营方法具有数据可信度高、真实性高、全程可追溯、透明等优点。
(4)在本发明中，智能生产核算模型采用智能合约进行构建，使得数据核算时处于封闭执行的状态，提供了一种高度信任的环境，允许没有第三方的情况下进行核算，并且智能生产核算模型呈现代码化，整个核算过程自动且高效，无需人为干预，减少了测量和核算误差，也提高了数据的准确性。
(5)在本发明中，政府侧、电网侧、新能源企业侧和工业企业侧等各侧可通过区块链网络的区块链节点进行数据的交互以及联络，解决了各侧的数据交互存在断点的问题，从而达到数据互联的效果，使得数据的浏览、监管、获取和传输变得更加简单和方便，形成了各侧合作相辅相成的局面，提高了工作效率。
附图说明
图1是本发明的步骤流程示意图；
图2是本发明的系统的框架结构示意图；
1-数据中控、2-企业端口、3-区域级数据展示端口、4-供应商端口。
具体实施方式
为了便于本领域技术人员的理解，下面结合附图和具体实施例对本发明作进一步描述。
如图1至图2所示，一种基于区块链的企业碳排能耗数据管理运营方法，包括：
S1：企业端口采集运营数据，将运营数据发送至数据中控，其中，运营数据包括企业的用水数据、用电数据、用煤数据、用气数据、用热数据、产值数据和设备数据；
上述步骤中，企业端口被设置在企业中，企业端口不仅用于采集运营数据，还可用于供企业调取区块链上的数据。企业一般为机械设备制造企业，机械设备制造企业包括金属制品业、通用设备制造业、专用设备制造业、汽车制造业、铁路、船舶、航空航天及其他运输设备制造业和电气机械和器材制造业。运营数据还包括企业信息，企业信息包括企业名称、企业地址、机构代码。
企业端口包括设备层，设备层可用于采集企业的运营情况，运营情况包括企业产品的设计、生产、加工、装配和测试等环节中的运营情况。设备层包括智能水表、智能电表、智能燃气表、智能开关、智能网关、温湿传感器。例如，智能水表可采集企业的用水数据，智能燃气表可采集企业的用气情况，智能开关和智能网关可采集企业的用电情况。设备数据中的设备指的是已有的生产设备，设备数据包括企业已有设备的型号、数量、标准技术参数和供应商联系方式。
S2：数据中控将运营数据代入智能生产核算模型计算得到企业级生产指标，将区 域内所有的企业级生产指标进行累加获得区域级生产指标，将区域级生产指标代入预测算法计算得到区域级预测数据，其中，生产指标包括碳排放指标、能耗指标和经济指标；
上述步骤中，数据中控指的是用于管理和运营数据的装置。区域指的是由一定地域范围内的所有企业端口组成的集合，相当于一定地域范围内的所有企业集合。使用者可在数据中控预先设置区域。生产指标包括企业的碳排放指标、能耗指标和经济指标，碳排放指标包括企业的碳排放总量，能耗指标包括企业的耗能总量，经济指标包括企业的产值总量。区域级生产指标代表的是特定地理区域内的所有企业的生产指标的总和。区域级预测数据指的是未来某一时间时该区域预计达到的碳排放指标、能耗指标和经济指标。
S3：数据中控上预先设置有区域级目标数据，判断区域级预测数据是否超过区域级目标数据，若是则根据区域级生产数据和区域级预测数据之间的差值规划得到区域级减能耗任务和区域级减碳排任务；
上述步骤中，区域级目标数据指的是未来某一时间时该区域期望达到的碳排放指标、能耗指标和经济指标。未来某一时间可以是接下来的一个年度。使用者可预先在数据中控上设置区域级目标数据。具体地，区域级目标数据可根据国家制定的碳达峰政策进行设置。例如，假设区域设置为北京，从2022年到2025年时，北京的碳达峰政策为，企业的二氧化碳排放总量降低18％，企业的能耗总量降低13.5％。区域级减能耗任务包括区域级生产数据中的能耗指标和区域级预测数据中的能耗指标的差值。区域级减能耗任务包括区域级生产数据中的碳排放指标和区域级预测数据中的碳排放指标的差值。
S4：数据中控根据企业级生产指标和区域级生产指标计算得到企业级减能耗潜力值和企业级减碳排潜力值，根据企业级减能耗潜力值和企业级减碳排潜力值对区域级减能耗任务和区域级减碳排任务进行分配得到企业级考核指标，将企业级考核指标发送至对应的企业端口，将区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标发送至区域级数据展示端口；
上述步骤中，企业级减能耗潜力值与企业的能耗指标和区域的能耗指标的比值呈正相关，企业级碳排放潜力值与企业的碳排放指标和区域的碳排放指标的比值呈正相关。若企业级减能耗潜力值越大时，则表示企业需要分配到越大的减能耗任务。若企业级减碳排潜力值越大时，则表示企业需要分配到越大的减碳排任务。企业级考核指标和企业级减能耗潜力值呈正相关。
S5：企业端口对企业级运营数据和企业级考核指标进行可视化展示，区域级数据展示端口对区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标进行可视化展示；
上述步骤中，企业端口还包括企业可视装置，企业可视装置可以是显示屏，其用于显示企业级运营数据和企业级考核指标等数据。区域级数据展示端口包括区域级数据可视装置，区域级数据可视装置也可以是显示屏，用于显示区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标等数据。
所述企业端口还可以可视化展示节能减排报告，节能减排报告包括新能源设备配置容量、节能设备配置规模、单位投入每年度节约的能耗值、单位投入新能源的减碳值、投入前后的能耗值对比、投入前后的碳排值对比、投资回报比、预测年收益率、回本周期等信息。
所述S2的步骤中，具体包括如下步骤：
S21：将生产计算公式编写成智能合约代码形式下的生产计算公式，其中，生产计算公式包括碳排放计算公式、能耗计算公式和经济计算公式；
上述步骤中，使得生产计算公式符合智能合约的代码编写要求和裸机要求，使得后续得到的数据具有通用性、保密性和统一性，避免各个区块链节点的代码不一致带来的误差。
S22：将智能合约代码形式下的生产计算公式编译在智能合约中得到智能生产核算模型，智能生产核算模型包括签名、时间戳和文件哈希值；
上述步骤中，由于企业的运营数据设计众多工艺、众多材料和众多统计对象等情况，以至于在人工核算时存在误差较大的问题。采用上述的智能生产核算模型对企业的整个运营中的碳排、耗能和经济进行统一的核算，提高了核算的效率和精确性。
S23：将运营数据代入智能生产核算模型计算得到企业级生产指标，将企业级生产指标上传至区块链网络，其中，区块链网络包括区块链节点，区块链节点包括企业端口、数据中控和区域级数据展示端口。
上述步骤中，企业级生产指标被上传至区块链网络，使得企业级生产指标不可被篡改以及获得永久保存，从而提高了本基于区块链的企业碳排能耗数据管理运营方法获得的数据的真实性和可信度。
所述智能合约代码采用的是图灵完备的编程语言。
另外，企业级运营数据、企业级考核指标、区域级数据展示端口对区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标也可以采用S21-23的步骤进行区块链网络的上传。
另外，数据中控还构建智能合约，具体包括如下步骤：
P1：在区块链网络中构建智能合约核心系统，使用图灵完备的编程语言编写智能合约代码，将智能合约代码代入单项散列函数计算得到合约文件地址，将发起者的账户地址和合约名称代入单项散列函数计算得到合约安装地址，并在区块链网络存储合约安装地址和合约文件地址的对应关系；
P2：在区块链网络中更新智能合约核心系统，将更新的智能合约代码代入单项散列函数计算得到更新的合约文件地址，将更新的发起者的账户地址和合约名称代入单项散列函数计算得到更新的合约安装地址，覆盖之前在区块链网络存储合约安装地址和合约文件地址的对应关系。
所述碳排放计算公式为
Figure PCTCN2022138374-appb-000002
其中，E 总指的是企业的温室气体排放总量，i指的是化石燃料的种类，NCV i指的是第i种化石燃料的平均低位发热量，FC i指的i是第种化石燃料的净消耗量，CC i指的是第i种化石燃料的单位热值含碳量，OF i指的是第i种化石燃料的碳氧化率，m指的是温室气体的种类，ETD m指的是第m种温室气体的泄漏量，AD 电力指的是企业的净购入使用的电量，EF 电力指的是区域内电网的年平均供电排放因子，AD 热力指的是企业的净购入使用的热量。
所述S3的步骤中，具体包括如下步骤：
S31：区域级数据展示端口还可视化展示有可滑动的时间进度条；
其中，时间进度条的整体可呈企业可视装置上可视化的小方块，若区域级数据展示端口上可视化展示区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标时，才可视化展示时间进度条。
S32：若时间进度条在区域级数据展示端口上滑动时，区域级数据展示端口可展示不同时间的区域级生产指标和区域级预测数据。
具体地，区域级数据可视装置为触摸式显示屏。使用者将手触碰在时间精度条上并滑动，即可带动时间精度条在区域级数据可视装置上滑动。时间精度条可在企业可视装置上直线滑动。例如，若使用者滑动时间进度条向左滑动时，区域级数据展示端口可视化展示较早时刻的区域级生产指标和区域级预测数据。若使用者滑动时间进度条向右滑动时，区域级数据展示端口可视化展示较晚时刻的区域级生产指标和区域级预测数据。
判断区域级预测数据是否超过区域级目标数据时，具体包括如下步骤：
数据中控判断某一时间节点上的区域级预测数据是否超过对应时间节点上的区域级预测值。
上述步骤中，某一时间节点和对应时间节点为同一时间。通过对同一时间上的区域级预测数据和区域级预测值，提高了数据处理的效率。在实际使用中，可判断n个时间节点上的区域级预测数据是否超过对应时间节点上的区域级预测值，若都超过则根据区域级生产数据和区域级预测数据之间的差值规划得到区域级减能耗任务和区域级减碳排任务，进而提高了判断的准确性。
本基于区块链的企业碳排能耗数据管理运营方法，还包括：
S6：数据中控判断企业生产指标是否存在同一时间的企业考核指标，若是则判断企业生产指标是否不小于同一时间的企业考核指标，若是则判定企业获得政策奖励。
上述步骤中，若数据中控判定企业获得政策奖励时，则发送领奖信息至企业端口进行可视化展示，从而提醒企业领取政策奖励，企业可通过企业端口向政府申请领取政策奖励。若否则判定企业不获得政策奖励。政策奖励可以包括发放现金、税收优惠、荣誉称号等形式。
若企业向政府申请领取政策奖励时，政府可通过企业端口或数据中控或区域级数据展示端口对数据进行复核。若复核通过，则政府向企业发放政策奖励。
本基于区块链的企业碳排能耗数据管理运营方法，还包括：
S7：数据中控设置有新能源设备数据，根据运营数据和生产指标调取新能源数据库中的新能源设备数据，其中，新能源设备数据库为若干新能源设备数据组成的集合，新能源设备数据包括新能源设备的型号，数量和价格；
上述步骤中，新能源设备包括光伏设备、风机设备和储能设备。运营数据和生产指标与新能源设备数据库之间建立有目标函数，数据中控将运营数据和生产指标代入目标函数调取相应的新能源设备数据。
S8：根据运营数据、生产指标和新能源设备数据生成节能减排报告，将节能减排报告发送至企业端口，其中，节能减排报告包括新能源设备数据、新能源设备的投入额、新能源设备的配置容量、新能源设备的配置规模、新能源设备的单位投入每年度节约的能耗值、 新能源设备的单位投入每年度节约的减碳排值、新能源设备投入前后的能耗比、新能源设备投入前后的碳排放比、新能源设备投入回报比、新能源设备投入后的预测年收益率、新能源设备投入后的回本周期；
上述步骤中，新能源设备的投入额为购买新能源设备需要的总金额，由新能源设备的数量和价格之积计算得到。新能源设备的配置容量指的是新能源设备的配置内存总量，根据新能源设备的数量和型号计算得到。新能源设备的配置规模指的是新能源设备的整体性能，也根据新能源设备的数量和型号计算得到。新能源设备的单位投入每年度节约的能耗值指的是新能源设备的投入每年度能耗值和每年度的企业的能耗指标之间差值再除以新能源设备的数量的结果，先根据新能源设备的型号和数量计算得到新能源设备的投入每年度的能耗值，再根据新能源设备的投入每年度能耗值和每年度的企业的能耗指标之差最后除以新能源设备的数量计算得到。新能源设备的单位投入每年度节约的减碳排值指的是新能源设备的投入每年度碳排放值和每年度的企业的碳排放指标之差除以新能源设备的数量的值，先根据新能源设备的型号和数量计算得到新能源设备的投入每年度的碳排放值，再根据新能源设备的投入每年度碳排放值和每年度的企业的碳排放指标之差最后除以新能源设备的数量计算得到。新能源设备投入前后的能耗比指的是新能源设备的投入每年度能耗值和每年度的企业的能耗指标之间的比值，根据新能源设备的投入每年度能耗值和每年度的企业的能耗指标之间的比值计算得到。新能源设备投入前后的碳排放比指的是新能源设备投入前企业的碳排放指标和新能源投入后预计的企业碳排放指标之间的比值。
S9：企业端口对节能减排报告进行可视化展示；
上述步骤中，企业可通过企业端口浏览节能减排报告，进而判断是否采购对应的新能源设备。若是则企业可通过新能源设备数据中的供应商联系方式和供应商取得联系。具体地，企业端口和供应商端口是互联的，企业通过企业端口与使用供应商端口的供应商取得联系。
所述新能源设备数据库通过供应商端口提供至数据中控，从而供应商可通过供应商端口对新能源数据库进行建设和更新。
具体地，若企业采购了新能源设备时，则企业端采集到的设备数据发生更新，进而数据中控中的设备数据也发生更新。
另外，本发明还公开了一种基于区块链的企业碳排能耗数据管理运营系统，其用于执行上述的基于区块链的企业碳排能耗数据管理运营方法。基于区块链的企业碳排能耗数据管理运营系统包括企业端口、数据中控、区域级数据展示端口和供应商端口，企业端口、数据中控、区域级数据展示端口和供应商端口通信连接在一起，企业端口、数据中控和区域级数据展示端口为区块链网络的一部分。企业端口用于采集运营数据以及可视化展示数据，该数据包括运营数据、生产指标、企业级运营数据和企业级考核指标。数据中控用于对数据进行处理和管理，该数据包括生产数据。区域级数据展示端口用于可视化展示数据，该数据包括区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标。供应商端口用于向数据中控提供新能源设备数据库，并且供应商可通过供应商端口对新能源设备数据库进行建设和更新。
上述实施例仅为本发明的较佳实施例，并非依此限制本发明的保护范围，故：凡依本发明的结构、形状、原理所做的等效变化，均应涵盖于本发明的保护范围之内。

Claims (5)

  1. 基于区块链的企业碳排能耗数据管理运营方法，其特征在于，所述方法包括：
    S1：企业端口采集运营数据，将运营数据发送至数据中控，其中，运营数据包括企业的用水数据、用电数据、用煤数据、用气数据、用热数据、产值数据和设备数据；
    S2：数据中控将运营数据代入智能生产核算模型计算得到企业级生产指标，将区域内所有的企业级生产指标进行累加获得区域级生产指标，将区域级生产指标代入预测算法计算得到区域级预测数据，其中，生产指标包括碳排放指标、能耗指标和经济指标；
    S3：数据中控上预先设置有区域级目标数据，判断区域级预测数据是否超过区域级目标数据，若是则根据区域级生产数据和区域级预测数据之间的差值规划得到区域级减能耗任务和区域级减碳排任务；
    S4：数据中控根据企业级生产指标和区域级生产指标计算得到企业级减能耗潜力值和企业级减碳排潜力值，根据企业级减能耗潜力值和企业级减碳排潜力值对区域级减能耗任务和区域级减碳排任务进行分配得到企业级考核指标，将企业级考核指标发送至对应的企业端口，将区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标发送至区域级数据展示端口；
    S5：企业端口对企业级运营数据和企业级考核指标进行可视化展示，区域级数据展示端口对区域级生产指标、区域级预测数据、区域级减能耗任务、区域级减碳排任务和企业级考核指标进行可视化展示；
    S6：数据中控判断企业生产指标是否存在同一时间的企业考核指标，若是则判断企业生产指标是否不小于同一时间的企业考核指标，若是则判定企业获得政策奖励；
    S7：数据中控设置有新能源设备数据库，根据运营数据和生产指标调取新能源设备数据库中的新能源设备数据，其中，新能源设备数据库为若干新能源设备数据组成的集合，新能源设备数据包括新能源设备的型号、数量和价格，新能源设备数据库通过供应商端口提供至数据中控；
    S8：根据运营数据、生产指标和新能源设备数据生成节能减排报告，将节能减排报告发送至企业端口，其中，节能减排报告包括新能源设备数据、新能源设备的投入额、新能源设备的配置容量、新能源设备的配置规模、新能源设备的单位投入每年度节约的能耗值、新能源设备的单位投入每年度节约的减碳排值、新能源设备投入前后的能耗比、新能源设备投入前后的碳排放比、新能源设备投入回报比、新能源设备投入后的预测年收益率、新能源设备投入后的回本周期；
    S9：企业端口对节能减排报告进行可视化展示；
    所述S2的步骤中，具体包括如下步骤：
    S21：将生产计算公式编写成智能合约代码形式下的生产计算公式，其中，生产计算公式包括碳排放计算公式、能耗计算公式和经济计算公式，碳排放计算公式为：
    Figure PCTCN2022138374-appb-100001
    其中，E 总指的是企业的温室气体排放总量，i指的是化石燃料的种类，NCV i指的是第i种化石燃料的平均低位发热量，FC i指的i是第种化石燃料的净消耗量，CC i指的是 第i种化石燃料的单位热值含碳量，OF i指的是第i种化石燃料的碳氧化率，m指的是温室气体的种类，ETD m指的是第m种温室气体的泄漏量，AD 电力指的是企业的净购入使用的电量，EF 电力指的是区域内电网的年平均供电排放因子，AD 热力指的是企业的净购入使用的热量；
    S22：将智能合约代码形式下的生产计算公式编译在智能合约中得到智能生产核算模型，智能生产核算模型包括签名、时间戳和文件哈希值；
    S23：将运营数据代入智能生产核算模型计算得到企业级生产指标，将企业级生产指标上传至区块链网络，其中，区块链网络包括区块链节点，区块链节点包括企业端口、数据中控和区域级数据展示端口。
  2. 根据权利要求1所述的基于区块链的企业碳排能耗数据管理运营方法，其特征在于，所述方法采用一种基于区块链的企业碳排能耗数据管理运营系统执行，一种基于区块链的企业碳排能耗数据管理运营系统包括企业端口、数据中控、区域级数据展示端口和供应商端口，企业端口、数据中控、区域级数据展示端口和供应商端口通信连接在一起，企业端口用于采集运营数据以及可视化展示数据，数据中控用于对数据进行处理和管理，区域级数据展示端口用于可视化展示数据，供应商端口用于向数据中控提供新能源设备数据库。
  3. 根据权利要求1所述的基于区块链的企业碳排能耗数据管理运营方法，其特征在于，所述智能合约代码采用的是图灵完备的编程语言。
  4. 根据权利要求1所述的基于区块链的企业碳排能耗数据管理运营方法，其特征在于，所述S3的步骤中，具体包括如下步骤：
    S31：区域级数据展示端口还可视化展示有可滑动的时间进度条；
    S32：若时间进度条在区域级数据展示端口上滑动时，区域级数据展示端口可展示不同时间的区域级生产指标和区域级预测数据。
  5. 根据权利要求1所述的基于区块链的企业碳排能耗数据管理运营方法，其特征在于，判断区域级预测数据是否超过区域级目标数据时，具体包括如下步骤：
    数据中控判断某一时间节点上的区域级预测数据是否超过对应时间节点上的区域级预测值。
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