Digital industrial production energy consumption management system and method

Abstract

The invention discloses a digital industrial production energy consumption management system and method, and relates to the technical field of energy consumption monitoring, and the system comprises a data collection layer, a block chain network layer, an environmental protection verification engine, an energy consumption analysis layer, a production equipment information collection module, an environmental protection equipment information collection module, and a metering instrument information collection module. The data acquisition layer is connected with a production equipment information collection module, an environmental protection equipment information collection module and a metering instrument information collection module through an industrial Internet of Things gateway, and obtains equipment energy consumption data and yield data in real time. Equipment full-link data is solidified through a distributed account book, a non-tampering environmental protection evidence chain is constructed, a dynamic linear verification model for monitoring trust crisis, production energy consumption and yield and environmental protection equipment power is solved, idling fraud behaviors are accurately recognized, early warning on the chain is triggered in a second level, and production scheduling is fed back in real time based on a verification result; and combined decision-making of energy efficiency control and pollution control is realized.

Description

Technical Field

[0001] This invention relates to the field of energy consumption monitoring technology, specifically to a digital industrial production energy consumption management system and method. Background Technology

[0002] Digital energy management systems have become core infrastructure for manufacturing enterprises. Current technologies primarily collect equipment energy consumption data through IoT sensors and combine this data with cloud platforms to achieve energy visualization and basic analysis. However, serious deficiencies remain in key areas such as environmental regulatory compliance verification, data credibility assurance, and cross-system collaboration. Traditional systems rely on enterprise-built databases to store energy consumption data, lacking third-party verification mechanisms. A 2023 Ministry of Ecology and Environment audit revealed that 23% of enterprises had tampered with environmental protection equipment operation records. Production equipment energy consumption data (MES system), environmental protection equipment status (environmental monitoring system), and output data (ERP system) are on separate platforms, making it impossible to establish an auditable chain of evidence. This makes it difficult for regulatory agencies to trace the authenticity of the data. Current technologies only monitor the on / off status or instantaneous operation of environmental protection equipment. Power consumption cannot verify actual processing efficiency. For example, a provincial inspection found that 38% of VOCs treatment equipment did not reach rated processing efficiency when running (no-load operation). Enterprises over-operate on environmental protection equipment during low-load production to fraudulently obtain subsidies, and shut down the core modules of treatment equipment during high-pollution production periods. In existing solutions, the energy management platform and environmental monitoring system are physically isolated, making it impossible to achieve joint optimization goals such as "minimizing carbon emissions per unit output". Fixed threshold alarm mechanisms (such as warnings triggered when power exceeds the threshold) cannot adapt to production fluctuations, with a false alarm rate exceeding 35%. Energy efficiency benchmark parameters need to be manually configured and cannot be transferred across industries. Discrete manufacturing industries need to redevelop the system, with an implementation cycle of 6-8 months. High-precision acquisition hardware compatible with multiple protocols accounts for more than 65% of the total system cost, which is unaffordable for small and medium-sized enterprises, thus hindering the widespread adoption of green manufacturing technologies. Summary of the Invention

[0003] The purpose of this invention is to provide a digital industrial production energy consumption management system and method to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a digital industrial production energy consumption management system, the system comprising:

[0005] The system includes a data acquisition layer, a blockchain network layer, an environmental protection verification engine, an energy consumption analysis layer, a production equipment information collection module, an environmental protection equipment information collection module, and a metering instrument information collection module.

[0006] The data acquisition layer connects to the production equipment information collection module, the environmental protection equipment information collection module, and the metering instrument information collection module through an industrial IoT gateway to acquire equipment energy consumption data and production data in real time.

[0007] The blockchain network layer consists of multiple distributed nodes, and smart contracts are used to encrypt and store the collected data.

[0008] The environmental protection verification engine has a built-in linear relationship verification module that dynamically generates a credibility report based on the power and production data of environmental protection equipment.

[0009] The energy consumption analysis layer generates multi-dimensional energy efficiency optimization strategies.

[0010] There is a bidirectional signal connection between the data acquisition layer and the blockchain network layer, a bidirectional signal connection between the environmental protection verification engine and the energy consumption analysis layer, a signal connection between the blockchain network layer and the energy consumption analysis layer, and a signal connection between the data acquisition layer and the environmental protection verification engine.

[0011] Furthermore, the blockchain network layer includes:

[0012] Data on-chain module: Writes the original data hash value of the device into a Merkle tree structure.

[0013] Consensus module: Uses the PBFT mechanism to verify the consistency of node data.

[0014] Timestamp service module: Attaches an immutable timestamp to each piece of data.

[0015] Furthermore, the environmental verification engine executes the following verification algorithm:

[0016] Obtain production data per unit time: Energy consumption of production equipment Production volume (Q) and environmental protection equipment power .

[0017] Calculate the standardized energy efficiency ratio: ,in This is the industry calibration coefficient.

[0018] Conditions for verifying the effectiveness of environmental protection equipment: , and These are the equipment's baseline parameters.

[0019] Furthermore, the environmental verification engine also includes a confidence interval calculation unit, which calculates the confidence interval when n consecutive periods satisfy:

[0020]

[0021] To determine if the equipment is operating effectively, the following criteria must be met:

[0022] : Standard deviation of historical data.

[0023] Allowable deviation threshold.

[0024] Furthermore, the baseline parameters of the environmental protection equipment are generated through the following process:

[0025] Collect standard operating condition datasets ( ).

[0026] Least squares fitting equation: = .

[0027] The solution and Write it into a blockchain smart contract.

[0028] Furthermore, the data acquisition layer includes:

[0029] Device fingerprint module: Assigns a unique blockchain ID to the device.

[0030] Edge computing unit: Calculates energy consumption characteristics at the data source.

[0031] Trusted transmission module: uses the national cryptographic algorithm SM9 for encrypted transmission.

[0032] Furthermore, the energy consumption analysis layer includes:

[0033] Carbon footprint tracking module: Calculates product carbon emissions based on on-chain data.

[0034] Dynamic optimization module: Adjusts production schedules based on environmental verification results.

[0035] Violation warning module: Triggers on-chain alarm when verification fails.

[0036] A digital industrial production energy consumption management method, applied in any one of the above-mentioned digital industrial production energy consumption management systems, includes the following steps:

[0037] S1. The data acquisition layer acquires the operating data of the production equipment information collection module, the environmental protection equipment information collection module, and the metering instrument information collection module in real time. After the data is marked with a unique identity by the equipment fingerprint module, the energy consumption feature value is generated in the edge computing unit and then encrypted and sent to the blockchain network layer by the trusted transmission module.

[0038] S2. After receiving encrypted data, the blockchain network layer writes the data hash value into the Merkle tree structure by the data on-chain module. The consensus module verifies the consistency of nodes through the PBFT mechanism. The timestamp service module adds an immutable timestamp to each piece of data to complete distributed evidence storage.

[0039] S3, the environmental protection verification engine calls the linear relationship verification module to perform linear relationship verification. Based on the dynamic correlation between the power of environmental protection equipment and production data, it generates an equipment operation credibility report and writes the verification results into the blockchain network layer for evidence storage.

[0040] S4. The energy consumption analysis layer calculates the carbon emissions of the product through the carbon footprint tracking module based on the verification results stored on the chain. The dynamic optimization module adjusts the production schedule. The violation warning module triggers an on-chain alarm when the verification fails. At the same time, it generates an environmental credit score and sends it back to the blockchain network layer for storage.

[0041] S5. The system feeds back the optimization strategies and compliance status output by the energy consumption analysis layer to the production equipment information collection module in real time to achieve closed-loop control.

[0042] Furthermore, the linear relationship verification includes dynamic weight calculation:

[0043] Confidence weight calculation:

[0044]

[0045] in:

[0046] : Equipment compliance operating time.

[0047] : Attenuation coefficient.

[0048] Overall score calculation:

[0049]

[0050] in:

[0051] Single verification result, compliant. =1, non-compliant =0.

[0052] when When environmental inspections are triggered, It is a dynamically configurable threshold value that is pre-set by regulatory agencies or enterprises according to environmental policy requirements.

[0053] Furthermore, the dynamic correction formula for the industry calibration coefficient k is as follows:

[0054]

[0055] in:

[0056] : Regulation factor, 0 .

[0057] : System measured energy efficiency.

[0058] Industry benchmark energy efficiency.

[0059] This invention provides a digital industrial production energy consumption management system and method. It has the following beneficial effects:

[0060] (1) In this invention, a trustworthy data foundation is constructed to solve the regulatory trust problem. The energy consumption and environmental protection data flow of industrial equipment are solidified through the blockchain distributed ledger, ensuring that the original data is traceable and tamper-proof throughout the entire chain, providing legally valid data credentials for environmental supervision. Multi-node consensus verification eliminates the moral hazard of enterprises proving their innocence and establishes a trustworthy data sharing environment among regulatory departments, third-party institutions, and production enterprises.

[0061] (2) In this invention, an innovative environmental protection equipment supervision paradigm is established to eliminate false operation. It is the first to conduct real-time linear correlation analysis between production data (energy consumption / output) and the power of environmental protection equipment, accurately identify fraudulent behaviors such as equipment idling and inefficient operation, and automatically trigger on-chain alarms based on mathematical model anomaly detection, realizing a minute-level response closed loop from data collection to violation handling, replacing the traditional manual spot check mode.

[0062] (3) In this invention, the value of industrial data is released to drive green production optimization, and the environmental protection verification results are dynamically fed back to the production scheduling system to realize collaborative decision-making on energy consumption control and pollution control (such as automatically adjusting production capacity during high pollution periods). Based on blockchain-stored production chain data, verifiable product-level carbon emission reports are generated to support carbon trading and green supply chain certification.

[0063] (4) In this invention, the threshold for system implementation is lowered, the industry universality is improved, key calculations are completed at the source of data, the storage pressure of blockchain is reduced, the limited infrastructure conditions of small and medium-sized factories are adapted, and different production scenarios (such as discrete manufacturing and process industries) are automatically matched through an adaptive industry benchmark learning mechanism, avoiding customized development costs.

[0064] (5) In this invention, a new type of regulatory tool is created to empower green development policies. Based on continuous verification results, an environmental credit score for enterprises is generated, providing an objective basis for the implementation of policies such as green credit and tax incentives. It supports regulatory authorities in remotely verifying the authenticity of the operation of enterprises' environmental protection equipment, greatly reducing law enforcement costs and solving the regulatory dilemma of "turning on during inspection and turning off after leaving". Attached Figure Description

[0065] Figure 1 This is a general system diagram of a digital industrial production energy consumption management system and method according to the present invention;

[0066] Figure 2This is a schematic diagram of the data acquisition layer of a digital industrial production energy consumption management system and method according to the present invention;

[0067] Figure 3 This is a schematic diagram of the blockchain network layer of a digital industrial production energy consumption management system and method according to the present invention;

[0068] Figure 4 This is a schematic diagram of an environmental verification engine for a digital industrial production energy consumption management system and method according to the present invention.

[0069] Figure 5 This is a schematic diagram of the energy consumption analysis layer of a digital industrial production energy consumption management system and method according to the present invention.

[0070] In the diagram: 1. Data Acquisition Layer; 2. Blockchain Network Layer; 3. Environmental Verification Engine; 4. Energy Consumption Analysis Layer; 101. Equipment Fingerprint Module; 102. Edge Computing Unit; 103. Trusted Transmission Module; 110. Production Equipment Information Collection Module; 120. Environmental Protection Equipment Information Collection Module; 130. Metering Instrument Information Collection Module; 201. Data On-Chain Module; 202. Consensus Module; 203. Timestamp Service Module; 210. Smart Contract; 310. Linear Relationship Verification Module; 311. Confidence Interval Calculation Unit; 401. Carbon Footprint Tracking Module; 402. Dynamic Optimization Module; 403. Violation Early Warning Module. Detailed Implementation

[0071] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0072] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention.

[0073] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0074] Example 1, please refer to Figure 1-5 This invention provides a technical solution: a digital industrial production energy consumption management system, the system comprising:

[0075] The system comprises a data acquisition layer 1, a blockchain network layer 2, an environmental verification engine 3, an energy consumption analysis layer 4, a production equipment information collection module 110, an environmental protection equipment information collection module 120, and a metering instrument information collection module 130. The data acquisition layer 1 connects to the production equipment information collection module 110, the environmental protection equipment information collection module 120, and the metering instrument information collection module 130 via an industrial IoT gateway to acquire real-time equipment energy consumption and production data. The blockchain network layer 2 consists of multiple distributed nodes and uses smart contracts 210 to encrypt and store the collected data. The environmental verification engine 3 has a built-in linear relationship verification module 310 that dynamically generates a credibility report based on the power of environmental protection equipment and production data. The energy consumption analysis layer 4 generates multi-dimensional energy efficiency optimization strategies. There are bidirectional signal connections between the data acquisition layer 1 and the blockchain network layer 2, between the environmental verification engine 3 and the energy consumption analysis layer 4, between the blockchain network layer 2 and the energy consumption analysis layer 4, and between the data acquisition layer 1 and the environmental verification engine 3.

[0076] Please see Figure 1-5 Blockchain network layer 2 includes: a data on-chain module 201, which writes the hash value of the original device data into a Merkle tree structure; a consensus module 202, which uses the PBFT mechanism to verify the consistency of node data; a timestamp service module 203, which attaches an immutable timestamp to each piece of data; and an environmental verification engine 3, which executes the following verification algorithm: obtaining production data per unit time: energy consumption of production equipment. Production volume (Q) and environmental protection equipment power Calculate the standardized energy consumption ratio: ,in The industry calibration coefficient is used to verify the effectiveness of environmental protection equipment. , and For the equipment reference parameters, the dynamic correction formula for the industry calibration coefficient k is: ,in: : Regulation factor, 0 , The system's measured energy efficiency The industry benchmark energy efficiency and environmental protection verification engine 3 also includes a confidence interval calculation unit 311, which calculates the confidence interval when n consecutive cycles meet the following conditions: To determine if the equipment is operating effectively, the following criteria are included: Standard deviation of historical data The allowable deviation threshold and the baseline parameters for environmental protection equipment are generated through the following process: collecting standard operating condition datasets. The least squares method fits the equation: = The solution will be obtained and Write to blockchain smart contract 210. Data acquisition layer 1 includes: device fingerprint module 101: assigns a unique blockchain ID to the device; edge computing unit 102: calculates energy consumption characteristic value at the data source; trusted transmission module 103: encrypts transmission using the national cryptographic SM9 algorithm; energy consumption analysis layer 4 includes: carbon footprint tracking module 401: calculates product carbon emissions based on on-chain data; dynamic optimization module 402: adjusts production schedule according to environmental protection verification results; and violation warning module 403: triggers on-chain alarm when verification fails.

[0077] Please see Figure 1-5 A digital industrial production energy consumption management method, applied to any of the above-mentioned digital industrial production energy consumption management systems, includes the following steps: S1, real-time acquisition of operating data from the production equipment information collection module 110, the environmental protection equipment information collection module 120, and the metering instrument information collection module 130 through the data acquisition layer 1; after the data is uniquely identified by the equipment fingerprint module 101, energy consumption feature values ​​are generated in the edge computing unit 102, and encrypted and sent to the blockchain network layer 2 by the trusted transmission module 103; S2, after receiving the encrypted data, the blockchain network layer 2 writes the data hash value into the Merkle tree structure by the data on-chain module 201; the consensus module 202 verifies node consistency through the PBFT mechanism; and the timestamp service module 203 adds an immutable timestamp to each piece of data. The distributed notarization process is completed. S3, the environmental verification engine 3 calls the linear relationship verification module 310 to perform linear relationship verification. Based on the dynamic correlation between the power of environmental protection equipment and production data, it generates an equipment operation credibility report and writes the verification results to the blockchain network layer 2 for notarization. S4, the energy consumption analysis layer 4 calculates product carbon emissions through the carbon footprint tracking module 401 based on the verification results stored on the chain. The dynamic optimization module 402 adjusts the production schedule. The violation warning module 403 triggers an on-chain alarm when verification fails, and simultaneously generates an environmental credit score and sends it back to the blockchain network layer 2 for notarization. S5, the system feeds back the optimization strategy and compliance status output by the energy consumption analysis layer 4 to the production equipment information collection module 110 in real time, achieving closed-loop control. The linear relationship verification includes dynamic weight calculation: confidence weight calculation: ,in: : Equipment compliance operating time Attenuation coefficient, comprehensive score calculation: ,in: Single verification result, compliant. =1, non-compliant =0, when When environmental inspections are triggered, It is a dynamically configurable threshold value that is pre-set by regulatory agencies or enterprises according to environmental policy requirements.

[0078] Example 2: This example uses a cement production line with a daily clinker output of 5,000 tons as an example. Please refer to [link / reference]. Figure 1-5 The present invention provides a technical solution:

[0079] A digital industrial production energy consumption management system, the system comprising:

[0080] The data acquisition layer 1 is equipped with a device fingerprint module 101, an edge computing unit 102, and a trusted transmission module 103.

[0081] The equipment fingerprint module 101 generates globally unique blockchain identity identifiers for the rotary kiln main motor, raw material mill main motor, high temperature fan, electrostatic precipitator high voltage power supply, desulfurization tower circulating pump and supporting smart meters and gas flow meters.

[0082] Edge computing unit 102 calculates three energy consumption characteristic values ​​in real time at the data source: "unit clinker power consumption", "unit clinker heat consumption" and "unit flue gas treatment energy consumption of electrostatic precipitator".

[0083] The trusted transmission module 103 uses the national cryptographic SM9 algorithm to encrypt the feature value and the original data, and then sends them to the blockchain network layer 2 via industrial Ethernet.

[0084] Blockchain network layer 2 consists of seven distributed nodes, which are deployed in the central control room of the cement production enterprise, the group headquarters, a third-party environmental regulatory agency, the equipment manufacturer's maintenance center, the power grid company, the provincial energy consumption monitoring center, and the national carbon emission exchange. Blockchain network layer 2 deploys a data on-chain module 201, a consensus module 202, a timestamp service module 203, and a smart contract 210.

[0085] The data upload module 201 calculates the hash value of the received encrypted data and writes it into a Merkle tree structure;

[0086] Consensus module 202 uses the PBFT mechanism to complete data consistency verification among seven nodes;

[0087] The timestamp service module 203 adds an immutable timestamp to each piece of data;

[0088] Smart contract 210 solidifies the benchmark parameters and compliance judgment rules for environmental protection equipment, which are then called by the environmental verification engine 3.

[0089] The environmental verification engine 3 has a built-in linear relationship verification module 310, and the linear relationship verification module 310 has a confidence interval calculation unit 311 deployed inside.

[0090] The environmental verification engine 3 obtains the high-voltage power of the electrostatic precipitator, clinker output, desulfurization tower circulating pump power, exhaust gas flow rate and sulfur content in real time from the blockchain network layer 2;

[0091] The linear relationship verification module 310 calls the confidence interval calculation unit 311 to determine whether the ratio of the high-voltage power of the electrostatic precipitator to the clinker output within five consecutive cycles falls within the historical confidence interval.

[0092] If all cycles fall within the confidence interval, the environmental verification engine 3 generates a credibility report of "effective operation of the electrostatic precipitator" and writes the report to the blockchain network layer 2 for evidence storage; if any cycle falls outside the confidence interval, a report of "abnormal operation of the electrostatic precipitator" is generated and also stored on the blockchain for evidence storage.

[0093] The energy consumption analysis layer 4 is equipped with a carbon footprint tracking module 401, a dynamic optimization module 402, and a violation early warning module 403.

[0094] The carbon footprint tracking module 401 calculates the carbon emissions of this batch of clinker based on all the original data stored in the blockchain network layer 2.

[0095] According to the "abnormal operation of electrostatic precipitator" report returned by the environmental verification engine 3, the dynamic optimization module 402 automatically reduces the speed of the high-temperature fan by 5%, the feed rate of the rotary kiln by 3%, and the power of the raw material mill main unit by 4%, and sends the new production scheduling parameters to the production equipment information collection module 110 of the data acquisition layer 1 through a one-way signal connection.

[0096] The violation warning module 403 immediately triggers an on-chain alarm after the "abnormal operation of electrostatic precipitator" report is uploaded to the chain, and pushes the alarm information to the node of the third-party environmental protection regulatory agency. At the same time, it generates an environmental credit score and sends it back to the blockchain network layer 2 for storage.

[0097] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A digitalized industrial production energy consumption management system, characterized in that, The system comprises: a data acquisition layer (1), a blockchain network layer (2), an environmental protection verification engine (3), an energy consumption analysis layer (4), a production equipment information collection module (110), an environmental protection equipment information collection module (120), and a metering instrument information collection module (130); The data acquisition layer (1) connects the production equipment information collection module (110), the environmental protection equipment information collection module (120), and the metering instrument information collection module (130) through an industrial Internet of Things gateway, and acquires device energy consumption data and yield data in real time; The blockchain network layer (2) is composed of multiple distributed nodes, and uses a smart contract (210) to encrypt and store the collected data; The environmental protection verification engine (3) has a linear relationship verification module (310) built-in, and generates a credibility report based on the power of the environmental protection equipment and the production data; The energy consumption analysis layer (4) generates a multi-dimensional energy efficiency optimization strategy; The data acquisition layer (1) and the blockchain network layer (2) are bidirectionally connected, the environmental protection verification engine (3) and the energy consumption analysis layer (4) are bidirectionally connected, the blockchain network layer (2) and the energy consumption analysis layer (4) are connected, and the data acquisition layer (1) and the environmental protection verification engine (3) are connected.

2. The digitalized industrial production energy consumption management system according to claim 1, characterized in that, The blockchain network layer (2) comprises: a data chaining module (201) for writing device original data hash values into a Merkle tree structure a consensus module (202) for verifying node data consistency using a PBFT mechanism; a timestamp service module (203) for attaching an unforgeable timestamp to each piece of data.

3. The digitalized industrial production energy consumption management system according to claim 2, characterized in that, The environmental protection verification engine (3) executes the following verification algorithm: Acquisition of production data per unit of time: energy consumption of production equipment , output Q, power of environmental protection equipment ; The normalized energy consumption ratio is calculated as: where is the industry calibration factor. Verification of environmental equipment effectiveness conditions: , With Reference parameters for equipment.

4. The digitalized industrial production energy consumption management system according to claim 3, characterized in that, The environmental protection verification engine (3) further comprises a confidence interval calculation unit (311), which satisfies the following conditions when the consecutive n periods: determine that the device is running effectively, wherein: : historical data standard deviation; : allowable deviation threshold value.

5. The digitalized industrial production energy consumption management system according to claim 4, characterized in that, The environmental protection equipment reference parameters are generated by the following process: Collecting standard working condition data set (S) ) Least squares fitting equation: = ; The solved With Write to blockchain smart contract (210).

6. The digitalized industrial production energy consumption management system according to claim 5, characterized in that, The data acquisition layer (1) comprises: a device fingerprint module (101) for assigning a blockchain unique ID to the device; an edge computing unit (102) for calculating energy consumption characteristic values at the data source; a trusted transmission module (103) for using the national SM9 algorithm for encrypted transmission.

7. The digitalized industrial production energy consumption management system according to claim 6, characterized in that, The energy consumption analysis layer (4) comprises: a carbon footprint tracking module (401) for calculating product carbon emissions based on on-chain data; a dynamic optimization module (402) for adjusting production scheduling according to the environmental protection verification result; a violation warning module (403) for triggering on-chain alarms when verification fails.

8. A method for managing energy consumption in digitized industrial production, characterized by, The system is applied to the digital industrial production energy consumption management system of any one of claims 1-7, comprising the following steps: S1, real-time acquisition of the running data of the production equipment information collection module (110), the environmental protection equipment information collection module (120), and the metering instrument information collection module (130) through the data acquisition layer (1), the data being marked with a unique identity by the device fingerprint module (101), generating energy consumption characteristic values in the edge computing unit (102), and being encrypted and sent to the blockchain network layer (2) by the trusted transmission module (103); S2, after the blockchain network layer (2) receives the encrypted data, the data hash value is written into the Merkle tree structure by the data chaining module (201), the consensus module (202) verifies the node consistency through the PBFT mechanism, the timestamp service module (203) adds an unforgeable timestamp to each piece of data, and completes the distributed evidence storage; S3, the environmental protection verification engine (3) calls the linear relationship verification module (310) to perform linear relationship verification, generates a device operation credibility report based on the dynamic correlation between the power of the environmental protection equipment and the production data, and writes the verification result into the blockchain network layer (2) for storage; S4, according to the verification result stored on the chain, the carbon footprint tracking module (401) calculates the product carbon emission, the dynamic optimization module (402) adjusts the production schedule, and the illegal early warning module (403) triggers the on-chain alarm when the verification fails, and generates the environmental protection credit score and returns it to the blockchain network layer (2) for storage; S5, the system feeds back the optimization strategy and compliance state output by the energy consumption analysis layer (4) to the production equipment information collection module (110) in real time, realizing closed-loop control.

9. The method of claim 8, wherein, The linear relationship verification includes dynamic weight calculation: Confidence weight calculation: Wherein: : device compliance runtime length; : attenuation coefficient; Comprehensive score calculation: Wherein: : single verification result, compliant = 1, non-compliant = 0; When triggering environmental protection inspection, is a dynamically configurable threshold value, which is set in advance by the regulatory agency or enterprise according to environmental protection policy requirements.

10. The digitalized industrial production energy consumption management system according to claim 3, characterized in that, The dynamic correction formula of the industry calibration coefficient k is: Wherein: : regulator, 0 ; : system measured energy efficiency; : Industry benchmark energy efficiency.

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