Zero-carbon factory gas transmission end and gas consumption end closed-loop control method and system

Abstract

This application provides a closed-loop control method and system for the gas supply and consumption ends of a zero-carbon factory, relating to the field of energy-saving control technology for industrial compressed air systems. It solves the technical problems of information flow disruption between gas supply and consumption, untraceable energy loss, rigid supply-demand matching, and delayed fault location. The method includes: quantifying gas consumption value into energy tokens; calculating consumption value and destroying tokens at the gas consumption end; using a two-way auction mechanism to match demand envelopes and supply quotations in real time to generate gas supply contracts at the scheduling layer; and triggering isolation control based on token conservation audit. This application is used in the process of transparent energy management and dynamic supply-demand optimization control of compressed air systems in zero-carbon factories.

Description

Technical Field

[0001] This invention belongs to the field of intelligent factory control technology, specifically a closed-loop control method and system for the gas supply and consumption ends of a zero-carbon factory. Background Technology

[0002] In the compressed air system of a zero-carbon factory, coordinated control of the air supply and consumption ends is a key link in achieving system energy efficiency optimization and accurate carbon emission accounting. Existing technologies for compressed air system control mainly fall into three categories: The first is a centralized group control method based on a fixed pressure threshold. This method sets a target pressure at the main pipeline and uses variable frequency speed regulation and loading / unloading logic to control the air output of the air compressor station, maintaining it within a specified pressure range. The second is a feedforward control method based on flow prediction. This method uses historical air consumption data to establish statistical or machine learning models to predict air demand over a future period and adjusts the output plan of the air supply end accordingly. The third is a simulation optimization method based on pipeline network modeling. This method establishes a fluid dynamics model of the pipeline network, performs offline simulation of the pressure distribution and flow allocation at each node of the network, generates a scheduling strategy, and then issues it for execution.

[0003] However, all of the aforementioned existing methods have significant shortcomings. First, at the energy metering level, existing methods generally use gas volume or mass as the unit of measurement, ignoring the differences in the gas's ability to perform work under different pressures and temperatures. This results in a failure to establish an energy-quality matching relationship between the high-grade energy supplied and the low-grade demand at the consumption end, leaving the actual efficiency of end-user energy consumption invisible and uncalculated. Second, at the supply-demand matching level, centralized group control methods and flow prediction methods rely on rigid preset values ​​or statistical averages for scheduling, failing to effectively address the random fluctuations in gas load and the differentiated energy quality demands in discrete manufacturing scenarios. This leads to the system operating in a redundant state of excessive gas supply for extended periods, with the pipeline pressure being raised across the board to ensure the minimum pressure requirements of the furthest equipment. This global pressurization introduces significant throttling losses and leakage amplification effects throughout the entire pipeline network. Third, at the fault diagnosis level, existing methods primarily rely on ultrasonic inspections or single-point pressure anomaly alarms for leak detection. This results in a long time window between the occurrence and detection of a leak, during which continuous energy dissipation increases carbon emissions and poses a safety hazard. Summary of the Invention

[0004] This application provides a closed-loop control method and system for the gas supply and consumption ends of a zero-carbon plant, which solves the technical problems of broken information flow between gas supply and consumption, untraceable energy loss, and delayed fault location in the prior art.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, a closed-loop control method for the gas transmission and consumption ends of a zero-carbon plant is provided, including: A token generator is set up at the gas supply source to quantify the energy value of the output gas into an energy token; the energy value represents the maximum energy value that the gas can be converted into useful work under a given pressure, temperature and flow rate. The system uses the pressure regulating valve at the gas end to output the required pressure and calculates the amount of energy consumed, and then destroys the corresponding number of energy tokens based on the calculated amount of energy consumed. The scheduling layer receives demand envelopes, including volume ranges and pressure constraints, broadcast by each gas user, as well as supply quotations from the gas supplier and energy storage end. It then uses a two-way auction mechanism to match the demand envelopes and supply quotations in real time to generate gas supply contracts. The two-way auction mechanism refers to a market mechanism in which buyers and sellers submit quotations and demands simultaneously within the same auction period, and all quotations are centrally matched and cleared according to a preset optimization target. Based on the total number of tokens generated by the token generator, the total number of tokens destroyed at each gas-consuming end, the total number of residual tokens recovered at the end of the recycling pipeline, and the token conversion amount corresponding to the gas storage in the pipeline, a token quantity conservation audit is performed, and isolation control is triggered according to the audit results.

[0006] Based on the above technical solution, in the closed-loop control method for the gas transmission and consumption ends of a zero-carbon factory provided in this application, the gas sludge value is quantified into energy tokens, and the actual work capacity of the gas is used as the basic unit for energy measurement and trading. This unifies the measurement scale of energy supply quality at the transmission end and demand quality at the consumption end, eliminating energy accounting distortion caused by pressure and temperature differences in traditional methods. By destroying energy tokens at the consumption end according to the actual sludge consumption, a real-time consumption confirmation mechanism for sludge value and tokens is established, making the energy destination of each energy consumption behavior traceable and verifiable, solving the long-standing problem of invisible and uncalcifiable end-point energy consumption in pneumatic systems. The scheduling layer uses a two-way auction mechanism to manage the flexible demand of each gas consumption end. The system matches gas supply and energy storage quotes in real time, giving gas users the ability to express their energy consumption flexibility. This allows the system's supply and demand matching to no longer rely on rigid forecasts, but instead achieves distributed optimal configuration spontaneously through market-based price signals, improving the system's adaptability to production fluctuations and distributed energy access. By incorporating the entire network's token generation, consumption, recovery, and dynamic gas storage into a unified conservation audit, and triggering isolation control for audit deviations, closed-loop cross-verification of energy flow and information flow is achieved. This ensures that any unmetered leaks, metering drift, or abnormal losses in the pipeline network can be detected and automatically isolated in a timely manner, thereby minimizing energy losses and carbon emission impacts caused by faults while ensuring gas supply continuity.

[0007] Furthermore, the zero-carbon plant includes a high-pressure gas supply network and a low-pressure gas recovery network; The high-pressure gas supply network is used to transport the high-pressure gas output from the air compressor station or energy storage terminal to each gas user terminal, serving as the physical channel for the synchronous transmission of the energy token to the airflow, and providing a high-pressure side pressure source for the flow regulating valve, enabling the flow regulating valve to complete the pressure regulation output by utilizing the pressure difference between the high-pressure side and the low-pressure side. The low-pressure recovery pipeline network is used to collect all low-pressure waste gas emitted after the gas-consuming end has finished its work, and to transport the low-pressure waste gas to the regeneration center, and to serve as the physical channel for the residual token to be returned with the low-pressure airflow; wherein, the regeneration center is an energy recovery and working fluid regeneration facility set at the end of the low-pressure recovery pipeline network, used to operate gas-electricity cycle, gas-heat cycle and gas-gas cycle in parallel, to recover the pressure energy, heat energy and working fluid in the waste gas and feed them back to the factory energy system and high-pressure gas supply network respectively.

[0008] Furthermore, the energy tokens are generated by a token generator protected by a hardware security module. Each token contains a unique identifier, an initial value, a generation timestamp, pressure and temperature parameters of the generation point, and the hash value of the previous token.

[0009] Furthermore, the energy token is transmitted synchronously with the airflow, specifically including: In the high-pressure gas supply network, each time the token generator generates an energy token, it simultaneously sends out the data packet of the token and the corresponding gas flow that generated the token through the network. The token audit gateway deployed at each pipe segment node of the pipeline network reads the token identifiers flowing through the pipeline in real time and records the passage time. It also collects the gas pressure and flow rate at each pipe segment node, establishing a mapping relationship between the advancement of the token flow on the time axis and the advancement of the gas on the spatial axis. This yields the time deviation between the arrival time of the token at each gas-consuming end and the arrival time of the corresponding gas flow at the same gas-consuming end. The pipe segment node is an intelligent monitoring unit located at a sectional valve, branch point, or pipe diameter change point in the high-pressure gas supply network, used to monitor the gas state parameters and token passage status of the upstream and downstream pipe segments. The token flow, generated chronologically by the token generator, arranges the energy tokens sequentially. The time deviation is used to verify the synchronization between the token and the gas flow. When the time deviation exceeds a preset synchronization threshold, a synchronization anomaly alarm is triggered at the corresponding pipe segment node. In the low-pressure recovery pipeline network, the residual tokens generated synchronously when the gas is exhausted are transmitted back to the regeneration center with the airflow, completing the synchronous transmission of the token flow and the airflow.

[0010] Furthermore, the calculation process for the consumed amount includes: When the flow regulating and pressure regulating valve performs the gas consumption action, it collects the gas pressure, temperature and mass flow rate entering the valve group in real time, as well as the pressure, temperature and mass flow rate of the discharged gas, and obtains the inlet state parameters and the exhaust state parameters respectively. The intake air-carrying input ν value is calculated based on the intake air state parameters, and the exhaust air-carrying output ν value is calculated based on the exhaust air state parameters. The difference between the input gas value and the output gas value is used to obtain the gas consumption amount of the current gas consumption action; The formula for calculating the value of 㶲 is: E is the gas enthalpy, m is the gas mass, h is the gas specific enthalpy, h0 is the gas specific enthalpy under environmental reference conditions, T0 is the environmental reference temperature, s is the gas specific entropy, and s0 is the gas specific entropy under environmental reference conditions. The environmental reference conditions are the annual average temperature and standard atmospheric pressure of the factory location.

[0011] Furthermore, the step of destroying a corresponding number of energy tokens based on the calculated amount specifically includes: The edge computing unit built into the flow regulating and pressure regulating valve receives the energy tokens that arrive with the intake airflow and processes them sequentially according to the order of arrival. Based on the calculated consumption, the energy tokens are deducted one by one from the currently held energy tokens. Each time a token is deducted, it means that the token is digitally signed using the private key corresponding to the flow regulating valve and marked as consumed. Tokens marked as consumed are broadcast to the entire network and removed from subsequent transactions and circulation processes.

[0012] Furthermore, when the value of a token is partially consumed, the token is split into a new token with the same value as the consumed amount and destroyed, and a change token with the same value as the remaining value is generated and put back into the token stream for continued transmission. Simultaneously, the residual value carried by the exhaust gas is calculated based on the exhaust gas state parameters, and an equal amount of residual tokens are generated locally, which enter the low-pressure recovery pipeline network along with the exhaust gas.

[0013] Furthermore, the demand envelope includes: the volume range required for a single operation of the gas-using equipment, the minimum allowable gas supply pressure of the gas-using equipment, the allowable time delay range for the operation of the gas-using equipment, and the default cost function of the gas-using equipment under different gas supply conditions; the default cost function is used to quantify the loss of production value of the gas-using equipment when the gas supply conditions deviate from the rated demand of the gas-using equipment, and is constructed based on the level of the industrial process performed by the gas-using equipment, the product scrap loss rate, and the production line downtime cost data.

[0014] Furthermore, the expression for the default cost function is: ;in, For the cost of breach of contract, This is the actual gas supply pressure. For gas supply delay time, This is the minimum pressure requirement for gas-using equipment. The maximum delay time, The penalty coefficient for insufficient pressure. This is the base cost coefficient for delay. This is the delay time amplification factor. The weight of the shutdown flag is 1. The value lost per unit of production line downtime. This is an indicator function that takes the value 1 if the condition within the parentheses is true, and 0 otherwise.

[0015] Furthermore, the gas supply contract is used to specify the quantity, gas pressure reference curve, and delivery time to be delivered from the gas supplier or energy storage unit to the specified gas user within a specified time window. The specific process of generating the gas supply contract includes: Each gas user obtains the sequence of actions to be executed within a preset time before the equipment is activated, generates the demand envelope based on the action sequence, and broadcasts the demand envelope to the auction room. The auction room is a virtual bidding space running on the edge computing node of the corresponding subnet, responsible for matching buy and sell transactions for all gas users, gas suppliers, and energy storage units within the subnet. A subnet refers to multiple local pipeline regions with independent supply and demand balancing capabilities, formed by dividing the entire pipeline network from top to bottom with preset segment valves or pressure regulating nodes in the high-pressure gas supply network as boundaries. Each subnet is monitored by a pipeline node agent for the inflow and outflow of tokens and pressure status, and matched by an auction room for the buy and sell applications within the subnet. Each pipeline segment node agent within the subnet is used to monitor the difference between inflow and outflow tokens and the gas storage status of its jurisdiction, and reports the monitoring data to the pipeline node agents of its subnet. When there is a local supply and demand imbalance, the pipeline node agent executes cascading routing across subnets. Based on real-time operating status, efficiency curves, and marginal costs, the gas supply and energy storage units generate supply orders that include available quantity, pressure level, and supply price, and submit the supply orders to the auction room. Gas consumers submit their supply orders to the same auction room using the demand envelope as their supply order. The supply order records the range of quantities the gas consumer is willing to purchase within a specified time window, the highest acceptable pressure bid, and pressure and time constraints. The auction room aims to maximize social welfare and, while satisfying the pressure and time constraints, matches and clears all buy and sell orders; wherein, maximizing social welfare means maximizing the difference between the total willingness of all buyers to pay and the total cost of the sellers. When the supply of the local subnet is insufficient, the pipeline nodes of the subnet act as agents to cascade routes to adjacent subnets or upstream subnets, and the upper-level auction room matches gas supply demands across subnets until the gas supply demand is met. Finally, a gas supply contract is generated by matching the successful bids and sell orders. The gas supply contract includes: the identification of the gas supplier or energy storage unit, the delivery quantity agreed in the contract, the gas supply pressure reference curve, the clearing settlement price, and the default compensation.

[0016] Furthermore, the matching and clearing of all buy and sell orders includes: Sort all buy orders from highest to lowest according to the highest acceptable price listed, and obtain the buyer's price curve; Sort all sell orders by their supply quotes from lowest to highest to obtain the seller price curve; The buyer's price curve and the seller's price curve are matched sequentially from left to right on the same volume axis. When the price curves of the buyer and seller intersect, the clearing price is determined, and all successfully matched buy and sell orders are executed at the clearing price.

[0017] Furthermore, the gas supply and energy storage terminals generate supply orders that include available quantity, pressure level, and supply price based on real-time operating status, efficiency curves, and marginal costs, specifically including: The gas supply end calculates the actual power consumption corresponding to the unit gas volume under the current operating conditions based on the current operating power, exhaust pressure, exhaust temperature and mass flow rate of the air compressor or booster equipment. The pressure-flow-specific power surface obtained by fitting the equipment's factory performance test report or historical operating data is used as the efficiency curve. The ratio of the actual power consumption to the optimal power consumption of the efficiency curve under the same operating conditions is used as the energy efficiency attenuation coefficient. The gas supplier obtains the real-time electricity price of the node where the factory is located, and multiplies the real-time electricity price, the energy efficiency attenuation coefficient, and the equipment unit maintenance cost conversion factor to obtain the marginal cost per unit volume. The gas supply end generates the supply quotation based on the marginal cost and the preset profit rate, and determines the available gas quantity and pressure level based on the current remaining adjustable capacity of the equipment and the outlet pressure range. The energy storage unit calculates the weighted average cost of the tokens corresponding to the current gas storage based on the current gas storage pressure, gas quality, and the average price of the tokens purchased in the previous cycle. The supply quotation is then generated by adding energy storage loss compensation to the weighted average cost. Simultaneously, the available gas quantity and pressure level are determined based on the upper limit of the sustainable venting rate and the lower limit of the safe gas storage pressure of the gas storage tank.

[0018] Furthermore, the two-way auction mechanism specifically includes: During each auction cycle, the auction room simultaneously accepts sell orders from gas suppliers and energy storage providers, as well as buy orders from gas consumers. The sell orders specify the seller, available quantity, pressure level, and supply price, while the buy orders specify the buyer, quantity range, pressure constraints, time constraints, and the highest acceptable price. The auction room matches and clears all valid buy and sell orders based on the current auction period. At the end of the auction period, the auction room broadcasts the clearing results to all participants, including the clearing price for this round, the volume of each participant's transaction, and the settlement time window. The remaining buy and sell orders that failed to be matched in this round may be carried over to the next auction cycle for rematching, or cancelled, depending on the instructions of the participating parties.

[0019] Furthermore, the conservation audit process specifically includes: Within each preset audit period, the total number of tokens generated by the token generator of the high-pressure gas supply network is collected and denoted as G; Collect the total number of tokens destroyed by all gas-using terminals, and denote it as C; The total amount of residual tokens collected and recycled by the recycling center is denoted as G'; Based on the changes in pipeline gas storage pressure and temperature monitored by the agent at each pipeline segment node, the token conversion amount corresponding to the change in dynamic gas storage volume of the pipeline network within the current audit period is calculated and denoted as ΔS; the agent at each pipeline segment node refers to an intelligent monitoring unit deployed at pipeline segment valves, branch nodes and pipe diameter changes, used to collect the gas pressure, temperature and flow rate of the pipeline segment in real time. Based on the difference between the incoming and outgoing tokens of each pipe segment node agent, identify and calculate the leakage loss token amount of the marked leaking pipe segment, denoted as L; Based on the above calculations, verify whether the equation G+G'=C+R+ΔS+L holds true. If the difference between the two sides of the equation exceeds a preset threshold, the conservation audit is deemed to fail; otherwise, the conservation audit is deemed to pass.

[0020] Furthermore, the identification of leaking pipe segments based on the difference between the inbound and outbound tokens of each pipe segment node agent specifically includes: The node agents deployed in each pipeline segment record in real time the total value of all energy tokens flowing through their upstream interface during the audit period, which is recorded as the inlet token quantity. They also record the total value of all energy tokens flowing through their downstream interface, which is recorded as the outlet token quantity. The total amount of tokens consumed and destroyed by the gas-using terminals under the jurisdiction of the pipeline segment during the audit period is obtained and recorded as the pipeline segment consumption. Calculate the difference between the inbound and outbound tokens for the pipeline node agent. The difference between the inbound and outbound tokens is the remainder obtained by subtracting the outbound token amount from the inbound token amount and then subtracting the pipeline consumption amount. The upper limit of the historical fluctuation range of the inbound and outbound token difference under normal operating conditions of the pipeline node agent is obtained. The upper limit of the historical fluctuation range is determined based on the statistical quantile of the inbound and outbound token difference samples of a preset number of historical audit periods. When the absolute value of the difference between the incoming and outgoing tokens exceeds the upper limit of the historical fluctuation range for a consecutive preset number of audit periods, the pipe segment is marked as a leaking pipe segment.

[0021] Furthermore, the triggering of isolation controls based on audit results specifically includes: When the conservation audit fails, the difference between the incoming and outgoing tokens of each subnet and each segment node agent is compared sequentially downwards. When the absolute value of the difference between the incoming and outgoing tokens continuously exceeds the upper limit of the historical fluctuation unit under normal operating conditions of the pipe segment node agent, the pipe segment corresponding to the difference between the incoming and outgoing tokens is determined to be a faulty pipe segment. Trigger the closing command of the electric shunt valves at both ends of the faulty pipe section to physically isolate the faulty pipe section from the pipe network; The scheduling layer updates the pipeline topology model during the generation of the gas supply contract based on the isolation status information of the faulty pipeline segment, bypassing the gas supply path associated with the faulty pipeline segment, and completing closed-loop control.

[0022] Secondly, a closed-loop control system for the gas supply and consumption ends of a zero-carbon factory is provided, including: The token generation module is used to quantify the energy value of the output gas into an energy token at the gas supply source; the energy value represents the maximum energy value that the gas can be converted into useful work under a given pressure, temperature and flow rate. The air-end execution module is used to output the required pressure of the device using the flow regulating and pressure regulating valve and calculate the amount of energy consumed, and destroy the corresponding number of energy tokens according to the calculated amount of energy. The scheduling module is used to receive the demand envelope, which includes the volume range and pressure constraints, broadcast by each gas user, as well as the supply quotations from the gas supplier and energy storage end. It then uses a two-way auction mechanism to match the demand envelope and supply quotations in real time to generate a gas supply contract. The two-way auction mechanism refers to a market mechanism in which buyers and sellers submit quotations and demands simultaneously within the same auction period, and all quotations are centrally matched and cleared according to a preset optimization target. The audit isolation module is used to perform token quantity conservation audit based on the total number of tokens generated by the token generation module, the total number of tokens destroyed by each gas user, the total number of residual tokens recovered by the end of the recycling pipeline, and the token conversion amount corresponding to the gas storage in the pipeline, and to trigger isolation control based on the audit results.

[0023] Compared with the prior art, the beneficial effects of this application are: By constructing a dual-network architecture of a high-pressure air supply network and a low-pressure recovery network, and placing the gas regeneration center at the end of the low-pressure recovery network to operate gas-electricity, gas-heat, and gas-gas cycles in parallel, this application enables the cascade recovery and reuse of the pressure energy, heat energy, and working fluid carried by the low-pressure exhaust gas that was originally directly discharged into the atmosphere. This transforms the traditional open pneumatic system into a closed-loop system from a physical topology perspective, significantly reducing the energy loss and carbon emissions throughout the entire lifecycle of the compressed air system. Simultaneously, by using energy tokens protected by hardware security modules as carriers, the gas quality, generation timestamp, pressure and temperature parameters, and hash values ​​of each unit of gas are encapsulated into an immutable data structure. Furthermore, by establishing a synchronous mapping relationship between token flow and airflow using token audit gateways deployed at each node of the network, the energy quality, transmission delay, and synchronization deviation at any location in the network can be monitored and verified in real time. This fundamentally solves the problems of disconnected information flow and energy flow, and the lack of visibility of end-user energy consumption in traditional pneumatic systems.

[0024] By integrating an edge computing unit into the flow and pressure regulating valve, the energy consumption of each gas usage action is calculated in real time based on the state parameters of intake and exhaust. Following the principle of "one energy consumption, one token," the system marks the full consumption of tokens, splits tokens for partial consumption, and recasts tokens for remaining energy value. This application achieves autonomous, accurate metering and real-time confirmation of energy-consuming terminals, ensuring that the destination of each energy conversion has a traceable and verifiable digital certificate, eliminating the blind spots in energy efficiency accounting caused by the lack of accurate end-point metering in traditional pneumatic systems. Furthermore, by introducing a demand envelope that includes energy consumption range, minimum supply pressure, allowable time delay range, and a default cost function, the system empowers gas-consuming terminals to express their energy consumption elasticity and production value loss when participating in two-way auctions. This allows the gas supply contract matching and clearing process to no longer rely on rigid preset values, but instead automatically matches supply and demand with the goal of maximizing social welfare while meeting physical constraints, thereby achieving distributed optimal configuration of the system under uncertain production cycles.

[0025] By incorporating the total number of tokens generated, the amount destroyed at gas terminals, the amount recovered by the gas regeneration center, the converted amount of changes in gas storage capacity in the pipeline network, and the amount of marked leakage losses into a unified conservation audit, and by comparing the difference between the incoming and outgoing tokens of each pipeline segment node at each level along the sub-network hierarchy when the audit deviation exceeds the limit in order to locate the faulty pipeline segment, trigger physical isolation, and update the pipeline network topology model of the scheduling layer, this application establishes for the first time a closed-loop cross-validation and fault self-healing mechanism for energy flow and information flow in a pneumatic system. This enables any unmetered leakage, metering drift, or abnormal loss to be detected, located, and automatically isolated in real time. While ensuring normal gas supply in non-faulty areas to the maximum extent, it minimizes the energy waste and carbon emission impact caused by faults, significantly improving the operational safety and robustness of the system. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A system architecture diagram of a closed-loop control system for the gas supply and consumption ends of a zero-carbon factory, provided in an embodiment of this application; Figure 2 A schematic flowchart illustrating a closed-loop control method for the gas transmission and consumption ends of a zero-carbon plant, provided in an embodiment of this application; Figure 3 A schematic diagram of the process for generating energy tokens provided in an embodiment of this application; Figure 4 A schematic diagram illustrating the process of generating a gas supply contract provided in an embodiment of this application; Figure 5 This is a flowchart illustrating the token conservation audit and closed-loop control provided in an embodiment of this application. Detailed Implementation

[0028] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0029] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0030] The closed-loop control method for the gas supply and consumption ends of a zero-carbon plant provided in this application embodiment can be applied to, for example... Figure 1 In a zero-carbon factory gas supply and consumption closed-loop control system, as shown, Figure 1 As shown, the system includes: The token generation module is used to quantify the energy value of the output gas into an energy token at the gas supply source; where the energy value represents the maximum energy value that the gas can be converted into useful work under a given pressure, temperature and flow rate. The air-end execution module is used to output the required pressure of the device using the flow regulating and pressure regulating valve and calculate the amount of energy consumed, and destroy the corresponding number of energy tokens according to the calculated amount of energy. The scheduling module is used to receive the demand envelope, which includes the volume range and pressure constraints, broadcast by each gas user, as well as the supply quotations from the gas supplier and energy storage end. It then uses a two-way auction mechanism to match the demand envelope and supply quotations in real time to generate a gas supply contract. The two-way auction mechanism means that within the same auction period, buyers and sellers submit quotations and demands simultaneously, and all quotations are centrally matched and cleared according to a preset optimization target. The audit isolation module is used to perform token quantity conservation audit based on the total number of tokens generated by the token generation module, the total number of tokens destroyed at each gas consumption end, the total number of residual tokens recovered at the end of the recycling network, and the token conversion amount corresponding to the gas storage in the network, and to trigger isolation control based on the audit results.

[0031] To address the technical problems in existing zero-carbon factory compressed air systems, such as the lack of precise energy metering between air supply and consumption, inability to match supply and demand in real time, low energy utilization efficiency, and difficulty in achieving closed-loop control, this application provides a closed-loop control method for the air supply and consumption ends of a zero-carbon factory. The method includes: A token generator is set up at the gas supply source to quantify the energy value of the output gas into an energy token; where the energy value represents the maximum energy value that the gas can be converted into useful work under a given pressure, temperature and flow rate. The system uses the pressure regulating valve at the gas end to output the required pressure and calculates the amount of energy consumed, and then destroys the corresponding number of energy tokens based on the calculated amount of energy consumed. The dispatch layer receives demand envelopes, including volume ranges and pressure constraints, broadcast by each gas user, as well as supply quotations from the gas supply and energy storage ends. It then uses a two-way auction mechanism to match the demand envelopes and supply quotations in real time to generate gas supply contracts. The two-way auction mechanism means that within the same auction cycle, buyers and sellers submit quotations and demands simultaneously, and all quotations are centrally matched and cleared according to a preset optimization target. Based on the total number of tokens generated by the token generator, the total number of tokens destroyed at each gas-consuming end, the total number of residual tokens recovered at the end of the recycling network, and the token conversion amount corresponding to the gas storage volume in the network, a token quantity conservation audit is performed, and isolation control is triggered based on the audit results.

[0032] Based on this, the method provided in this application establishes a unified energy measurement standard from the gas supply source to the gas consumption end by quantifying the energy value of compressed air into energy tokens. This enables quantifiable, traceable, and auditable closed-loop energy management capabilities between the gas supply end and the gas consumption end. By destroying the corresponding tokens at the gas consumption end according to the actual energy consumption, precise energy consumption accounting of "how much is used, how much is consumed" is achieved, eliminating energy waste caused by the inability to accurately measure energy in traditional compressed air systems. Through a two-way auction mechanism at the scheduling layer, supply and demand are matched in real time, achieving dynamic supply and demand balance and optimal resource allocation between the gas supply end, energy storage end, and gas consumption end. Through token quantity conservation auditing and isolation control, energy conservation and safe operation of the entire system process are ensured, providing a complete closed-loop control solution for zero-carbon factory compressed air systems.

[0033] like Figure 2 As shown in the embodiment of this application, a closed-loop control method for the gas transmission and consumption ends of a zero-carbon plant includes: S1. Set up a token generator at the gas supply source to quantify the energy value of the output gas into energy tokens.

[0034] The value of ν represents the maximum energy that a gas can convert into useful work under given pressure, temperature, and flow rate conditions.

[0035] Traditional energy analysis focuses solely on the quantity of energy, neglecting its quality. Compressed air, as an energy carrier, depends not only on its total energy but also on its convertible work capacity, determined by its pressure, temperature, and other state parameters. Energy quality analysis (EQA), based on the second law of thermodynamics, considers both quantity and quality, comprehensively analyzing energy utilization and loss. In industrial compressed air systems, due to irreversible factors such as pressure loss, flow friction, and leakage, a significant amount of energy is wasted as a reduction in quality rather than quantity. This quality loss cannot be identified and quantified using traditional energy values; however, using EQA as a measurement benchmark accurately reflects the effective work capacity of compressed air under actual operating conditions, providing a basis for precise energy accounting and energy-saving optimization. Therefore, using EQA brings previously overlooked energy quality issues into the management purview, transforming energy management in zero-carbon plants from quantitative statistics to quality-based accounting.

[0036] In some implementations, the token generator can be deployed at the outlet of an air compressor or gas storage tank, integrating pressure, temperature, and flow sensors to collect real-time data on the pressure, temperature, and flow rate of the output gas. The token generator can have a built-in computing unit that, based on the gas thermodynamic equation of state and the ΔV calculation model, substitutes the collected real-time parameters into the ΔV calculation formula to calculate the ΔV of the current output gas, and converts the ΔV into integer or floating-point energy tokens according to a preset token exchange rate. The token generator can record the generated energy tokens in local storage and simultaneously upload them to the scheduling layer or a blockchain distributed ledger, ensuring the credibility and traceability of the token generation records.

[0037] It should be noted that the total number of tokens generated by the token generator should be used as the baseline value for the energy input of the entire compressed air system. All token consumption at downstream air-consuming ends, token recovery in the recycling network, and token conversion of the network's air storage volume should be calculated with reference to this baseline value. The exchange rate of the token generator can be dynamically adjusted according to the energy efficiency characteristics and operating conditions of the air-supplying equipment. For example, when the air compressor's operating efficiency decreases, the number of tokens corresponding to the unit value can be adjusted accordingly to reflect the change in the actual energy conversion efficiency at the air-supply end.

[0038] S2. Utilize the pressure regulating valve at the gas end to output the required pressure of the equipment and calculate the amount of energy consumed, and destroy the corresponding number of energy tokens based on the calculated amount of energy consumed.

[0039] In traditional compressed air systems, energy consumption at the user end is typically estimated using indirect indicators such as volumetric flow rate or pressure drop, which cannot accurately reflect the actual effective energy usage. By linking actual energy consumption to token destruction, each unit of effective energy consumption corresponds to the destruction of a token, making energy consumption measurable, traceable, and auditable. This mechanism also constitutes an energy constraint at the user end—the user can only consume the amount corresponding to the token they hold; energy demands exceeding the token holding limit cannot be met, thus fundamentally preventing excessive energy consumption and waste.

[0040] In some implementations, the energy consumption is calculated as follows: Pressure sensors and temperature sensors are installed at the inlet and outlet of the flow regulating valve, respectively, to collect real-time pressure and temperature data before and after the valve. During the process of adjusting the gas supply pressure to the required pressure at the gas-consuming end, the gas undergoes throttling and expansion, resulting in changes in both pressure and temperature. According to the energy balance equation for a thermodynamic open system, the difference in energy values ​​before and after the gas flows through the flow regulating valve is the actual energy consumption at the gas-consuming end. Specifically, the energy value of the gas before the valve at the inlet pressure and temperature is calculated, and the energy value of the gas after the valve at the outlet pressure and temperature is calculated; the difference between the two is the energy consumption during the flow regulating process. This energy consumption includes the actual utilization of gas pressure energy and the irreversible loss during the throttling process, comprehensively reflecting the actual situation of the gas-consuming end extracting effective energy from compressed air. The calculated amount of gas is converted into the number of tokens to be destroyed at the same exchange rate as the token generator. After confirming that the gas consumption is completed, the token manager at the gas-consuming end destroys the corresponding number of tokens from the local wallet and uploads the destruction record to the scheduling layer or the blockchain distributed ledger.

[0041] It should be noted that if the number of tokens held by the gas user is insufficient to cover the amount of gas consumed in this transaction, the token manager should refuse to perform the destruction operation and report an alarm message indicating insufficient tokens to the scheduling layer. The scheduling layer can then trigger a gas supply interruption or flow restriction control on the gas user.

[0042] S3. The dispatch layer receives the demand envelope, which includes the volume range and pressure constraints, broadcast by each gas user, as well as the supply quotations from the gas supplier and energy storage end. It then uses a two-way auction mechanism to match the demand envelope and supply quotations in real time to generate a gas supply contract.

[0043] The demand range represents the range of demand values ​​required by the gas consumer in the next scheduling cycle. It is typically given as a minimum demand range and a maximum demand range, characterizing the elasticity of the gas consumer's energy demand—the minimum demand range ensures basic production operation, while the maximum demand range reflects the upper limit of energy consumption for full-load operation. The introduction of the demand range allows the gas consumer to provide demand-side responsiveness while ensuring production; that is, it can flexibly adjust actual energy consumption within the range based on the gas supplier's supply capacity and price signals, thus providing adjustment space for the scheduling layer's optimal resource allocation.

[0044] Pressure constraints represent the minimum gas pressure requirements of the gas consumer, typically expressed as a minimum permissible gas supply pressure. In a two-way auction mechanism, pressure constraints participate in the matching process as a technical feasibility constraint—only supply bids with a pressure not lower than the gas consumer's pressure constraint can enter the candidate matching set for that gas consumer. The introduction of pressure constraints ensures that the auction result is not only economically optimal but also technically meets the normal operation requirements of each gas consumer's equipment. The demand envelope reflects the comprehensive energy demand information of the gas consumer in the next scheduling cycle, including dimensions such as capacity range, pressure constraints, gas usage time period, gas usage priority, and interruptibility; it is a complete digital description of the gas consumer's energy demand. The supply bid reflects the capacity that the gas supplier and energy storage unit can supply, the available gas pressure, and the corresponding bid price in the next scheduling cycle; it is a complete digital description of the supply capacity.

[0045] The advantages of using a two-way auction mechanism for supply and demand matching are as follows: Within the same auction cycle, buyers (gas users) and sellers (gas suppliers and energy storage providers) simultaneously submit their respective bids and demands. The scheduling layer then centrally matches and clears all bids according to preset optimization objectives. Compared to traditional methods of bidding first and then matching or unilateral pricing, this approach offers higher market efficiency and fairness—the information of both buyers and sellers is presented transparently and openly within the same time window, avoiding resource misallocation caused by information asymmetry. Furthermore, the two-way auction mechanism can simultaneously handle complex matching problems involving multiple buyers and sellers within a single framework, achieving globally optimal rather than locally optimal resource allocation through a centralized optimization algorithm.

[0046] In some implementations, a two-way auction mechanism may include the following process: At the beginning of each auction cycle, the scheduling layer collects the demand envelope broadcast by all gas users and the supply quotations broadcast by all gas suppliers and energy storage providers, constructing a bid set for both buyers and sellers based on the collected information. For seller bids, a supply curve is constructed by sorting the quotations from low to high; for buyer bids, a demand curve is constructed by sorting the quotations from high to low. The intersection of the supply curve and the demand curve represents the market clearing price and clearing quantity.

[0047] Building upon this, the scheduling layer further incorporates pressure constraints as a technical feasibility constraint into the matching process. After determining the initial matching scheme, it verifies one by one whether the pressure provided by the gas supplier in each matching pair meets the pressure constraint of the gas consumer. If not, the matching pair is marked as infeasible and removed from the candidate schemes, and the matching calculation is repeated until all matches meet the pressure constraint. The final generated gas supply contract includes information such as gas supplier identifier, gas consumer identifier, gas supply volume, gas supply pressure, gas supply period, and transaction price.

[0048] It should be noted that the optimization objectives of the two-way auction mechanism can be flexibly configured according to the actual operational needs of the zero-carbon plant. For example, it can be set as minimizing total energy costs, maximizing total energy efficiency, minimizing carbon emissions, or a weighted combination of multiple objectives. The length of the auction cycle can be dynamically adjusted according to the system scale and gas consumption fluctuation characteristics. For example, short-cycle auctions at the second or minute level can be used for production lines with drastic gas consumption fluctuations, while long-cycle auctions at the hour level can be used for production lines with relatively stable gas consumption.

[0049] S4. Based on the total number of tokens generated by the token generator, the total number of tokens destroyed at each gas-consuming end, the total number of residual tokens recovered at the end of the recycling network, and the token conversion amount corresponding to the gas storage in the network, perform a token quantity conservation audit and trigger isolation control based on the audit results.

[0050] Token conservation auditing, based on the principle of energy conservation, involves the periodic calculation and verification of token generation, consumption, recovery, and inventory throughout the entire compressed air system. The purpose of this audit is to ensure that the energy flow throughout the compressed air system, from the air supply source to the end-user and the recovery network, adheres to the law of energy conservation. This verifies the accuracy and consistency of energy measurement, token generation, token destruction, and token recovery at every stage of the system. In a zero-carbon factory's compressed air system, if a measurement deviation, data tampering, or equipment malfunction occurs in any stage, token conservation auditing can promptly detect and locate the problem through abnormal signals indicating an imbalance between supply and demand, thus ensuring the safe operation and reliable management of the system.

[0051] In some implementations, token conservation auditing is performed using the following formula: Total tokens - Gas supply = Total tokens - Destroyed + Total tokens - Recycled + Total tokens - Pipeline inventory; Among them, "Total Tokens_Gas Supply" refers to the total number of tokens generated by the token generator since the system started; "Total Tokens_Destroyed" refers to the total number of tokens destroyed by all gas users since the system started; "Total Tokens_Recovered" refers to the total number of residual tokens recovered by the end of the recovery pipeline since the system started. The residual energy recovery device installed at the end of the recovery pipeline converts the residual energy value in the exhaust gas emitted by the gas users, which still has the ability to perform work, into recovered tokens at the same exchange rate; "Total Tokens_Pipeline Stock" refers to the number of tokens converted from the energy value corresponding to the compressed air stored in all gas storage facilities (including main pipelines, branch pipelines, gas tanks, etc.) in the pipeline at the current moment, using the same exchange rate. The above four quantities should satisfy the conservation equation.

[0052] If the audit results satisfy the conservation equation (allowing a certain measurement error threshold, such as ±0.5%), the system is considered to be operating normally, and the scheduling and control for the next cycle continues. If the audit results show a significant deviation, the system is considered to be abnormal, triggering isolation control.

[0053] It should be noted that isolation control can trigger different levels of isolation measures based on the type and severity of the anomaly: for minor deviations (e.g., deviations within 1%), early warning-level isolation control can be triggered, sending alarm information only to maintenance personnel; for moderate deviations (e.g., deviations between 1% and 5%), local isolation control can be triggered, isolating the abnormal gas-consuming or supplying end from the dispatch network, with the backup gas supplying end or energy storage end taking over its gas supply task; for severe deviations (e.g., deviations exceeding 5%), global isolation control can be triggered, immediately cutting off the gas supply output from all gas supplying ends, while simultaneously initiating emergency pressure relief procedures to ensure system safety. The triggering of isolation control is not only a passive response to abnormal states, but also the last line of defense for the entire zero-carbon plant's compressed air system—through timely isolation and disconnection, preventing energy runaway or safety accidents caused by metering errors, equipment failures, or malicious attacks.

[0054] Based on the above technical solutions, this application provides a closed-loop control method for the gas supply and consumption ends of a zero-carbon factory. By quantifying the value of compressed air into energy tokens and generating them at the gas supply source, destroying them at the gas consumption end according to actual consumption, and recovering them as residual energy at the end of the recycling network, a unified energy measurement and accounting system covering the entire life cycle of the compressed air system is established, solving the fundamental problem that traditional compressed air systems cannot accurately measure effective energy consumption. Through a two-way auction mechanism at the scheduling layer, the demand envelope at the gas consumption end and the supply price at the gas supply end are matched in real time, realizing the optimal matching of multiple objectives between the supply and demand sides in terms of energy quantity, pressure level, time dimension, and price dimension, solving the scheduling problem that traditional compressed air systems cannot dynamically match supply and demand in real time. Through token quantity conservation auditing and hierarchical isolation control, a complete safety closed loop from energy measurement to anomaly detection to safety isolation is constructed, providing a systematic solution for the safe, efficient, and reliable operation of the compressed air system in a zero-carbon factory.

[0055] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, the above S1 can be implemented through the following S101, S102 and S103, which are explained in detail below: S101. Set up a standard metering station at the gas supply source to collect the pressure, temperature and mass flow rate of the output gas in real time.

[0056] The standard metering station is a comprehensive measurement unit located at the outlet main pipe of the air compressor station or air storage tank. Its function is to provide raw state parameters for subsequent value calculation.

[0057] In some implementations, the standard metering station can consist of a separate Coriolis mass flow meter, pressure transmitter, and temperature sensor, or it can be an integrated gas metering skid-mounted device that integrates all of the above measurement functions. The mass flow meter can be selected with an accuracy class of 0.1 or 0.2, the pressure transmitter can be selected with an accuracy of 0.075% of full scale, and the temperature sensor can be a Class A platinum resistance thermometer.

[0058] It should be noted that the installation location of the standard metering station should be selected in a straight pipe section at the outlet of the air compressor station, where the flow field is sufficiently developed to accommodate a length of 10 times the pipe diameter upstream and 5 times the pipe diameter downstream. When site space is limited and the straight pipe section requirement cannot be met, a flow conditioner should be installed upstream of the flow meter for compensation.

[0059] S102. Calculate the output gas value according to the collected pressure, temperature and mass flow rate using a predetermined formula.

[0060] The calculation of specific enthalpy follows the enthalpy analysis theory of steady flow systems in thermodynamics. For working fluids such as air, which can be regarded as ideal gases, the specific enthalpy can be calculated by using the changes in specific enthalpy and specific entropy relative to the environmental reference state.

[0061] In some implementations, the formula for calculating the value of ⇲ can take the following complete form: ;in, E is the total work done by the gas, expressed in kilojoules, representing the maximum useful work that the gas can theoretically output relative to the environmental reference state under the current condition; m is the mass of the gas, expressed in kilograms, obtained by integrating the mass flow rate over time. e is the specific enthalpy of the gas, expressed in kilojoules per kilogram, representing the enthalpy value per unit mass of gas; h is the specific enthalpy of the gas under the current pressure and temperature, expressed in kilojoules per kilogram; h0 is the specific enthalpy of the gas under environmental reference conditions, expressed in kilojoules per kilogram; T0 is the environmental reference temperature, expressed in Kelvin, taken as the annual average temperature of the factory location; s is the specific entropy of the gas under the current pressure and temperature, expressed in kilojoules per kilogram per Kelvin; s0 is the specific entropy of the gas under environmental reference conditions, expressed in kilojoules per kilogram per Kelvin.

[0062] When the gas can be considered an ideal gas and the specific heat capacity can be approximated as constant within the operating temperature range, the above formula for calculating the specific heat capacity can be further expanded as follows: ;in, This represents the specific heat capacity of a gas at constant pressure, which is taken as 1.005 kJ / kg / Kelvin for air. T represents the current gas temperature in Kelvin; T0 represents the ambient reference temperature, which is 298.15 Kelvin (25 degrees Celsius) by default; R represents the gas constant, which is 0.287 kJ / kg / Kelvin for air; p represents the current absolute gas pressure in kPa; p0 represents the ambient reference pressure, which is 101.325 kPa (1 atmosphere) by default.

[0063] The first term of the formula The second item represents the portion of heat contained in a gas when its temperature is higher than the ambient temperature. The entropy change of the gas due to the temperature and pressure differences from the reference state is subtracted from its work capacity. The remaining value after subtracting the two parts is the maximum energy that a unit mass of gas can theoretically be converted into mechanical work under the current state.

[0064] For example, under operating conditions of 800 kPa pressure, 323.15 Kelvin temperature, 298.15 Kelvin ambient reference temperature, and 101.325 kPa ambient reference pressure, substituting into the above formula yields a specific velocity (E) of approximately 127.8 kJ per kilogram. If the standard metrology station measures 0.5 kg of gas flowing through the gas in a 1-second sampling period, then the total specific velocity (E) of the output gas in that sampling period is 0.5 × 127.8, or 63.9 kJ.

[0065] S103. The calculated energy value is quantified into an energy token. The token generator forges an energy token every time the accumulated energy value reaches a preset energy threshold.

[0066] The process of quantifying gas vapor value into energy tokens involves dividing the continuously accumulated gas vapor value into discrete digital token units according to a preset quantization step size. Each energy token corresponds to a fixed number of vapor values. This quantization operation transforms the continuous energy flow in the physical domain into a discrete token sequence that can be identified individually, tracked independently, and accurately calculated in the information domain. This provides a basic counting unit for subsequent token transmission with the airflow, end-point consumption and destruction, and end-to-end conservation auditing.

[0067] In some implementations, the token generator internally maintains a value accumulation register. This register receives the current value increment calculated in step S102 each sampling period and sums it with the remaining accumulation at the end of the previous sampling period to obtain the current total accumulation. The token generator continuously determines whether the current total accumulation reaches or exceeds a preset token unit baseline value. If it does, it forges an energy token, deducts the baseline value from the accumulation, and carries over the remaining accumulation to the next sampling period for continued accumulation. The token unit baseline value can be 100 kilojoules.

[0068] Each forged energy token contains the following data fields: a globally unique identifier, composed of a token generator serial number and a monotonically increasing counter; the base quantity represented by the token, i.e., the preset base quantity of the token unit; a generation timestamp, recording the precise moment when the token was forged, with the time source being the global navigation satellite system time synchronization or the master clock of a time-sensitive network; the pressure and temperature at the generation point, taken as the average of the pressure and temperature within the current sampling period; and the hash value of the previous token, which is calculated by using a secure hash algorithm such as SHA-256 from the entire payload of the previous token, forming an immutable hash chain from the initial token to the latest token.

[0069] For example, suppose the base energy of the token unit is set to 100 kJ, the remaining accumulated energy at the end of the previous sampling period is 45.2 kJ, and the energy increment calculated in the current sampling period is 63.9 kJ. Then the current total accumulated energy is 45.2 plus 63.9 equals 109.1 kJ. This value is greater than 100 kJ, so the token generator forges an energy token and assigns it a unique identifier such as TK-20250731-00038, records the generation timestamp as July 31, 2025, 14:32:05:023, the generation point pressure is 800 kPa, and the temperature is 323.15 Kelvin. The SHA-256 hash value of the previous token TK-20250731-00037 is calculated and written into the hash chain field of the current token. After deducting the base energy, 9.1 kJ remain, which is carried over to the next sampling period. The forged energy token data packet is then sent via the network interface to the communication link that is synchronously transmitted with the outlet of the high-pressure gas supply network. The token is transmitted downstream of the network along with the corresponding gas micro-element.

[0070] Based on the above technical solution, by collecting physical state parameters, calculating energy values, and minting discrete tokens at the gas supply source, this step achieves lossless mapping and verifiable anchoring of physical energy to digital credentials. The token generator, as the sole reliable source of energy information for the entire system, is protected by a hash chain structure and hardware security modules. It provides immutable and traceable metering units for the subsequent circulation, consumption, recycling, and auditing of all tokens, ensuring the information integrity and reliability of the closed-loop control method.

[0071] In one possible implementation of this application embodiment, the above-mentioned S2 can be specifically implemented by the following S201, S202 and S203, which are described in detail below: S201. When the flow regulating and pressure regulating valve performs a gas consumption action, it collects the gas state parameters entering the valve group and exiting the valve group in real time.

[0072] Among these, gas state parameters are the original data source for calculating the energy consumption value at the gas consumption end. Unlike S101, which performs centralized metering at the gas supply source, S201's data collection occurs within the valve assembly of each gas consumption terminal, belonging to distributed end-point metering. Collecting state parameters from both the inlet and outlet sides, rather than only the inlet side, is key to accurately calculating the energy consumption value—because the gas discharged after pneumatic equipment performs work still carries considerable residual pressure energy and heat energy. If this residual energy is not deducted, the energy consumption measurement will be underestimated, failing to truly reflect the energy conversion efficiency.

[0073] In some implementations, the flow regulating and pressure regulating valve integrates two sensor arrays: one for the intake side and one for the exhaust side. The intake-side sensor array includes a miniature pressure transmitter, a thin-film thermocouple temperature probe, and a thermal mass flow microsensor, used to collect the intake pressure, temperature, and mass flow rate, respectively. The exhaust-side sensor array is configured identically to the intake-side array, used to collect the corresponding exhaust parameters. All sensors are connected to the edge computing unit via the valve assembly's built-in signal conditioning circuitry.

[0074] It should be noted that for simple pneumatic actuators with only a single air inlet and a single atmospheric outlet, the exhaust side parameters can be simplified to collecting the pressure and temperature at the back pressure chamber or exhaust muffler, and the mass flow rate can be indirectly estimated by the difference between the intake flow rate and the cylinder volume change rate. For complex valve groups with independent exhaust channels, independent flow measurement points should be set on the exhaust channels to ensure measurement accuracy.

[0075] S202. Based on the collected intake and exhaust state parameters, calculate the input ΔV carried by the intake and the output ΔV carried by the exhaust respectively, and take the difference between the two as the ΔV consumed by the current air consumption action.

[0076] In some implementations, the input and output VV values ​​are calculated using the same ratio formula as in S102. For the intake side, the input VV value is calculated as follows: first, the intake mass is calculated by integrating the intake mass flow rate over the duration of the air consumption action; then, the intake ratio VV is calculated based on the intake temperature and pressure; finally, the intake mass is multiplied by the ratio VV to obtain the input VV value. For the exhaust side, the output VV value is calculated similarly, by multiplying the exhaust mass and the exhaust ratio VV. The consumption VV value is the difference between the input and output VV values.

[0077] Since the actual work capacity consumed by gas-using equipment is the portion extracted from high-pressure gas and converted into mechanical work, not the total energy carried by the intake gas; the residual energy carried by the exhaust gas is the unused portion and should be retained in the downstream recycling network for reuse, rather than being included in consumption, the difference calculation method is used. The complete calculation formula can be expressed as: ; ; ; in, This indicates the amount of gas consumed in the current action, measured in kilojoules. This indicates the input air volume carried by the intake air, measured in kilojoules. This indicates the output value carried by the exhaust gas, in kilojoules. This represents the intake air mass, expressed in kilograms, and is obtained by integrating the intake air mass flow rate over the duration of the action. The exhaust mass is expressed in kilograms and is obtained by integrating the exhaust mass flow rate over the duration of the action. The intake ratio is expressed in kilojoules per kilogram. The ratio of exhaust gas is expressed in kilojoules per kilogram. , This indicates the intake air temperature and pressure, with units of Kelvin and kilopascals, respectively. , The temperature and pressure of the exhaust gas are expressed in Kelvin and kilopascals, respectively. The meanings of T0, R, and p0 are the same as in S102, representing the isobaric specific heat capacity of air. The gas constant R is set to 1.005 kJ / kg / Kelvin, the ambient reference temperature T0 is set to 298.15 Kelvin by default, and the ambient reference pressure p0 is set to 101.325 kPa by default.

[0078] It should be noted that when the pneumatic action is intermittent and brief, and the duration of the action is much shorter than the response time constant of the temperature sensor, the gas temperature during the action can be assumed to be constant, and the temperature reading at the start of the action can be used as an approximation. When the action duration is long and the temperature change is significant, the gas temperature should be integrated over time segments to obtain a more accurate gas temperature calculation result. Furthermore, for pneumatic equipment with external leakage, the exhaust mass may be slightly less than the intake mass. The gas temperature loss corresponding to this mass difference will be automatically included in the gas temperature consumption and identified through the leakage loss item in subsequent conservation audits.

[0079] S203, the edge computing unit built into the flow regulating and pressure regulating valve destroys a corresponding number of tokens from the received energy tokens according to the calculated consumption amount, and generates residual tokens to be output with the exhaust.

[0080] In some implementations, the edge computing unit maintains a "queue of tokens to be consumed," arranged in the order in which the tokens arrive. After step S202 calculates the consumption amount of the current action, the edge computing unit executes the following destruction process: Starting from the head of the queue, tokens are taken out one by one, and the value of the current token is compared with the remaining amount of tokens to be destroyed. If the value of the token is less than or equal to the remaining amount of tokens to be destroyed, the token is marked as consumed, the value of the token is deducted from the remaining amount of tokens to be destroyed, and the next token is processed. If the value on the token is greater than the remaining amount to be destroyed, the token is split into two: one corresponding to the remaining amount to be destroyed, marked as consumed and destroyed; the other corresponding to the difference, is used as a change token to be reintroduced into the token stream for continued transmission downstream or for use in subsequent actions.

[0081] All tokens marked as consumed are digitally signed by the edge computing unit using the private key corresponding to the valve group. The signed tokens are then broadcast to the entire network and permanently removed from the valid token pool.

[0082] Meanwhile, the edge computing unit calculates the exhaust output value based on step S202. An equal number of residual tokens are minted locally. The residual tokens use the same unique identifier system as the energy tokens, but with an additional "Residual" type marker field to distinguish them from the energy tokens circulating in the high-pressure network. The residual tokens are then carried by the exhaust gas flow into the low-pressure recovery network, proceeding towards the regeneration center.

[0083] It should be noted that the token destruction operation should be executed in an atomic transaction manner. That is, the three actions of destroying the consumed token, generating the change token, and minting the remaining token must be completed within the same indivisible operation cycle. If any step fails, the entire transaction is rolled back to avoid data inconsistencies such as tokens being destroyed but the remaining tokens not being generated, or consumption being recorded but the tokens not being marked. Atomic transactions can be guaranteed through the local transaction logs and redo mechanisms of the edge computing unit.

[0084] For example, suppose the current action consumes a value of 2.623 kJ. In the edge computing unit's token queue, the first token, TK-00038, carries a value of 100 kJ. Since this value is greater than 2.623 kJ, a splitting operation is triggered: TK-00038 is split into two sub-tokens, TK-00038-1 and TK-00038-2. TK-00038-1, corresponding to the consumed 2.623 kJ, is signed using the valve group's private key, marked as consumed, broadcast to the entire network, and removed from the valid token pool. TK-00038-2, corresponding to the remaining 97.377 kJ, is kept at the head of the token queue as change tokens for use in the next action. Simultaneously, the edge computing unit, based on the exhaust output value of 1.189 kJ, forges a residual token RTK-00019, recording the residual amount of 1.189 kJ, which enters the low-pressure recovery pipeline with the exhaust gas flow.

[0085] Based on the above technical solution, this step synchronizes the energy consumption behavior in the physical domain with the token lifecycle management in the information domain by implementing distributed processing from status parameter acquisition and energy consumption calculation to token destruction and residual token recasting at each gas-consuming terminal. The difference calculation of energy consumption values ​​ensures the thermodynamic accuracy of end-point metering, the token splitting and zeroing mechanism ensures the flexibility of token metering, and atomic transactions ensure data consistency. This provides a reliable and accurate energy-side data foundation for subsequent market-based matching and end-to-end conservation auditing at the scheduling layer.

[0086] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 4 As shown, the above S3 can be implemented through the following S301, S302 and S303, which are explained in detail below: S301. Each gas-consuming end generates a demand envelope containing the quantity range and pressure constraints according to the action sequence to be executed, and broadcasts the demand envelope to the auction room of the corresponding subnet.

[0087] The demand envelope consists of a declaration of energy consumption intention submitted by the gas user to the system before participating in scheduling and matching. The energy range reflects the flexible space of energy required by the gas-consuming equipment to complete a specified process action—the lower limit represents the minimum energy required to guarantee the action's completion, and the upper limit represents the maximum energy supply allowed by the process. Pressure constraints specify the minimum pressure level that the gas supply must reach. The demand envelope also includes time constraints, namely the earliest and latest acceptable gas supply times for the action, used to match the delivery time on the gas supply side. By submitting this flexible range to the scheduling layer, the gas user transforms its tolerance for gas supply conditions into a system-schedulable flexible resource, reducing the probability of supply-demand mismatch caused by changes in production cycle time or forecast errors.

[0088] In some implementations, the specific process of generating the demand envelope using the gas end is as follows: The edge computing unit at the air end obtains the action sequence for the next 0.5 to 1 second from the machine tool controller, robot controller, or production line programmable logic controller through a real-time communication interface.

[0089] For each action to be executed in the action sequence, the edge computing unit calculates the required baseline energy consumption based on the historical energy consumption data of that action or an energy consumption prediction algorithm based on a physical model, and determines the upper and lower limits of the energy consumption range based on this. For example, the lower limit can be 90% of the baseline energy consumption, and the upper limit can be 120% of the baseline energy consumption. These two proportional coefficients are set according to the historical fluctuation statistics of this process step.

[0090] The minimum gas supply pressure in the pressure constraint is the lower limit of the rated working pressure specified on the equipment nameplate or in the process specification. The latest gas supply time in the time constraint is the time when the action plan is executed plus the maximum allowable delay, which is 50 milliseconds by default, but can be shortened to 10 milliseconds for critical workstations.

[0091] The demand envelope also includes a default cost function, which quantifies the production value loss that the gas user will suffer when the gas supply conditions do not meet the constraints within the demand envelope.

[0092] It should be noted that the generation of the demand envelope can be based entirely on explicit action instructions issued by the device controller, or it can be combined with statistical learning models to predict device actions.

[0093] In some implementations, the process of generating the requirement envelope includes the following steps: First, the edge computing unit at the gas-operating end establishes a data link with the upper-level controller via a real-time communication interface. Within each control cycle, the upper-level controller sends the sequence of actions to be executed within a preset time window to the edge computing unit in the form of structured data frames. These structured data frames contain action identifiers, action type codes, planned execution times, target positions or target force parameters, and action priority levels. The edge computing unit parses these data frames, extracts the key parameters for each action, and stores them in a local action cache queue, which is arranged in ascending order of planned execution times. The default length of the preset time window is 0.8 seconds.

[0094] Next, for each action in the action cache queue, the edge computing unit calls the energy consumption model corresponding to that action type to calculate its baseline energy consumption. The energy consumption model can be established using one of the following two methods or a combination of both: Method 1 is a historical statistical model, where the edge computing unit maintains a historical energy consumption database indexed by action type, recording the actual energy consumption samples of that type of action in the most recent preset number of executions, and taking the median of the samples as the baseline energy consumption. The preset number is 100 by default. Method 2 is a physical parameter model. For cylinder-type actuators, the formula for calculating the baseline energy consumption is: ;in, The reference volume is in kilojoules; The equivalent pressure corresponding to the average thrust required for the cylinder to perform its power stroke, in kilopascals, is obtained by dividing the target force by the effective piston area; A is the effective piston area in square meters; L is the stroke in meters. This is the rated pressure of the gas supply, expressed in kilopascals (kPa). The pressure is atmospheric pressure, which is set to 101.325 kPa by default; η is the energy conversion efficiency coefficient of the pneumatic system, which is set to 0.35 by default and can be calibrated according to the actual measured data of the equipment.

[0095] For other types of pneumatic equipment such as vacuum generators and pneumatic motors, the physical parameter model of their reference volume can be established separately according to their respective thermodynamic cycle characteristics, but all of them take the target parameters of the action and the gas supply conditions as inputs and the reference volume as the output.

[0096] Based on the baseline value, the edge computing unit determines the lower and upper limits of the value range according to the historical fluctuation statistics of this action type. Lower limit The calculation method is to multiply the baseline quantity by a lower limit proportional coefficient. upper limit The calculation method is to multiply the base quantity by the upper limit ratio coefficient. . and The calibration is performed using the following method: A sample set of actual energy consumption for this action type under normal operating conditions is extracted from the historical energy consumption database. The cumulative distribution function of the sample set is calculated, and the ratio of the 5th percentile value to the median is taken as the calibrated value. The default value is 0.90, and the ratio of the 95th percentile value to the median is used as the median. The default value is 1.20.

[0097] Then, the edge computing unit obtains the process constraint parameters associated with the current action from the device controller, including: minimum gas supply pressure. The parameters are taken from the equipment nameplate or process specifications, or 80% of the rated gas supply pressure is taken as a conservative default value; maximum allowable delay time. The default value for critical processes is 10 milliseconds, the default value for general processes is 50 milliseconds, and the default value for non-critical auxiliary processes is 100 milliseconds.

[0098] The edge computing unit simultaneously calculates the default cost function. The default cost function is used to quantify the economic losses caused when gas supply conditions deviate from gas demand during the auction matching process; its expression is: ;in, The cost of default is represented by p; the actual gas supply pressure is represented by p. This is the gas supply delay time, which is the difference between the actual gas supply time and the planned execution time. This represents the minimum gas supply pressure required at the gas consumption end. The maximum allowable delay time; α is the pressure deficiency penalty coefficient, reflecting the loss value corresponding to the square of a unit pressure deficiency, with a default value of 0.01; β is the delay baseline cost coefficient, reflecting the scaling factor of the exponential growth function of the delay time, with a default value of 0.05; γ is the delay time amplification coefficient, reflecting the growth rate of delay loss over time, with a default value of 0.1 per millisecond; δ is the stop flag weight, with a value of 0 or 1. When the process in which this action is performed is an uninterruptible critical process, δ is 1, otherwise it is 0. The value of production line downtime loss per unit time is calculated from the production line's output per unit time and product profit margin, with a default value of 100 internal settlement units per second; I(·) is an indicator function, which takes the value 1 when the condition in parentheses is true, and 0 otherwise.

[0099] The default cost function reflects the following engineering characteristics: the first term is the loss due to insufficient gas supply pressure, expressed in a square form to reflect the nonlinear deterioration effect of insufficient pressure on the output force and speed of pneumatic equipment; the second term is the loss due to gas supply delay, expressed in an exponential form to reflect the accelerated amplification effect of delay on production cycle time; the third term is the step loss caused by delay exceeding the tolerance limit, leading to production line shutdown, triggered by an indicator function. The calculated cost will serve as an important reference for the highest acceptable price for the gas user during the auction matching process.

[0100] Finally, the edge computing unit assembles the information generated in the above stages into a demand envelope message according to a preset data structure. This message contains the following fields: user identity identifier, action identifier, and lower limit of demand quantity. upper limit of demand Minimum gas supply pressure The system includes the earliest gas supply time, the latest gas supply time, the default cost function parameter set, the highest acceptable price, and a priority flag. After assembly, the edge computing unit broadcasts the demand envelope message to the auction room of its subnet in a publish-subscribe pattern via a time-sensitive network. The broadcast time should be no later than the action plan execution time minus the sum of one auction cycle and communication transmission delay to ensure that the auction room has sufficient time to complete the matching and transmit the gas supply contract back to the gas user.

[0101] It should be noted that when the gas consumer cannot obtain future action sequences due to communication interruption or controller failure, the generation of the demand envelope can be downgraded to a standby mode based on statistical forecasting. In standby mode, the edge computing unit uses the historical gas consumption time-series data it maintains for that gas consumer to predict the gas demand within a preset time window using time series forecasting methods such as exponentially weighted moving average or autoregressive integral moving average, and generates the corresponding demand envelope. The width of the demand range generated in standby mode should be expanded by a preset proportion based on the normal range, with a default expansion of 50%, and the insufficient pressure penalty coefficient α and the delay benchmark cost coefficient β in the default cost should be reduced to 50% of the normal value to reflect the risk discount caused by forecast uncertainty.

[0102] Furthermore, for gas-consuming equipment clusters consisting of multiple gas-consuming terminals, an aggregation agent can be set up at the cluster controller level. The aggregation agent collects the individual device demand envelopes from each gas-consuming terminal within the cluster, merges and deduplicates them according to the time window of the action execution time, and packages multiple demands with overlapping time windows that can be served by the same gas supply path into a single aggregated demand envelope, thereby reducing the matching scale and communication overhead of the auction room. The quantity range of the aggregated demand envelope is the algebraic sum of the quantity ranges of each individual device's demand envelope; the pressure constraint is the highest among all individual devices; and the time constraint is the narrowest intersection time window among all individual devices. The aggregation agent, as the sole representative of the cluster, participates in the auction room clearing process. After the auction is completed, the aggregation agent further allocates the winning quantity and supply pressure according to the priority and quantity ratio of each individual device's demand.

[0103] S302, the gas supply end and energy storage end generate supply orders containing available quantity, pressure level and supply price based on real-time operating status, efficiency curve and marginal cost, and submit the supply orders to the auction room.

[0104] In this auction market, supply orders serve as the vehicle for gas suppliers and energy storage providers to express their supply capacity and willingness. Gas suppliers are typically air compressor stations or distributed booster pumps, while energy storage providers are high-pressure gas storage tanks and their associated control valve assemblies. The supply price in the order represents the minimum price required by the gas supplier or energy storage provider to sell a unit volume of gas to the market at the current moment. This price is calculated based on real-time marginal gas supply costs and accurately reflects the economic cost and energy conversion efficiency of gas supply.

[0105] In some implementations, the specific process of generating a supply order at the gas supply end is as follows: First, the air supply end collects the current operating parameters of the air compressor or booster equipment, including input electrical power, exhaust pressure, exhaust temperature, and exhaust mass flow rate; Then, the current output flow rate is calculated based on the exhaust pressure, temperature, and mass flow rate, and its ratio to the input electrical power is used as the "electric-to-electric" conversion efficiency at the current operating point.

[0106] The gas supplier obtains the real-time electricity price of the current node published by the factory's energy management system, divides the real-time electricity price by the above conversion efficiency, and obtains the electricity cost consumed by the production unit of gas. Then, it adds the equipment unit maintenance cost conversion factor, which can be the quotient of the total maintenance cost of the equipment throughout its entire life cycle divided by the total output of the equipment throughout its entire life cycle, and the default value is 0.02 internal settlement units per kilojoule.

[0107] Ultimately, the gas supplier adds a preset profit margin (default 5%) to the sum of electricity and maintenance costs to form the supply quote. Simultaneously, the gas supplier determines the available quantity and pressure level based on the equipment's current remaining adjustable capacity and outlet pressure adjustment range, and packages this together with the supply quote into a supply sell order.

[0108] In some implementations, the formula for calculating the gas supply price is as follows: ;in, The supply quote represents the minimum price required by the gas supplier to sell a unit of gas to the market. The unit electricity price is calculated from the real-time electricity price published by the factory's energy management system for the current node. The "electric-to-gas" conversion efficiency is dimensionless and defined as the ratio of the gas flow rate output at the gas supply end to the input electrical power under current operating conditions. = / ,in The flow rate is calculated from the exhaust pressure, temperature, and mass flow rate using the ratio-to-flow-rate formula. For input electrical power; The unit maintenance cost conversion factor is the quotient of the total maintenance cost of the equipment throughout its entire life cycle divided by the total output of the equipment throughout its entire life cycle, with a default value of 0.02 internal settlement units per kilojoule; r is the preset profit rate, dimensionless, with a default value of 0.05.

[0109] The process of generating supply orders for energy storage is slightly different: the energy storage unit records the weighted average cost of energy tokens purchased by its gas storage tanks during each filling cycle. When the energy storage unit decides to release gas to the market, its supply price is generated based on this weighted average cost, with the addition of energy storage loss compensation. The default energy storage loss compensation rate is 3% to cover the energy quality degradation caused by heat exchange during the gas storage process.

[0110] It should be noted that when a factory connects to distributed renewable energy generation and has a surplus of green electricity, the real-time electricity price at the gas supply end may be extremely low or even zero. In this case, the marginal cost at the gas supply end mainly consists of maintenance costs, and the supply price will be significantly reduced. This will naturally give the gas supply end priority in auctions, guiding the system to absorb green electricity. Energy storage can also purchase large quantities of energy tokens for storage when electricity prices are low, and sell them at a price lower than the real-time gas supply price when electricity prices are high, thus achieving peak shaving and valley filling.

[0111] S303. The auction room aims to maximize social welfare. Under the premise of meeting the pressure and time constraints in the demand envelope, it matches and clears buy and sell orders to generate gas supply contracts.

[0112] In this context, maximizing social welfare means achieving an auction result that maximizes the difference between the total willingness to pay of all successful buyers and the total cost of all successful sellers. From an engineering perspective, by sorting buy orders from highest to lowest acceptable price and sell orders from lowest to highest supply price, the intersection of the two price curves on the same volume axis is the market clearing point. The price corresponding to this point is the clearing price. All buy orders with acceptable prices higher than the clearing price and all sell orders with prices lower than the clearing price are executed at the clearing price. This clearing mechanism is equivalent to solving a constrained linear programming problem whose objective function is to maximize the total surplus. The constraints include pressure and time constraints in the demand envelope of each gas consumer and gas supply capacity constraints at the gas supply end.

[0113] In some implementations, the specific process for the auction room to execute a matching and clearing process is as follows: The first step is filtering: The auction room iterates through all valid buy and sell orders in the current auction period. For each buy order, it checks whether there is at least one sell order whose pressure level meets the minimum gas supply pressure requirement of the buy order and whether the delivery time window of the sell order overlaps with the time window of the buy order. If not, the buy order will not participate in the clearing of this round and will enter the waiting queue or trigger cross-subnet routing.

[0114] The pressure rating represents the stable gas pressure that the gas supply or storage unit can output to the pipeline network under current operating conditions. It is obtained based on the rated exhaust pressure range of the air compressor, booster pump, or gas tank connected to the supply or storage unit, as well as the actual measured value of the current output pressure. The pressure rating is specified as a rigid constraint field in the supply order, used for matching and verification against the minimum gas supply pressure requirement in the demand envelope of the gas user. During the filtering phase, the auction room compares the pressure rating of the sell order with the minimum gas supply pressure requirement of the buy order. Only when the pressure rating of the sell order is greater than or equal to the minimum gas supply pressure requirement of the buy order is the sell order considered a candidate supplier for that buy order. If multiple sell orders meet the same pressure constraint of the buy order, these sell orders are retained for subsequent sorting and clearing steps, where the social welfare maximization objective further selects the best match.

[0115] The second step is sorting: the buy orders that have passed the filter are sorted in descending order of the highest acceptable price, and buy orders with the same price are sorted in order of time constraint urgency, with priority given to those with more urgent time windows; sell orders are sorted in ascending order of supply price from low to high, and sell orders with the same price are sorted in descending order of gas supply conversion efficiency.

[0116] The third step, matching and clearing: Proceeding simultaneously from the high and low ends of the price sequence towards the middle, on the same volume accumulation axis, the highest-priced buy orders are matched sequentially with the lowest-priced sell orders, and the trading volume is accumulated. As matching progresses, the marginal willingness to pay for buy orders decreases, while the marginal cost of sell orders increases. Matching terminates when the acceptable price for a matched pair of buy orders is lower than the supply price of sell orders. At this point, the price range of the previously successfully matched buy / sell order pair becomes the clearing price range, and the midpoint of this range or the average of the buy and sell prices is taken as the clearing price. All buy and sell orders that have been successfully matched before the termination point are executed at this clearing price. This process can also be equivalently expressed as solving the following optimization problem: Objective: Maximize ; Satisfy constraints: For all buyers i, The upper limit of the demand for paying for i; For all sell orders j, The available quantity for sell order j; For each matching pair (i,j), the following must be satisfied: Furthermore, their delivery time windows overlap.

[0117] in The highest acceptable price for paying i. For the supply quote of sell order j, For the amount allocated to purchaser i, For the quantity of the bid that was won by seller j, For sell order J, the pressure level The minimum gas supply pressure for paying bill i.

[0118] Step 4, Cross-Subnet Cascading: When a bid order cannot be satisfied by a local sell order during the matching process in the auction room of this subnet, the shortage demand is encapsulated into a cascading bid order by the pipeline nodes of this subnet and routed to the auction rooms of adjacent subnets or upstream subnets. The upstream auction room treats this cascading bid order as a regular bid order and includes it in the clearing process for this cycle. If the upstream auction room still cannot fully satisfy the demand, the cascading continues upwards until the demand is satisfied or reaches the root node of the entire network.

[0119] Step 5: Contract Generation: For each successfully matched buy and sell order, the auction room generates a gas supply contract. The data structure of the gas supply contract includes the following fields: the identity identifier of the gas supplier or energy storage unit, the identity identifier of the gas user, the agreed delivery quantity, the delivery start and end time window, the gas supply pressure reference curve, the clearing settlement price, and the supplier's default compensation clause determined based on the gas user's default cost function. Immediately after contract generation, it is sent to both parties via a secure communication channel. After verifying the contract content locally, both parties execute the physical delivery as agreed.

[0120] For example, a sub-network auction room received 3 buy orders and 2 sell orders during this period. Buy order A: Demand quantity 2.0 kJ, acceptable maximum price 0.9; Buy order B: Demand quantity 1.5 kJ, acceptable maximum price 0.7; Buy order C: Demand quantity 3.0 kJ, acceptable maximum price 0.5. Sell order X: Available quantity 4.0 kJ, quoted price 0.3; Sell order Y: Available quantity 2.0 kJ, quoted price 0.6. After sorting, the buy order order is A, B, C, and the sell order order is X, Y.

[0121] First, buy order A and sell order X are matched, with a successful match of 2.0 kJ. The remaining sell order X is also 2.0 kJ. Next, buy order B is matched, with a successful match of 1.5 kJ. The remaining sell order X is 0.5 kJ. Then, buy order C is attempted to be matched. At this point, buy order C's acceptable price is 0.5, and sell order X's offer is 0.3, resulting in a successful match of 0.5 kJ, and sell order X is sold out. Buy order C has 2.5 kJ remaining. Next, sell order Y is matched. Buy order C's acceptable price is 0.5, and sell order Y's offer is 0.6. At this point, the buy order's acceptable price is lower than the sell order's offer, and the matching process terminates.

[0122] Ultimately, buy orders A and B were fully executed, buy order C only executed for 0.5 kJ, sell order X was fully executed, and sell order Y was not executed. The clearing price was the average of the last successfully matched buy and sell prices, i.e., (0.5 plus 0.3) divided by 2, which equals 0.4 internal settlement units per kJ. The auction room generated and issued gas supply contracts for the three executed buy and sell pairs. The remaining 2.5 kJ demand of buy order C was encapsulated by the pipeline node and routed to the upstream subnet.

[0123] Based on the above technical solution, this step establishes a two-way auction matching mechanism based on flexible demand envelope and real-time supply bidding, transforming the supply and demand matching of the pneumatic system from the traditional centralized, rigid scheduling to a distributed, market-oriented, and dynamically optimal configuration. The elastic demand expression of the gas user provides the system with aggregateable and tradable flexible resources. The marginal cost-based bidding of the gas supply and energy storage sides directly maps the difference in physical efficiency between energy production and storage into economic signals. The clearing objective of maximizing social welfare mathematically guarantees that each matching maximizes the difference between the energy utilization efficiency and cost of the entire system, thereby achieving millisecond-level self-organized optimal coordination between the gas supply and gas user sides in a highly uncertain production environment.

[0124] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 5 As shown, the above S4 specifically includes the following S401 to S403: S401. Within each preset audit period, collect the total number of tokens generated, consumed, recovered, and the changes in gas storage in the pipeline network, and perform a token quantity conservation audit.

[0125] In any closed physical pipeline system, if all changes in energy input, consumption, recovery, and storage are precisely measured, then the total amount of energy tokens entering the system must be strictly equal to the total amount of tokens leaving the system or stored within the system. This conservation relationship stems from the mapping of the first law of thermodynamics to tokenized energy systems—tokens, as the equivalent carrier of energy in the digital domain, must maintain strict consistency with the increase or decrease of physical energy. Any breach of conservation implies the existence of undetected energy leakage, i.e., physical leakage, or information domain faults such as meter drift, token forgery, or communication packet loss. Therefore, conservation auditing can detect any local anomalies from a global perspective at the system level, with a sensitivity far exceeding that of threshold alarms from individual sensors or sections of pipeline.

[0126] In some implementations, the execution process of conservation auditing is as follows: Within each preset audit cycle (default is 1 second), the audit engine reads the total value of the energy tokens generated between the end of the previous audit cycle and the end of the current audit cycle from the token generator at the outlet of the high-pressure gas supply network, and obtains the total token generation G.

[0127] The audit engine aggregates the total value of tokens marked as consumed and signed by all gas terminals in the entire system during the audit period, obtaining the total token consumption C. After completing the token destruction operation in S203, each gas terminal uploads its consumption record in real time.

[0128] The audit engine reads the total residual token value received during the audit period from the standard metering station at the end of the low-pressure recycling pipeline and the entrance of the recycling center, and obtains the total amount of residual tokens recovered, R.

[0129] When the energy regeneration center re-injects the recovered residual energy into the high-pressure gas supply network after gas-to-electricity, gas-to-heat, or gas-to-gas cycle conversion, the total energy value of the regeneration tokens cast by the regeneration token generator at the injection point is included in the total regeneration token quantity G'. If there is no regeneration injection operation during the audit period, G' is zero.

[0130] The total amount of gas stored in the pipeline network changes dynamically due to pressure fluctuations. This change in gas mass must be considered as a factor in the conservation equation; otherwise, normal pressure regulation may be misjudged as a leak. Each pipeline node agent monitors the gas pressure and temperature of its respective segment in real time, calculates the gas mass within the segment using the gas state equation, and combines this with the current pressure and temperature to calculate the total gas mass corresponding to that segment, thus obtaining the tokenized amount ΔS for the change in gas storage in the pipeline network. The formula for calculating ΔS is as follows: ; ; in, This represents the total gas volume stored in the entire pipeline network at the end of the audit period. The total gas stored in the entire pipeline network at the start of the audit period; k is the pipe segment number, and the pipe segment is divided by the segment valve, branch node or pipe diameter change point in the pipeline network; The average absolute pressure of the gas in pipe segment k at time t is obtained by averaging the pressure measurements taken by upstream and downstream nodes of the pipe segment. The geometric volume of pipe segment k is obtained from the pipe design drawings or as-built measurement data. The default value is entered during system initialization. R is the gas constant; Let be the average gas temperature in pipe segment k at time t; Let be the ratio of gas in pipe segment k at time t.

[0131] During pipeline operation, some pipeline segments may have been marked as potentially leaking segments by node agents due to persistently abnormal differences in inlet and outlet token values. However, during the audit period before isolation, the energy loss corresponding to these known leaks should be separated from the conservation deviation and listed separately to distinguish between "discovered leaks" and "newly emerging unknown anomalies." The value of the leakage loss token amount L is calculated by each pipeline segment node agent based on the historical cumulative difference between the inlet token flow and the outlet token flow of that pipeline segment, after deducting normal pipeline losses. Normal pipeline losses are defaulted to 0.1% of the total flow rate of the pipeline segment.

[0132] Finally, the conservation equation is verified. The audit engine substitutes the above quantities into the conservation equation: The physical meaning of this equation is as follows: During the audit period, the sum of high-pressure energy tokens generated by the gas supply source and regenerated tokens reinjected by the regeneration center—that is, the total newly added token energy of the system—must be equal to the sum of token energy actually consumed and destroyed by gas-using terminals, residual token energy recovered by the low-pressure recovery pipeline network, changes in the gas storage capacity within the pipeline network, and identified leak-related token losses. The left side of the equation represents the input terms, and the right side represents the destination terms; their balance is the mathematical criterion for the healthy operation of the system.

[0133] When the absolute value of the difference between the two sides of the equation exceeds the preset conservation deviation threshold, the conservation audit is deemed unsuccessful. The setting of the conservation deviation threshold should comprehensively consider the accuracy level of all metering instruments in the system, the time synchronization error of data at each stage, and the communication latency jitter during token circulation. This threshold can be set to 0.5% of the total circulating tokens within the audit period, and an absolute lower limit should be set to prevent false alarms caused by an excessively low threshold under low-traffic conditions. This absolute lower limit is set to 50 kJ by default.

[0134] S402. When the conservation audit fails, compare the difference between the incoming and outgoing tokens of each segment node agent from top to bottom along the subnet level to locate the faulty segment.

[0135] The "top-down" approach refers to a diagnostic path that starts from the root node of the entire network. First, it compares the balance of incoming and outgoing tokens in each first-level subnet to identify imbalanced or abnormal subnets. Then, within these abnormal subnets, it compares the differences in incoming and outgoing tokens in each of its subordinate second-level subnets or pipe segment nodes. This process is repeated layer by layer until the anomaly is pinpointed to one or a few specific pipe segments. This hierarchical narrowing strategy avoids blindly searching through a massive number of nodes across the entire network, making the time complexity of the localization process linearly related to the depth of the network hierarchy, rather than being directly proportional to the total number of nodes.

[0136] In some implementations, the method for calculating the difference between the incoming and outgoing tokens of the segment node agent is as follows: During the audit period, the node agent records the total value of all energy tokens flowing through its upstream interface, denoted as IN; and records the total value of all energy tokens flowing through its downstream interface, denoted as OUT. Simultaneously, the total number of tokens directly consumed and destroyed by gas-using terminals during the audit period for this pipeline section is recorded as follows: Then the token flow balance difference of this pipe segment. =IN-OUT- Under normal conditions with no leaks and no metering malfunctions, this difference should statistically approach zero.

[0137] when A continuous deviation from the normal range indicates an abnormality in that pipe section.

[0138] The criteria for identifying abnormal pipeline segments consider both the magnitude and duration of the difference to avoid misjudgments caused by brief pressure fluctuations or communication jitter. The specific judgment logic is as follows: if the difference between the inbound and outbound tokens proxies a certain pipeline segment node... If the absolute value of the fluctuation exceeds the upper limit of the historical fluctuation range under normal operating conditions for N consecutive audit periods, then the pipeline segment is marked as a faulty segment. The upper limit of the historical fluctuation range can be taken as the historical normal operating period of the node. The 95th percentile of the absolute value is calculated by default from a sample of 1000 audit periods of historical operating data. The number of consecutive judgment periods N is 3 by default, but this value can be configured between 1 and 10. The smaller the value, the higher the sensitivity of fault detection, but the greater the risk of false alarms; the larger the value, the stronger the anti-interference ability, but the longer the delay in fault location.

[0139] It should be noted that for pipe sections where the end of the pipeline network is directly connected to the gas user terminal, The item is the sum of the consumption tokens of all gas-using terminals within the jurisdiction of this pipeline section; for the main transmission and distribution pipeline section, It is usually zero, at this time Simplified to IN minus OUT. For node agents located at the subnet boundary, they are responsible for both internal auditing of their own subnet and for summarizing and reporting the net inbound and outbound token volume of their subnet to the next higher-level node agent.

[0140] For example, continuing from the S401 example where the audit failed, the audit engine first queries the agent summary data for the three primary subnets across the entire network: Subnet A reports a net inflow of 1000 kJ and a net outflow of 1005 kJ, a difference of -5 kJ; Subnet B reports a net inflow of 500 kJ and a net outflow of 490 kJ, a difference of +10 kJ, both within the normal range. Subnet C reports a net inflow of 200 kJ and a net outflow of 105 kJ, a difference of +95 kJ, which is significantly abnormal. The audit engine narrows the diagnostic scope to subnet C. Within subnet C, there are 5 pipeline node agents, of which node C3... The values ​​for the three most recent consecutive audit periods were +82 kJ, +88 kJ, and +95 kJ, respectively, while the historical fluctuation limit is 15 kJ. The difference between the incoming and outgoing tokens for node C3 continuously exceeded the limit, and it was identified as a faulty segment. The audit engine marked this faulty segment as "Suspected Leakage or Metering Fault - Segment C3".

[0141] S403: Trigger the closing command of the electric sectional valves at both ends of the faulty pipe section to physically isolate the faulty pipe section from the pipe network, and notify the dispatch layer to update the pipe network topology model to bypass the fault path and complete the closed-loop control.

[0142] This step transforms the fault location results of S402 into actual physical protection actions and scheduling strategy adjustments, thereby completing a full self-healing closed loop of "detection-location-isolation-reconstruction" at the system level. The primary goal of isolation control is to prevent the continuous loss of leaked working fluid and the potential expansion of safety risks. Secondly, by updating the available path information at the scheduling layer, subsequent gas supply contracts automatically bypass the faulty pipeline segment, rebuilding the supply and demand balance on the remaining healthy pipeline network, and maximizing the production continuity of non-faulty areas.

[0143] In some implementations, the specific execution flow of isolation control is as follows: The first step is to issue an isolation command. After identifying the faulty pipe segment, the audit engine immediately sends a closing command to the electrically operated sectional valves at both ends of that segment. The command is transmitted via a time-sensitive network, and the execution time of the electrically operated sectional valve from receiving the command to complete closure is limited to no more than 200 milliseconds by default. The electrically operated sectional valve can be driven by a motor or a pneumatic actuator. For sectional valves driven by pneumatic actuators, an independent control air source or accumulator should be configured to ensure reliable closure even if the main air source pressure is insufficient due to leakage.

[0144] The second step is to confirm the isolation status. After completing the closing action, the electric sectional valve sends a confirmation signal of its valve position switch to the audit engine. Upon receiving confirmation that both valves at both ends are closed, the audit engine marks the status of the faulty pipe segment as "isolated". If no confirmation of position is received from either valve within the specified timeout period (default 5 seconds), the audit engine will issue an execution failure alarm, notify maintenance personnel to intervene, and automatically include the adjacent upstream pipe segment of the faulty pipe segment in the risk monitoring scope.

[0145] The third step is to update the pipeline topology model. The audit engine pushes the isolated status information of the faulty pipeline segment to the pipeline topology model of the scheduling layer. The scheduling layer marks the edge corresponding to the faulty pipeline segment as unavailable in the directed graph of the pipeline network it maintains, and recalculates the available gas supply paths between each subnetwork. The updated topology model will be used by the auction room when generating gas supply contracts in the next auction cycle, by default after 100 milliseconds.

[0146] The fourth step involves the dispatch layer generating contracts by bypassing faulty paths. During the subsequent auction matching process, the auction room automatically excludes all paths passing through isolated faulty pipeline segments when selecting cross-subnet paths and matching gas supply contracts. Gas users originally supplied by these paths will have their needs met through other available paths or by the nearest energy storage facility. For isolated gas users whose available gas supply paths are interrupted due to fault isolation, the dispatch layer reports this situation to the plant equipment management system and activates the degraded production contingency plan.

[0147] Step 5: Reconnection after fault recovery. Once the maintenance personnel have repaired the faulty pipeline segment and confirmed through system verification that the difference between its inbound and outbound tokens has returned to normal, they can send a recovery command to the audit engine. Upon receiving the recovery command, the audit engine performs a short-term special conservation audit for that pipeline segment, which lasts for 10 audit cycles by default. After confirming that there are no anomalies, it removes the isolation mark, notifies the scheduling layer to restore the corresponding edge of the pipeline segment to an available state, and releases the closed state of the electric sectional valve, allowing the pipeline segment to resume normal operation.

[0148] For example, following the fault location result of S402, the audit engine simultaneously sends a closing command to the electric sectional valves V3a and V3b at both ends of pipe segment C3. V3a sends back a confirmation of closing completion after 120 milliseconds, and V3b sends back a confirmation of closing completion after 150 milliseconds, both completing within the default time limit of 200 milliseconds. The audit engine marks pipe segment C3 as "isolated" and pushes a topology update message to the scheduling layer. In the next auction cycle, the scheduling layer removes the gas supply path containing pipe segment C3 from the list of available paths. The two welding stations that were originally supplied with gas via this path will have their demand envelopes automatically routed to a high-pressure gas storage tank agent within subnet C in the next auction, with the energy storage end taking over the gas supply.

[0149] Based on the above technical solution, this step establishes a closed-loop process from global conservation auditing and hierarchical fault location to automatic isolation and topology reconfiguration. This transforms leaks and metering faults in traditional pneumatic systems, which require manual inspection and experience-based judgment to detect, into quantifiable, automatically located, and instantly isolated digitally driven decisions. The conservation auditing equation is constrained by physical conservation laws, and the hierarchical comparison of inbound and outbound token differences is indexed by the natural hierarchical structure of the pipeline topology. This makes the location accuracy and timeliness of faulty pipe sections significantly better than conventional single-point threshold alarm methods. The linkage between isolation and scheduling reconfiguration ensures the system's ability to safely degrade under fault conditions.

[0150] It should be noted that among the various calculation formulas involved in the technical solution of this application, the conservation audit equation, the default cost function, and the supply quotation calculation expression belong to the type of formulas where dimensions can be ignored and only the calculated numerical values ​​need to be considered. Specifically, the conservation audit equation can directly add or subtract the numerical values ​​carried on the tokens during audit execution, without the need for unit conversion during the calculation process; the default cost function directly substitutes the values ​​of pressure and time to obtain the cost value during calculation; and the supply quotation calculation expression can directly perform arithmetic operations on the numerical values ​​during real-time quotation generation. A common feature of these formulas is that their input variables and output results are scalar values ​​under the same dimensional system, and the coefficients in the formulas have already absorbed the necessary dimensional conversion factors. Therefore, in the system software implementation, programs can be written directly according to the numerical calculation logic without the need for dimensional annotations or dimensional verification in each intermediate calculation step, thereby simplifying the numerical processing flow of modules such as the edge computing unit and the auction room.

[0151] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0152] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative descriptions of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and variations.

Claims

1. A closed-loop control method for the gas supply and consumption ends of a zero-carbon factory, characterized in that, include: A token generator is set up at the gas supply source to quantify the energy value of the output gas into energy tokens; The value represents the maximum energy that a gas can convert into useful work under given pressure, temperature and flow conditions. The system uses the pressure regulating valve at the gas end to output the required pressure and calculates the amount of energy consumed, and then destroys the corresponding number of energy tokens based on the calculated amount of energy consumed. The scheduling layer receives demand envelopes, including volume ranges and pressure constraints, broadcast by each gas user, as well as supply quotations from the gas supplier and energy storage end. It then uses a two-way auction mechanism to match the demand envelopes and supply quotations in real time to generate gas supply contracts. The two-way auction mechanism refers to a market mechanism in which buyers and sellers submit quotations and demands simultaneously within the same auction period, and all quotations are centrally matched and cleared according to a preset optimization target. Based on the generation, destruction, recycling, loss, and changes in the stock of the energy tokens, a token quantity conservation audit is performed, and isolation control is triggered based on the audit results.

2. The closed-loop control method for the gas supply and consumption ends of a zero-carbon factory according to claim 1, characterized in that, The zero-carbon plant includes a high-pressure gas supply network and a low-pressure gas recovery network; The high-pressure gas supply network is used to transport the high-pressure gas output from the air compressor station or energy storage terminal to each gas user terminal, serving as the physical channel for the synchronous transmission of the energy token to the airflow, and providing a high-pressure side pressure source for the flow regulating valve, enabling the flow regulating valve to complete the pressure regulation output by utilizing the pressure difference between the high-pressure side and the low-pressure side. The low-pressure recovery pipeline network is used to collect all low-pressure waste gas emitted after the gas-consuming end has finished its work, and to transport the low-pressure waste gas to the regeneration center, and to serve as a physical channel for the residual gas to be returned with the low-pressure gas flow; wherein, the regeneration center is an energy recovery and working fluid regeneration facility set at the end of the low-pressure recovery pipeline network, used to operate gas-electricity cycle, gas-heat cycle and gas-gas cycle in parallel, to recover the pressure energy, heat energy and working fluid in the waste gas and feed them back to the factory energy system and high-pressure gas supply network respectively; The energy tokens are generated by a token generator protected by a hardware security module. Each token contains a unique identifier, an initial value, a generation timestamp, pressure and temperature parameters of the generation point, and the hash value of the previous token.

3. The closed-loop control method for the gas supply and consumption ends of a zero-carbon factory according to claim 2, characterized in that, The energy token is transmitted synchronously with the airflow, specifically including: In the high-pressure gas supply network, each time the token generator generates an energy token, it simultaneously sends out the data packet of the token and the corresponding gas flow that generated the token through the network. The token audit gateway deployed at each pipe segment node of the pipeline network reads the token identifiers flowing through the pipeline in real time and records the passage time. It also collects the gas pressure and flow rate at the pipe segment nodes and establishes a mapping relationship between the advancement of the token flow on the time axis and the advancement of the gas on the spatial axis to obtain the time deviation between the arrival time of the token at each gas-consuming end and the arrival time of the corresponding gas flow at the same gas-consuming end. The pipe segment node is an intelligent monitoring unit set at the segment valve, branch point, or pipe diameter change point in the high-pressure gas supply network to monitor the gas state parameters and token passage status of the upstream and downstream pipe segments. The token flow, generated chronologically by the token generator, arranges the energy tokens sequentially. The time deviation is used to verify the synchronization between the token and the gas flow. When the time deviation exceeds a preset synchronization threshold, a synchronization anomaly alarm is triggered at the corresponding pipe segment node. In the low-pressure recovery pipeline network, the residual tokens generated synchronously when the gas is exhausted are transmitted back to the regeneration center with the airflow, completing the synchronous transmission of the token flow and the airflow.

4. The closed-loop control method for the gas supply and consumption ends of a zero-carbon factory according to claim 1, characterized in that, The calculation process for the amount of energy consumed includes: When the flow regulating and pressure regulating valve performs the gas consumption action, it collects the gas pressure, temperature and mass flow rate entering the valve group in real time, as well as the pressure, temperature and mass flow rate of the discharged gas, and obtains the inlet state parameters and the exhaust state parameters respectively. The intake air-carrying input ν value is calculated based on the intake air state parameters, and the exhaust air-carrying output ν value is calculated based on the exhaust air state parameters. The difference between the input gas value and the output gas value is used to obtain the gas consumption amount of the current gas consumption action; The formula for calculating the value of 㶲 is: E is the gas enthalpy, m is the gas mass, h is the gas specific enthalpy, h0 is the gas specific enthalpy under environmental reference conditions, T0 is the environmental reference temperature, s is the gas specific entropy, and s0 is the gas specific entropy under environmental reference conditions. The environmental reference conditions are the annual average temperature and standard atmospheric pressure of the factory location.

5. The closed-loop control method for the gas supply and consumption ends of a zero-carbon factory according to claim 4, characterized in that, The process of destroying a corresponding number of energy tokens based on the calculated amount specifically includes: The edge computing unit built into the flow regulating and pressure regulating valve receives the energy tokens that arrive with the intake airflow and processes them sequentially according to the order of arrival. Based on the calculated consumption, the energy tokens are deducted one by one from the currently held energy tokens. Each time a token is deducted, it means that the token is digitally signed using the private key corresponding to the flow regulating valve and marked as consumed. Tokens marked as consumed are broadcast to the entire network and removed from subsequent transactions and circulation processes.

6. The closed-loop control method for the gas supply and consumption ends of a zero-carbon factory according to claim 1, characterized in that, The gas supply contract specifies the quantity, gas pressure reference curve, and delivery time to be delivered from the gas supplier or energy storage unit to the gas consumer within a specified time window. The specific process for generating the gas supply contract includes: Each gas consumer obtains the sequence of actions to be executed within a preset time before the equipment is executed, generates the demand envelope based on the action sequence, and broadcasts the demand envelope to the auction room; the auction room is a virtual bidding space running on the edge computing node of the corresponding subnet, which is responsible for matching the buying and selling of all gas consumers, gas suppliers and energy storage terminals in the subnet; Based on real-time operating status, efficiency curves, and marginal costs, the gas supply and energy storage units generate supply orders that include available quantity, pressure level, and supply price, and submit the supply orders to the auction room. Gas consumers submit their supply orders to the same auction room using the demand envelope as their supply order. The supply order records the range of quantities the gas consumer is willing to purchase within a specified time window, the highest acceptable pressure bid, and pressure and time constraints. The auction room aims to maximize social welfare and, while satisfying the pressure and time constraints, matches and clears all buy and sell orders; wherein, maximizing social welfare means maximizing the difference between the total willingness of all buyers to pay and the total cost of the sellers. When the supply of the local subnet is insufficient, the pipeline nodes of the subnet act as agents to cascade routes to adjacent subnets or upstream subnets, and the upper-level auction room matches gas supply demands across subnets until the gas supply demand is met. Finally, a gas supply contract is generated by matching the successful bids and sell orders. The gas supply contract includes: the identification of the gas supplier or energy storage unit, the delivery quantity agreed in the contract, the gas supply pressure reference curve, the clearing settlement price, and the default compensation.

7. The closed-loop control method for the gas supply and consumption ends of a zero-carbon factory according to claim 1, characterized in that, The gas supply and energy storage terminals generate supply orders based on real-time operating status, efficiency curves, and marginal costs, including available gas quantity, pressure level, and supply price. Specifically, these orders include: The gas supply end calculates the actual power consumption corresponding to the unit gas volume under the current operating conditions based on the current operating power, exhaust pressure, exhaust temperature and mass flow rate of the air compressor or booster equipment. The pressure-flow-specific power surface obtained by fitting the equipment's factory performance test report or historical operating data is used as the efficiency curve. The ratio of the actual power consumption to the optimal power consumption of the efficiency curve under the same operating conditions is used as the energy efficiency attenuation coefficient. The gas supplier obtains the real-time electricity price of the node where the factory is located, and multiplies the real-time electricity price, the energy efficiency attenuation coefficient, and the equipment unit maintenance cost conversion factor to obtain the marginal cost per unit volume. The gas supply end generates the supply quotation based on the marginal cost and the preset profit rate, and determines the available gas quantity and pressure level based on the current remaining adjustable capacity of the equipment and the outlet pressure range. The energy storage unit calculates the weighted average cost of the tokens corresponding to the current gas storage based on the current gas storage pressure, gas quality, and the average price of the tokens purchased in the previous cycle. The supply quotation is then generated by adding energy storage loss compensation to the weighted average cost. Simultaneously, the available gas quantity and pressure level are determined based on the upper limit of the sustainable venting rate and the lower limit of the safe gas storage pressure of the gas storage tank.

8. The closed-loop control method for the gas supply and consumption ends of a zero-carbon factory according to claim 2, characterized in that, The process of conservation auditing specifically includes: Within each preset audit period, the total number of tokens generated by the token generator of the high-pressure gas supply network is collected and denoted as G; Collect the total number of tokens destroyed by all gas-using terminals, and denote it as C; The total amount of residual tokens collected and recycled by the recycling center is denoted as G'; Based on the changes in pipeline gas storage pressure and temperature monitored by the agent at each pipeline segment node, the token conversion amount corresponding to the change in dynamic gas storage volume of the pipeline network within the current audit period is calculated and denoted as ΔS; the agent at each pipeline segment node refers to an intelligent monitoring unit deployed at pipeline segment valves, branch nodes and pipe diameter changes, used to collect the gas pressure, temperature and flow rate of the pipeline segment in real time. Based on the difference between the incoming and outgoing tokens of each pipe segment node agent, identify and calculate the leakage loss token amount of the marked leaking pipe segment, denoted as L; Verify whether the equation G+G'=C+R+ΔS+L holds true; and if the difference between the two sides of the equation exceeds a preset threshold, the conservation audit is deemed to fail; otherwise, the conservation audit is deemed to pass.

9. A closed-loop control method for the gas supply and consumption ends of a zero-carbon factory according to claim 8, characterized in that, The triggering of isolation controls based on audit results specifically includes: When the conservation audit fails, the difference between the incoming and outgoing tokens of each subnet and each segment node agent is compared sequentially downwards. When the absolute value of the difference between the incoming and outgoing tokens continuously exceeds the upper limit of the historical fluctuation unit under normal operating conditions of the pipe segment node agent, the pipe segment corresponding to the difference between the incoming and outgoing tokens is determined to be a faulty pipe segment. Trigger the closing command of the electric shunt valves at both ends of the faulty pipe section to physically isolate the faulty pipe section from the pipe network; The scheduling layer updates the pipeline topology model during the generation of the gas supply contract based on the isolation status information of the faulty pipeline segment, bypassing the gas supply path associated with the faulty pipeline segment, and completing closed-loop control.

10. A closed-loop control system for the gas supply and consumption ends of a zero-carbon factory, characterized in that, include: The token generation module is used to quantify the energy value of the output gas into energy tokens at the gas supply source. The value represents the maximum energy that a gas can convert into useful work under given pressure, temperature and flow conditions. The air-end execution module is used to output the required pressure of the device using the flow regulating and pressure regulating valve and calculate the amount of energy consumed, and destroy the corresponding number of energy tokens according to the calculated amount of energy. The scheduling module is used to receive the demand envelope, which includes the volume range and pressure constraints, broadcast by each gas user, as well as the supply quotations from the gas supplier and energy storage end. It then uses a two-way auction mechanism to match the demand envelope and supply quotations in real time to generate a gas supply contract. The two-way auction mechanism refers to a market mechanism in which buyers and sellers submit quotations and demands simultaneously within the same auction period, and all quotations are centrally matched and cleared according to a preset optimization target. The audit isolation module is used to perform token quantity conservation audits based on the generation, destruction, recycling, loss, and changes in the stock of the energy tokens, and to trigger isolation controls based on the audit results.

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