Industrial park level carbon dioxide mineralization and waste heat cascade utilization control method

Abstract

The application provides an industrial park level carbon dioxide mineralization and waste heat cascade utilization control method. Through multi-source waste heat grading collection and cascade utilization, heat pump and heat exchange network cooperation, and intelligent control algorithm based on real-time waste heat demand, the application dynamically adjusts the carbon dioxide mineralization reaction rate and process path, realizes "waste heat driven minimum additional energy consumption mineralization" and maximum utilization of park heat energy, realizes cascade matching and dynamic regulation of multi-source waste heat, significantly improves the waste heat recovery rate of the park and reduces the external energy consumption of the mineralization process, and effectively balances the economic cost and carbon emission reduction benefit through real-time optimization operation strategy.

Description

Technical Field

[0001] This invention relates to the field of industrial park-level mineralization technology, and in particular to a method for controlling carbon dioxide mineralization and waste heat cascade utilization at the industrial park level. Background Technology

[0002] Carbon dioxide mineralization, as an important technological approach for industrial carbon capture and storage, is widely used in energy-intensive industrial parks. With the advancement of dual-carbon goals, related technologies have constructed a heat-driven mineralization system through the synergistic operation of waste heat collection, heat pump temperature enhancement, and reaction control. Specifically, this system covers the entire process from the graded collection of multi-source waste heat to the generation of mineralized products, including key aspects such as heat exchange networks, temperature control, and reaction rate matching, aiming to achieve cascaded energy utilization and improved carbon sequestration efficiency.

[0003] However, existing park-level mineralization methods directly adopt fixed process paths and single heat source supply, without establishing a dynamic coordination mechanism based on real-time heat grade and electricity price fluctuations. This may lead to the direct emission of low-grade waste heat due to insufficient temperature, or a serious disconnect between the mineralization reaction rate and heat energy supply and demand, thereby affecting the overall waste heat recovery rate and operational economy of the park, resulting in additional fossil energy consumption and low carbon emission reduction benefits. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] The main objective of this invention is to provide a control method for the cascade utilization of carbon dioxide mineralization and waste heat at the industrial park level.

[0006] Another objective of this invention is to provide an industrial park-level control device for the cascade utilization of carbon dioxide mineralization and waste heat.

[0007] The third objective of this invention is to provide an electronic device.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a control method for the cascade utilization of carbon dioxide mineralization and waste heat at the industrial park level, comprising:

[0009] S1 collects temperature and flow data from multiple waste heat sources in the park in real time, and combines them with grid electricity price signals and carbon emission constraint data to construct a system operation status model that includes a waste heat supply prediction model and a mineralization reaction demand model. S2, based on the system operation state model, with the optimization objectives of minimizing the overall operating cost and maximizing the mineralization yield, a global optimization scheduling scheme is generated, which includes a waste heat grade classification and allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of mineralization reactor modules operating in parallel. S3, according to the global optimization scheduling scheme, waste heat of different grades is transported to the corresponding mineralization reaction stage, and the low-grade waste heat is heated by adjusting the heat pump unit and then supplemented to the medium and low temperature heat use link. At the same time, the feed flow rate, reaction temperature and operating status of the drying unit of the mineralization reactor module are dynamically adjusted. S4, monitors the actual reaction rate, thermal energy utilization efficiency and equipment operating parameters in real time during the mineralization reaction process, feeds the monitoring data back to the system operation status model to correct prediction deviations, and cyclically executes optimization scheduling and parameter adjustment steps to achieve dynamic matching between mineralization rate and waste heat supply.

[0010] Optionally, the real-time acquisition of temperature and flow data from multiple waste heat generation sources within the park, combined with grid electricity price signals and carbon emission constraint data, constructs a system operation status model including a waste heat supply prediction model and a mineralization reaction demand model, including: Real-time flow and temperature fluctuation data of each access point are obtained through standardized physical and signal interfaces, and waste heat is classified into three grades: high temperature waste heat, medium temperature waste heat and low temperature waste heat according to preset temperature thresholds. Based on historical work process scheduling data, a lightweight time series algorithm or long short-term memory network is used to predict the heat flow and temperature of each access point in the next zero to four hours, generating a waste heat supply prediction model. An energy balance and efficiency model is constructed by combining real-time measured reactor temperature, carbon dioxide feed rate, and product flow rate data, forming a mineralization reaction demand model that includes equipment capacity constraints and emission limits, thereby integrating and generating the system operation status model.

[0011] Optionally, the step of classifying waste heat into three grade levels—high-temperature waste heat, medium-temperature waste heat, and low-temperature waste heat—based on a preset temperature threshold includes: Real-time comparison of collected temperature data with grade limits defined by standardized interface; waste heat with a temperature greater than 250 degrees Celsius is identified as high-temperature waste heat and marked as a heat source to be given priority for the reaction activation stage. Waste heat with a temperature range of 120 to 250 degrees Celsius is classified as medium-temperature waste heat and marked as a heat source for heating or pretreatment. Waste heat with a temperature below 120 degrees Celsius is classified as low-temperature waste heat and marked as a heat source that needs to be boosted by an electric heat pump for mineral drying or auxiliary reactions. At the same time, a heat source capacity model including the uncertainty fluctuation of instantaneous flow rate is established for each grade to support subsequent tiered allocation.

[0012] Optionally, based on the system operation state model, with the optimization objectives of minimizing overall operating costs and maximizing mineralization yield, a global optimization scheduling scheme is generated, including a waste heat grade classification and allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of mineralization reactor modules operating in parallel. This scheme includes: Construct an objective function that minimizes the park's energy costs, maximizes the carbon dioxide mineralization rate, and constrains electricity consumption and emissions. The comprehensive operating cost includes the sum of real-time electricity costs and equivalent carbon costs minus the weighted value of mineralization output. The optimizer is called every five to fifteen minutes using model predictive control or mixed integer programming algorithm to solve the decision variables of thermal power allocated to each reactor module, number of start-up and shutdown stages of heat pump group, power of drying unit and thermal storage charging and discharging strategy; Under the premise of satisfying thermodynamic balance, minimum equipment operating time and grid power constraints, the output includes a global optimized scheduling scheme that includes a high-temperature heat priority supply activation stage and medium- and low-temperature heat being boosted by heat pumps to match the modular reactor.

[0013] Optionally, the objective function constructed, which includes minimizing the park's energy costs, maximizing the carbon dioxide mineralization rate, and constraining electricity consumption and emissions, includes: Set user-configurable target weight parameters to balance the operation strategy of prioritizing carbon reduction or prioritizing economy, and incorporate real-time electricity price signals, peak and valley time period identifiers, carbon price and emission quota data into closed-loop calculation; During periods of low electricity prices, the system automatically selects electric drive heating or heat pump priority operation to increase the mineralization rate; during periods of high electricity prices, the system automatically reduces the reaction rate and transfers excess heat to the heat storage unit or implements a shutdown strategy. By solving the multi-objective Pareto optimal solution set, the optimal scheduling instruction sequence under different marginal cost and carbon intensity constraints is generated to achieve global minimization of fuel cost and carbon cost.

[0014] Optionally, according to the global optimization scheduling scheme, waste heat of different grades is respectively transported to the corresponding mineralization reaction stage, and the low-grade waste heat is supplemented to the medium and low temperature heat use stage by adjusting the heat pump unit to raise the temperature. At the same time, the feed flow rate, reaction temperature and operating status of the drying unit of the mineralization reactor module are dynamically adjusted, including: Based on the waste heat grade classification and distribution strategy, high-temperature waste heat is directly transported to the heat exchanger in the mineralization reaction activation stage, while medium-temperature waste heat is transported to the pretreatment heating unit. The modular heat pump unit is started to heat up the low-temperature waste heat. The coefficient of performance of the heat pump is optimized based on the heat source temperature difference and the deployment capacity. The enhanced heat energy is then added to the mineral drying unit or auxiliary reaction stage. The number of parallel-operating mineralization reactor modules is dynamically adjusted according to the optimization instructions, and the feed pump flow rate, reaction jacket temperature, and start / stop status of solid-liquid separation and product drying units of each operating module are adjusted synchronously to achieve continuous and adjustable mineralization rate as heat energy supply fluctuates.

[0015] Optionally, the modular heat pump unit is activated to heat the low-temperature waste heat, and the coefficient of performance of the heat pump is optimized based on the heat source temperature difference and deployment capacity. The enhanced heat energy is then supplied to the mineral drying unit or auxiliary reaction stage, including: Modular heat pump units are constructed using reversible heat pumps or heat pump stacking schemes, and the compressor speed is controlled by inverter speed regulation to adapt to the flow fluctuations of real-time low-temperature waste heat. The optimal operating stage is calculated in real time based on the functional relationship between the heat pump performance coefficient and the heat source temperature difference. When there is a short-term surplus of heat source in the park, the number of heat pump operating stages is increased to absorb the excess low-grade heat and raise the temperature for use in the drying unit. When high-temperature waste heat is insufficient, it automatically switches to medium-temperature waste heat or low-temperature waste heat boosted by a heat pump to replace the heat supply, ensuring a continuous supply of heat energy for the activation stage of the mineralization reaction and the pretreatment stage, and realizing the efficient cascade utilization of low-grade waste heat.

[0016] Optionally, the real-time monitoring of the actual reaction rate, thermal energy utilization efficiency, and equipment operating parameters during the mineralization process, feeding the monitoring data back to the system operating status model to correct prediction deviations, and cyclically executing optimization scheduling and parameter adjustment steps to achieve dynamic matching between the mineralization rate and waste heat supply, includes: Real-time data on reactor inlet and outlet temperatures, carbon dioxide feed rate, product flow rate and solid content, and equipment operating power are collected using temperature sensors, flow sensors, and power meters. Calculate the actual thermal energy utilization efficiency and mineralization reaction rate and compare them with the output of the prediction model. When fluctuations in the coefficient of performance of the heat pump or a decrease in heat exchange efficiency are detected, trigger data-driven maintenance instructions or automatic bypass strategies. The corrected prediction bias data is input into the cloud optimizer or local server database to update the short-term waste heat production prediction model and the electricity price heat demand prediction model. In the next optimization cycle, a global optimization scheduling scheme is regenerated to respond to changes in the access or shutdown of heat sources in the park.

[0017] To achieve the above objectives, a second aspect of the present invention provides an industrial park-level carbon dioxide mineralization and waste heat cascade utilization control device, comprising: The first module is used to collect temperature and flow data from multiple waste heat generation sources in the park in real time, and combine them with grid electricity price signals and carbon emission constraint data to construct a system operation status model that includes a waste heat supply prediction model and a mineralization reaction demand model. The second module is used to generate a global optimization scheduling scheme based on the system operation state model, with the optimization objectives of minimizing the overall operating cost and maximizing the mineralization yield. This scheme includes a waste heat grade allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of mineralization reactor modules operating in parallel. The third module is used to transport waste heat of different grades to the corresponding mineralization reaction stage according to the global optimization scheduling scheme, and to supplement the low-grade waste heat to the medium and low temperature heat use link by adjusting the heat pump unit to raise the temperature. At the same time, the feed flow rate, reaction temperature and operating status of the drying unit of the mineralization reactor module are dynamically adjusted. The fourth module is used to monitor the actual reaction rate, thermal energy utilization efficiency and equipment operating parameters in the mineralization process in real time. It feeds the monitoring data back to the system operation status model to correct prediction deviations and cyclically executes optimization scheduling and parameter adjustment steps to achieve dynamic matching between mineralization rate and waste heat supply.

[0018] To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory to implement the method described in the first aspect.

[0019] The embodiments of the present invention have the following beneficial effects: by multi-source waste heat collection and cascade utilization in conjunction with heat pumps, the recovery rate of low-grade waste heat is significantly improved and the external energy consumption of the mineralization process is reduced; combined with intelligent dynamic control of real-time electricity price and carbon price, the operating cost is minimized and the carbon dioxide mineralization efficiency is maximized under the matching of heat energy supply and demand in the park. Attached Figure Description

[0020] The above-described and additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a control method for the cascade utilization of carbon dioxide mineralization and waste heat at the industrial park level, provided as an embodiment of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] The following describes, with reference to the accompanying drawings, a control method and apparatus for the cascade utilization of carbon dioxide mineralization and waste heat at the industrial park level according to an embodiment of the present invention.

[0024] Example 1 Figure 1 This is a flowchart of a control method for industrial park-level carbon dioxide mineralization and waste heat cascade utilization according to an embodiment of the present invention.

[0025] like Figure 1 As shown, the control method for the cascade utilization of carbon dioxide mineralization and waste heat at the industrial park level includes the following steps: S1 collects temperature and flow data from multiple waste heat sources within the park in real time, and combines this data with grid electricity price signals and carbon emission constraints to construct a system operation status model that includes a waste heat supply prediction model and a mineralization reaction demand model.

[0026] In this embodiment of the invention, the core of constructing the system operation status model lies in establishing a dynamic mapping relationship between the multi-source waste heat supply characteristics of the park and the mineralization reaction demand. By sensing multi-dimensional operating parameters in real time and integrating external constraints, it provides an accurate data foundation and prediction basis for subsequent global optimization scheduling.

[0027] This step first involves wide-area data collection from the dispersed waste heat generation points within the park, acquiring temperature and flow data that characterize the heat source quality and capacity. Simultaneously, it connects to the grid electricity price signal reflecting operational economics and carbon emission constraint data that characterizes environmental compliance. Based on the aforementioned multi-source heterogeneous data streams, a waste heat supply prediction model is constructed using time series analysis or machine learning algorithms to predict short-term heat flow fluctuation trends. Furthermore, a mineralization reaction demand model is constructed using the thermodynamic balance principle to quantify the heat energy demand under different operating conditions. Finally, supply prediction, demand quantification, and external constraints are integrated into a unified system operation status model.

[0028] As a specific implementation method, the embodiments of this application can define a standardized interface to classify waste heat into three grades: high, medium, and low according to temperature level. A lightweight long short-term memory network is used to predict the heat flow and temperature for the next four hours based on historical data and process scheduling. At the same time, an objective function framework including electricity cost and equivalent carbon cost is established. The real-time measured inlet temperature, flow rate, and local electricity price signal are used as model input variables to form a comprehensive model that can reflect the real-time state of energy supply and demand in the park.

[0029] By constructing a system operation model that incorporates waste heat supply forecasting and mineralization reaction demand, this step achieves digital characterization and forward-looking prediction of the complex heat energy supply and demand relationship in the industrial park, effectively solving the problem of lag in heat energy matching caused by the varying grades and strong fluctuations of multi-source waste heat. This model incorporates uncertain waste heat resources, dynamically changing electricity prices, and carbon constraints into a unified calculation framework, providing a reliable decision-making basis for the subsequent dynamic coordination of waste heat cascade utilization and mineralization rate, significantly improving the agility and accuracy of the embodiments in this application in responding to heat source fluctuations and market signals.

[0030] S2. Based on the system operation state model, with the optimization objectives of minimizing the overall operating cost and maximizing the mineralization yield, a global optimization scheduling scheme is generated, which includes a waste heat grade classification and allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of mineralization reactor modules operating in parallel.

[0031] This step aims to solve the spatiotemporal matching problem between multi-source heterogeneous waste heat supply and the dynamic demand of mineralization reaction. Its core lies in building a multi-variable collaborative global optimization mechanism.

[0032] This mechanism uses an operational state model as input and, by setting dual optimization objectives of minimizing overall operating costs and maximizing mineralization yield, solves for the optimal control sequence encompassing heat and material flow under the conditions of thermodynamic balance, equipment capacity constraints, and carbon emission limits. The global optimization scheduling scheme is essentially a set of multi-dimensional decision variables, including not only the hierarchical distribution paths of waste heat of different grades in the park's pipeline network, but also the start-up and shutdown logic and power output settings of heat pump units, the charging and discharging timing strategies of thermal energy storage units, and the configuration of the number of parallel-operated mineralization reactor modules. Through this optimization process, the embodiments of this application can dynamically weigh the economic and technical feasibility of directly utilizing high-grade waste heat, using heat pumps to enhance low-grade waste heat, or utilizing stored thermal energy based on real-time electricity price signals, carbon quota constraints, and waste heat prediction data, thereby generating the optimal operating instructions adapted to the current operating conditions.

[0033] As a specific implementation method, model predictive control or mixed integer programming algorithm can be used as the top-level optimizer. It is called at fixed time intervals, and the weighted combination of park energy cost, equivalent carbon cost and mineralization output is used as the objective function to calculate the heat power distribution, heat pump stage and drying unit status of each reactor module in the future period, and then sends it to the middle-level scheduler for execution.

[0034] By implementing the aforementioned global optimization scheduling, this embodiment of the application can achieve refined allocation of energy flow at the park level, significantly reducing dependence on additional fossil fuels or peak electricity, while ensuring that low-grade waste heat is effectively incorporated into the mineralization energy supply path. This collaborative control strategy based on multi-objective optimization not only significantly improves the overall waste heat recovery rate and the economic efficiency of mineralization unit energy consumption in the park, but also enhances the adaptive capability of this embodiment of the application to heat source fluctuations and market signal changes, achieving overall optimization of economic benefits and carbon emission reduction performance.

[0035] S3, according to the global optimization scheduling scheme, waste heat of different grades is transported to the corresponding mineralization reaction stage, and the low-grade waste heat is heated by adjusting the heat pump unit and then supplemented to the medium and low temperature heat use link. At the same time, the feed flow rate, reaction temperature and operating status of the mineralization reactor module and the drying unit are dynamically adjusted.

[0036] This step aims to implement a global optimization scheduling scheme, which achieves the directional transport of multi-source waste heat and dynamic coordination of the reaction process by constructing a thermodynamic matching mechanism between thermal energy grade and mineralization reaction stage.

[0037] The core of this system lies in guiding waste heat according to its temperature grade characteristics to the appropriate reaction stages in the mineralization process. This ensures that high-grade heat energy prioritizes the activation needs of energy-intensive processes, while medium- and low-grade heat energy is used for preheating or auxiliary treatment. For low-grade waste heat that is insufficient for direct utilization, the temperature grade is upgraded using the heat pump unit's thermal enhancement function, and the waste heat is then added to medium- and low-temperature heat utilization stages, thereby expanding the coverage of available heat sources. Based on this, by linking and adjusting key operating parameters of the mineralization reactor module, including feed flow rate, reaction temperature, and the start / stop status of the drying unit, the mineralization reaction rate can be flexibly adjusted in real time according to changes in heat energy supply, forming a closed-loop control logic of "production determined by heat."

[0038] As a specific implementation method, the embodiments of this application can directly transport high-temperature waste heat above a certain threshold to the reaction activation stage, use medium-temperature waste heat for material pretreatment, and use low-temperature waste heat for product drying after being boosted by a heat pump. At the same time, the yield can be continuously adjusted by changing the number of reactor modules operating in parallel and adjusting the feed pump speed of each module.

[0039] Through the aforementioned technical means, a precise match between waste heat grade and the energy consumption requirements of the mineralization reaction is achieved, significantly improving the recovery and utilization rate of low-grade waste heat in the industrial park and avoiding the downgrading of high-quality heat energy or the direct emission of low-quality heat energy. Simultaneously, dynamically adjusting the reactor's operating status allows the mineralization process to adapt to fluctuations in heat source and electricity prices, maximizing mineralization yield and balancing the economic efficiency and carbon performance of the embodiments described in this application while ensuring minimal overall operating costs.

[0040] S4, monitors the actual reaction rate, thermal energy utilization efficiency and equipment operating parameters in real time during the mineralization reaction process, feeds the monitoring data back to the system operation status model to correct prediction deviations, and cyclically executes optimization scheduling and parameter adjustment steps to achieve dynamic matching between mineralization rate and waste heat supply.

[0041] This step aims to eliminate the discrepancy between the system's operational state model and physical reality through a closed-loop feedback mechanism, ensuring a continuous dynamic match between the mineralization rate and waste heat supply. Its core technical approach lies in constructing a control loop that includes data acquisition, deviation correction, and iterative optimization. This involves acquiring key performance indicators and thermal energy conversion efficiency parameters characterizing the mineralization reaction process in real time, and inputting these measured data as correction factors into the system's operational state model to update the internal parameters or boundary conditions of the waste heat supply prediction model and the mineralization reaction demand model.

[0042] Through this online correction method, the embodiments of this application can adaptively compensate for prediction errors caused by heat source fluctuations, equipment performance degradation or environmental interference, thereby triggering the recalculation and execution of the global optimization scheduling scheme, forming a continuous loop control logic from monitoring to adjustment.

[0043] Under this overarching concept, as a specific implementation method, the embodiments of this application can collect data such as the actual reaction rate in the reactor, the COP of the heat pump unit, and the heat exchange efficiency of the heat exchanger. When a short-term excess or insufficient high temperature is detected in the waste heat source, the number of parallel reactor modules, the feed flow rate, or the start-up and shutdown status of the drying unit are automatically adjusted using the corrected model, thereby maximizing the thermal energy utilization efficiency while meeting carbon emission constraints.

[0044] By implementing the aforementioned real-time monitoring and feedback correction steps, the embodiments of this application can effectively overcome the problems of increased energy consumption or reaction stagnation caused by model mismatch in open-loop control, and significantly improve the adaptability to fluctuations in multi-source waste heat. This not only ensures that the mineralization reaction always operates near the optimal operating point, achieving precise dynamic coupling between waste heat supply and mineralization demand, but also extends the stable operating cycle of the equipment through a continuous self-calibration mechanism, ultimately achieving a dual improvement in park-level waste heat recovery rate and carbon fixation efficiency.

[0045] Example 2 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S1 in the industrial park-level carbon dioxide mineralization and waste heat cascade utilization control method: "Real-time collection of temperature and flow data from multiple waste heat generation points within the park, combined with grid electricity price signals and carbon emission constraint data, to construct a system operation status model that includes a waste heat supply prediction model and a mineralization reaction demand model."

[0046] In this embodiment, the specific process of constructing the system operation state model begins with the data acquisition and grade classification stage. This embodiment uses standardized physical and signal interfaces to acquire real-time instantaneous flow data and temperature fluctuation data from each waste heat generation point within the park as input sources. The processing includes comparing the acquired real-time temperature data with preset standardized interface grade limits; specifically, it classifies temperatures greater than a certain threshold. The waste heat was identified as high-temperature waste heat and marked as a priority heat source for the reaction activation stage; the temperature was set at... to Waste heat within the specified range is classified as meso-temperature waste heat and marked as a heat source for heating or pretreatment processes; waste heat with temperatures below a certain range is classified as medium-temperature waste heat. The waste heat is identified as low-temperature waste heat and marked as a heat source that needs to be boosted by an electric heat pump for mineral drying or auxiliary reactions. During this process, embodiments of this application simultaneously establish a heat source capacity model for each grade, incorporating instantaneous flow rate uncertainty fluctuations. The output of this step is a real-time waste heat dataset with grade labels and preliminary heat source capacity characterization parameters.

[0047] Subsequently, based on the aforementioned waste heat dataset with grade labels, this embodiment of the application enters the waste heat supply prediction model generation stage. The input sources are historical process scheduling data for each access point and real-time waste heat data generated in the previous step. The processing employs a lightweight time series algorithm or a long short-term memory network to predict the heat flow and temperature for each access point over the next zero to four hours. This prediction process fully considers the periodic impact and instantaneous fluctuation characteristics of production scheduling on waste heat generation. The output is a waste heat supply prediction model containing future heat flow trends and temperature change curves. This model quantifies the available amount and confidence interval of waste heat of each grade within different time windows.

[0048] Finally, this embodiment integrates and generates a complete system operation status model. Input sources include the previously generated waste heat supply prediction model, real-time measured reactor temperature, carbon dioxide feed rate, product flow rate data, grid electricity price signals, and carbon emission constraint data. The processing involves constructing an energy balance and efficiency model based on the aforementioned real-time measurement data, forming a mineralization reaction demand model that includes equipment capacity constraints and emission limits, and coupling the waste heat supply prediction model with the mineralization reaction demand model. The output is a system operation status model that reflects the dynamic balance of thermal energy supply and demand in the industrial park, equipment operating boundaries, and economic and environmental constraints. This model serves as the foundational data for subsequent global optimization scheduling, ensuring the accuracy and timeliness of control command generation.

[0049] Through the above specific implementation methods, the embodiments of this application realize refined perception and hierarchical modeling of multi-source heterogeneous waste heat, transforming unstructured waste heat data into structured models with clear grade attributes and time prediction characteristics. This not only significantly improves the accuracy of low-grade waste heat identification and utilization, but also provides high-fidelity data support for subsequent dynamic optimization scheduling based on real-time electricity prices and carbon constraints, effectively solving the problem of matching waste heat supply and mineralization demand on a spatiotemporal scale.

[0050] Example 3 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S2 in the industrial park-level carbon dioxide mineralization and waste heat cascade utilization control method: "Based on the system operation state model, with the optimization objectives of minimizing comprehensive operating costs and maximizing mineralization yield, a global optimization scheduling scheme is generated, which includes a waste heat grade allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of parallel operation modules of mineralization reactors."

[0051] In this embodiment, the specific process of generating a globally optimized scheduling scheme first uses real-time electricity price signals, carbon price data, emission quota restrictions, and short-term waste heat production predictions output by the system operation state model as input sources. The control center constructs a multi-objective function that includes minimizing the park's energy costs and maximizing the carbon dioxide mineralization rate. The comprehensive operating cost is defined as the sum of real-time electricity costs and equivalent carbon costs minus the weighted value of mineralization production. The weight parameters are configured by the user according to a strategy that prioritizes carbon reduction or economy. Specifically, during periods of low electricity prices, the objective function automatically tends to prioritize electric heating or heat pump operation to increase the mineralization rate; while during periods of high electricity prices, it automatically reduces the reaction rate and transfers excess heat to the thermal storage unit or implements a shutdown strategy. By solving the multi-objective Pareto optimal solution set, the optimal scheduling instruction sequence under different marginal costs and carbon intensity constraints is generated.

[0052] Subsequently, the processing enters the algorithm solution stage, employing model predictive control or mixed-integer programming algorithms, with the optimizer invoked every five to fifteen minutes. This step uses real-time collected heat source temperature, flow rate data, and grid power constraints as boundary conditions to solve for decision variables such as the heat power allocated to each reactor module, the number of heat pump start-up and shutdown stages, the power of the drying unit, and the heat storage charging and discharging strategy. During the solution process, the algorithm strictly adheres to the thermodynamic balance equations and the minimum operating time constraints of the equipment, ensuring that high-temperature waste heat is preferentially supplied to the activation stage of the mineralization reaction, while medium- and low-temperature waste heat is boosted by heat pumps and matched to the heating or drying stages of the modular reactor.

[0053] Ultimately, the output is a globally optimized scheduling scheme containing specific execution instructions. This scheme clarifies the flow path of waste heat at each grade, the specific operating stage of the heat pump unit, and the number of parallel operation units of the mineralization reactor module. This output directly serves as the input instruction for the next-level real-time scheduler, driving the actuators to complete valve opening adjustment, pump frequency adjustment, and module start-up and shutdown operations, thereby achieving global minimization of fuel and carbon costs.

[0054] This specific implementation method incorporates real-time electricity prices, carbon prices, and emission quotas into closed-loop optimization calculations, achieving a dynamic balance between the economic efficiency and carbon performance of the park's operation. At the same time, it utilizes multi-objective Pareto optimality solutions to ensure the synergistic improvement of waste heat utilization and mineralization yield under complex constraints.

[0055] Example 4 Based on the above embodiments, this embodiment provides a detailed description of the implementation of step S3 in the industrial park-level carbon dioxide mineralization and waste heat cascade utilization control method: "According to the global optimization scheduling scheme, waste heat of different grades is transported to the corresponding mineralization reaction stage, and the low-grade waste heat is heated by adjusting the heat pump unit and then supplemented to the medium and low temperature heat use link, while dynamically adjusting the feed flow rate, reaction temperature and operating status of the mineralization reactor module and the drying unit".

[0056] In this embodiment, the high-temperature waste heat is directly transported to the heat exchanger in the mineralization reaction activation stage according to the waste heat grade classification and allocation strategy, and the medium-temperature waste heat is transported to the pretreatment heating unit. The specific process is as follows: the embodiment of this application receives real-time temperature and flow data from the waste heat collection trunk line of the park as input source, and identifies temperatures higher than the required level. High-temperature waste heat flow and temperature are at The intermediate-temperature waste heat flow; the processing actions include opening the corresponding high-temperature pipeline valve to directly supply the high-temperature waste heat to the heat jacket or heat exchanger of the mineralization reactor module to provide the heat energy required for reaction activation, and at the same time opening the intermediate-temperature pipeline valve to introduce the intermediate-temperature waste heat into the raw material pretreatment heating unit for preheating; the output results are the reactant material that has reached the set reaction temperature in the activation stage and the pretreated material that has completed preheating.

[0057] Subsequently, the modular heat pump unit is activated to heat the low-temperature waste heat. The coefficient of performance (COP) of the heat pump is optimized based on the heat source temperature difference and deployment capacity. The enhanced heat energy is then supplied to the mineral drying unit or auxiliary reaction stages. Specifically, the input source is a temperature below [temperature value missing]. The processing of low-temperature waste heat flow and real-time electricity price signals includes constructing modular heat pump units using reversible heat pumps or heat pump stacking schemes, controlling the compressor speed through inverter speed regulation to adapt to fluctuations in real-time low-temperature waste heat flow, and adjusting the processing based on the heat pump performance coefficient. The optimal operating stage is calculated in real time based on the functional relationship with the heat source temperature difference. When there is a short-term surplus of heat source in the park, the number of heat pump operating stages is increased to absorb the excess low-grade heat and raise the temperature. Alternatively, when the high-temperature waste heat is insufficient, it can automatically switch to medium-temperature waste heat or low-temperature waste heat boosted by the heat pump to replace the heat supply. The output result is a stable heat flow that raises the temperature to meet the requirements of mineral drying or auxiliary reaction, ensuring a continuous supply of heat energy in the mineralization reaction activation stage and the pretreatment stage.

[0058] Finally, the number of parallel-operating mineralization reactor modules is dynamically adjusted according to the optimization instructions, and the feed pump flow rate, reaction jacket temperature, and start / stop status of the solid-liquid separation and product drying units of each operating module are adjusted synchronously. This achieves continuous and adjustable mineralization rate as heat energy supply fluctuates. The input sources are the heat power allocation instructions generated by the global optimization scheduling scheme and the reactor module status feedback. The processing actions include dynamically increasing or decreasing the number of parallel-operated reactor modules according to the total available heat energy, and coordinating the adjustment of the feed pump frequency, jacket heating medium flow rate, and start / stop of the drying unit motor for each operating module. The output result is a mineralization reaction rate that matches the current waste heat supply in real time and a stable product solid content, thereby achieving efficient cascade utilization of low-grade waste heat.

[0059] This implementation method significantly improves the waste heat recovery rate of the park by precisely matching waste heat of different grades to different heat demand stages of the mineralization process, and by combining the flexible temperature control of modular heat pumps with the dynamic adjustment of the number of parallel reactor modules. The waste heat recovery rate is increased from 30% to 70%. At the same time, it realizes the adaptive following of the mineralization rate to the fluctuation of heat energy supply, and reduces the additional fossil energy consumption and unit mineralization energy consumption.

[0060] Example 5 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S4 in the industrial park-level carbon dioxide mineralization and waste heat cascade utilization control method: "real-time monitoring of the actual reaction rate, thermal energy utilization efficiency and equipment operating parameters during the mineralization reaction process, feeding the monitoring data back to the system operating status model to correct prediction deviations, and cyclically executing optimization scheduling and parameter adjustment steps to achieve dynamic matching between mineralization rate and waste heat supply".

[0061] In this embodiment, the specific implementation of step S4 first relies on the real-time acquisition of multi-source sensor data. The input sources include temperature sensors deployed at the inlet and outlet of the mineralization reactor module, flow sensors installed in the carbon dioxide feed pipeline and product outlet pipeline, online analytical instruments for detecting the solid content of the product, and power meters connected to the heat pump unit and drying unit. The processing involves the control center reading the raw signals from the above sensors at a frequency of seconds, filtering and converting them to form a structured dataset containing reactor inlet and outlet temperatures, carbon dioxide feed rate, product flow rate and solid content, and equipment operating power. The output result is a real-time status data stream, which is temporarily stored in the local server database.

[0062] Subsequently, this embodiment calculates the actual thermal energy utilization efficiency and mineralization reaction rate based on the data stream. The input sources are the aforementioned real-time status data stream and the predicted output values ​​in the system operation status model. The processing action involves comparing the unit energy consumption productivity calculated in real time with the theoretical value predicted by the model, and simultaneously applying the heat pump performance coefficient formula. Real-time inversion of the actual operating efficiency of the current heat pump unit; when the actual... When the value is lower than the set threshold or the temperature difference of the heat exchanger increases abnormally, indicating a decline in efficiency, the logic judgment module immediately generates a trigger signal. The output result is a data-driven maintenance command or an automatic bypass strategy, which controls the valve to switch the faulty heat pump or heat exchanger to the backup circuit to ensure continuous operation.

[0063] Furthermore, in this embodiment, the corrected prediction deviation data is input into the cloud optimizer or the local server database. The input source is the deviation value generated in the previous step and the park heat source access status signal. The processing action is to use the deviation data to iteratively update the weight parameters of the short-term waste heat production prediction model and the electricity price heat demand prediction model. In particular, the time series prediction algorithm is recalibrated for the boundary condition changes caused by the constant access or shutdown of heat sources of enterprises in the park. The output result is the updated prediction model parameter set.

[0064] Finally, in the next optimization cycle, the cloud optimizer calls the updated model to resolve the mixed integer programming problem, generating a new global optimization scheduling scheme that includes waste heat grade allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of mineralization reactor modules operating in parallel, thereby achieving dynamic and precise matching between mineralization rate and waste heat supply.

[0065] Through the above specific implementation methods, the embodiments of this application can quickly identify equipment performance degradation based on real-time monitoring data and automatically execute bypass protection, effectively avoiding the shutdown of the entire line due to single-point failure. At the same time, the feedback correction mechanism significantly improves the accuracy of waste heat production and heat demand prediction models, ensuring that the optimal energy efficiency ratio and mineralization yield can still be maintained under the condition of frequent fluctuations in heat source in the park.

[0066] Through the specific implementation methods described above, this embodiment achieves the graded collection and cascaded utilization of multi-source waste heat at the industrial park scale. By prioritizing the allocation of high-temperature waste heat to the mineralization reaction activation stage, and dynamically matching medium- and low-temperature waste heat with modular reactors through cascaded heat pumps, low-grade waste heat that is traditionally difficult to utilize is incorporated into the mineralization energy supply path. Actual measurement data shows that this scheme can increase the overall waste heat recovery rate of the park from the common 30% to 70%. Simultaneously, by considering electricity and carbon prices in real time, this embodiment achieves global minimization of fuel and carbon costs, significantly reducing dependence on additional fossil fuels or high-grade electric heating, improving the economic efficiency and carbon performance of mineralization unit energy consumption, and achieving the technical effect of waste heat-driven mineralization with minimal additional energy consumption and maximizing the utilization of park thermal energy.

[0067] Example 6 This invention also provides an industrial park-level carbon dioxide mineralization and waste heat cascade utilization control device, which includes: The first module is used to collect temperature and flow data from multiple waste heat generation sources in the park in real time, and combine them with grid electricity price signals and carbon emission constraint data to construct a system operation status model that includes a waste heat supply prediction model and a mineralization reaction demand model. The second module is used to generate a global optimization scheduling scheme based on the system operation state model, with the optimization objectives of minimizing the overall operating cost and maximizing the mineralization yield. This scheme includes a waste heat grade allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of mineralization reactor modules operating in parallel. The third module is used to transport waste heat of different grades to the corresponding mineralization reaction stage according to the global optimization scheduling scheme, and to supplement the low-grade waste heat to the medium and low temperature heat use link by adjusting the heat pump unit to raise the temperature. At the same time, the feed flow rate, reaction temperature and operating status of the drying unit of the mineralization reactor module are dynamically adjusted. The fourth module is used to monitor the actual reaction rate, thermal energy utilization efficiency and equipment operating parameters in the mineralization process in real time. It feeds the monitoring data back to the system operation status model to correct prediction deviations and cyclically executes optimization scheduling and parameter adjustment steps to achieve dynamic matching between mineralization rate and waste heat supply.

[0068] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0069] Example 7 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0071] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0072] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for controlling the cascade utilization of carbon dioxide mineralization and waste heat at the industrial park level, characterized in that, Includes the following steps: S1 collects temperature and flow data from multiple waste heat sources in the park in real time, and combines them with grid electricity price signals and carbon emission constraint data to construct a system operation status model that includes a waste heat supply prediction model and a mineralization reaction demand model. S2, based on the system operation state model, with the optimization objectives of minimizing the overall operating cost and maximizing the mineralization yield, a global optimization scheduling scheme is generated, which includes a waste heat grade classification and allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of mineralization reactor modules operating in parallel. S3, according to the global optimization scheduling scheme, waste heat of different grades is transported to the corresponding mineralization reaction stage, and the low-grade waste heat is heated by adjusting the heat pump unit and then supplemented to the medium and low temperature heat use link. At the same time, the feed flow rate, reaction temperature and operating status of the drying unit of the mineralization reactor module are dynamically adjusted. S4, monitors the actual reaction rate, thermal energy utilization efficiency and equipment operating parameters in real time during the mineralization reaction process, feeds the monitoring data back to the system operation status model to correct prediction deviations, and cyclically executes optimization scheduling and parameter adjustment steps to achieve dynamic matching between mineralization rate and waste heat supply.

2. The method as described in claim 1, characterized in that, The system collects real-time temperature and flow data from multiple waste heat sources within the park, and combines this data with grid electricity price signals and carbon emission constraints to construct a system operation status model that includes a waste heat supply prediction model and a mineralization reaction demand model. Real-time flow and temperature fluctuation data of each access point are obtained through standardized physical and signal interfaces, and waste heat is classified into three grades: high temperature waste heat, medium temperature waste heat and low temperature waste heat according to preset temperature thresholds. Based on historical work process scheduling data, a lightweight time series algorithm or long short-term memory network is used to predict the heat flow and temperature of each access point in the next zero to four hours, generating a waste heat supply prediction model. An energy balance and efficiency model is constructed by combining real-time measured reactor temperature, carbon dioxide feed rate, and product flow rate data, forming a mineralization reaction demand model that includes equipment capacity constraints and emission limits, thereby integrating and generating the system operation status model.

3. The method as described in claim 2, characterized in that, The waste heat is classified into three grades—high-temperature waste heat, medium-temperature waste heat, and low-temperature waste heat—based on a preset temperature threshold, including: Real-time comparison of collected temperature data with grade limits defined by standardized interface; waste heat with a temperature greater than 250 degrees Celsius is identified as high-temperature waste heat and marked as a heat source to be given priority for the reaction activation stage. Waste heat with a temperature range of 120 to 250 degrees Celsius is classified as medium-temperature waste heat and marked as a heat source for heating or pretreatment. Waste heat with a temperature below 120 degrees Celsius is classified as low-temperature waste heat and marked as a heat source that needs to be boosted by an electric heat pump for mineral drying or auxiliary reactions. At the same time, a heat source capacity model including the uncertainty fluctuation of instantaneous flow rate is established for each grade to support subsequent tiered allocation.

4. The method as described in claim 1, characterized in that, Based on the system operation state model, with the optimization objectives of minimizing overall operating costs and maximizing mineralization yield, a global optimization scheduling scheme is generated, including a waste heat grade classification and allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of mineralization reactor modules operating in parallel. Construct an objective function that minimizes the park's energy costs, maximizes the carbon dioxide mineralization rate, and constrains electricity consumption and emissions. The comprehensive operating cost includes the sum of real-time electricity costs and equivalent carbon costs minus the weighted value of mineralization output. The optimizer is called every five to fifteen minutes using model predictive control or mixed integer programming algorithm to solve the decision variables of thermal power allocated to each reactor module, number of start-up and shutdown stages of heat pump group, power of drying unit and thermal storage charging and discharging strategy; Under the premise of satisfying thermodynamic balance, minimum equipment operating time and grid power constraints, the output includes a global optimized scheduling scheme that includes a high-temperature heat priority supply activation stage and medium- and low-temperature heat being boosted by heat pumps to match the modular reactor.

5. The method as described in claim 4, characterized in that, The objective function constructed, which aims to minimize the park's energy costs, maximize the carbon dioxide mineralization rate, and constrain electricity consumption and emissions, includes: Set user-configurable target weight parameters to balance the operation strategy of prioritizing carbon reduction or prioritizing economy, and incorporate real-time electricity price signals, peak and valley time period identifiers, carbon price and emission quota data into closed-loop calculation; During periods of low electricity prices, the system automatically selects electric drive heating or heat pump priority operation to increase the mineralization rate; during periods of high electricity prices, the system automatically reduces the reaction rate and transfers excess heat to the heat storage unit or implements a shutdown strategy. By solving the multi-objective Pareto optimal solution set, the optimal scheduling instruction sequence under different marginal cost and carbon intensity constraints is generated to achieve global minimization of fuel cost and carbon cost.

6. The method as described in claim 1, characterized in that, According to the global optimization scheduling scheme, waste heat of different grades is transported to the corresponding mineralization reaction stages, and the low-grade waste heat is heated by adjusting the heat pump unit before being supplemented to the medium and low temperature heat application stage. Simultaneously, the feed flow rate, reaction temperature, and operating status of the drying unit of the mineralization reactor module are dynamically adjusted, including: Based on the waste heat grade classification and distribution strategy, high-temperature waste heat is directly transported to the heat exchanger in the mineralization reaction activation stage, while medium-temperature waste heat is transported to the pretreatment heating unit. The modular heat pump unit is started to heat up the low-temperature waste heat. The coefficient of performance of the heat pump is optimized based on the heat source temperature difference and the deployment capacity. The enhanced heat energy is then added to the mineral drying unit or auxiliary reaction stage. The number of parallel-operating mineralization reactor modules is dynamically adjusted according to the optimization instructions, and the feed pump flow rate, reaction jacket temperature, and start / stop status of solid-liquid separation and product drying units of each operating module are adjusted synchronously to achieve continuous and adjustable mineralization rate as heat energy supply fluctuates.

7. The method as described in claim 6, characterized in that, The modular heat pump unit is activated to heat the low-temperature waste heat. Based on the heat source temperature difference and deployment capacity, the heat pump coefficient of performance is optimized, and the enhanced heat energy is then supplied to the mineral drying unit or auxiliary reaction stage, including: Modular heat pump units are constructed using reversible heat pumps or heat pump stacking schemes, and the compressor speed is controlled by inverter speed regulation to adapt to the flow fluctuations of real-time low-temperature waste heat. The optimal operating stage is calculated in real time based on the functional relationship between the heat pump performance coefficient and the heat source temperature difference. When there is a short-term surplus of heat source in the park, the number of heat pump operating stages is increased to absorb the excess low-grade heat and raise the temperature for use in the drying unit. When high-temperature waste heat is insufficient, it automatically switches to medium-temperature waste heat or low-temperature waste heat boosted by a heat pump to replace the heat supply, ensuring a continuous supply of heat energy for the activation stage of the mineralization reaction and the pretreatment stage, and realizing the efficient cascade utilization of low-grade waste heat.

8. The method as described in claim 1, characterized in that, The real-time monitoring of the actual reaction rate, thermal energy utilization efficiency, and equipment operating parameters during the mineralization process, feeding the monitoring data back to the system operating status model to correct prediction deviations, and cyclically executing optimization scheduling and parameter adjustment steps to achieve dynamic matching between the mineralization rate and waste heat supply, includes: Real-time data on reactor inlet and outlet temperatures, carbon dioxide feed rate, product flow rate and solid content, and equipment operating power are collected using temperature sensors, flow sensors, and power meters. Calculate the actual thermal energy utilization efficiency and mineralization reaction rate and compare them with the output of the prediction model. When fluctuations in the coefficient of performance of the heat pump or a decrease in heat exchange efficiency are detected, trigger data-driven maintenance instructions or automatic bypass strategies. The corrected prediction bias data is input into the cloud optimizer or local server database to update the short-term waste heat production prediction model and the electricity price heat demand prediction model. In the next optimization cycle, a global optimization scheduling scheme is regenerated to respond to changes in the access or shutdown of heat sources in the park.

9. A control device for the cascade utilization of carbon dioxide mineralization and waste heat in an industrial park, characterized in that, include: The first module is used to collect temperature and flow data from multiple waste heat generation sources in the park in real time, and combine them with grid electricity price signals and carbon emission constraint data to construct a system operation status model that includes a waste heat supply prediction model and a mineralization reaction demand model. The second module is used to generate a global optimization scheduling scheme based on the system operation state model, with the optimization objectives of minimizing the overall operating cost and maximizing the mineralization yield. This scheme includes a waste heat grade allocation strategy, heat pump unit start-up and shutdown and power regulation commands, thermal energy storage unit charging and discharging strategy, and the number of mineralization reactor modules operating in parallel. The third module is used to transport waste heat of different grades to the corresponding mineralization reaction stage according to the global optimization scheduling scheme, and to supplement the low-grade waste heat to the medium and low temperature heat use link by adjusting the heat pump unit to raise the temperature. At the same time, the feed flow rate, reaction temperature and operating status of the drying unit of the mineralization reactor module are dynamically adjusted. The fourth module is used to monitor the actual reaction rate, thermal energy utilization efficiency and equipment operating parameters in the mineralization process in real time. It feeds the monitoring data back to the system operation status model to correct prediction deviations and cyclically executes optimization scheduling and parameter adjustment steps to achieve dynamic matching between mineralization rate and waste heat supply.

10. An electronic device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-8.

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