Intelligent optimization control method for garbage treatment power generation and electronic equipment

Abstract

The invention discloses an intelligent optimization control method for garbage disposal power generation and electronic equipment, and relates to the technical field related to garbage disposal, and the method comprises the steps: deploying a garbage sensor network on garbage disposal power generation equipment, and collecting and obtaining a multi-source garbage sensing data flow; performing digital twinborn simulation based on the garbage treatment power generation equipment characteristic parameter flow and the garbage historical power generation data set; constructing a garbage power generation optimization objective function and embedding a garbage treatment power generation digital twinborn body; and performing power generation control solution on the multi-source garbage sensing data flow by using the garbage power generation control digital twin, determining a target power generation control parameter, and performing power generation dynamic scheduling and feedback closed-loop control on the garbage treatment power generation equipment based on the target power generation control parameter. The technical problems of difficult dynamic optimization, insufficient multi-source data fusion capability and poor control strategy real-time performance in the waste incineration power generation process in the prior art are solved, and the technical effects of improving the waste power generation efficiency and optimizing the equipment operation stability are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to garbage disposal, and specifically to an intelligent optimization control method and electronic equipment for garbage disposal power generation. Background Art

[0002] Waste-to-energy incineration combines waste reduction with energy recovery. However, the complex composition of waste, large fluctuations in calorific value, inaccurate equipment operating parameters, and unstable combustion efficiency pose significant challenges to optimizing and controlling the waste-to-energy process. Traditional waste incineration control relies primarily on manual experience and static models, making it difficult to adapt to the dynamic changes in waste characteristics and, consequently, unable to achieve dynamic optimization. This leads to unstable energy conversion efficiency, low power generation efficiency, or excessive pollutant emissions. Furthermore, it is difficult to efficiently integrate heterogeneous data generated by sensor networks. The complex physical and chemical reactions involved in waste incineration make digital twin modeling inaccurate, thus affecting waste-to-energy efficiency and operational stability.

[0003] Therefore, in the current relevant technologies, there are technical problems such as difficulty in dynamic optimization of the waste incineration power generation process, insufficient multi-source data fusion capabilities, and poor real-time performance of control strategies. Summary of the Invention

[0004] This application solves the technical problems existing in the prior art of difficulty in dynamic optimization of the waste incineration power generation process, insufficient multi-source data fusion capabilities, and poor real-time control strategy by providing an intelligent optimization control method and electronic equipment for waste treatment power generation, thereby achieving the technical effect of improving waste power generation efficiency and optimizing equipment operation stability.

[0005] The present application provides an intelligent optimization control method for waste treatment power generation, which includes: deploying a waste sensor network on waste treatment power generation equipment, and collecting and acquiring multi-source waste perception data streams through the waste sensor network; performing digital twin simulation based on the characteristic parameter stream of the waste treatment power generation equipment and the waste historical power generation data set, and building a waste treatment power generation digital twin; decomposing power generation indicators and designing objective functions for the waste treatment power generation target, constructing a waste power generation optimization objective function, and embedding the waste power generation optimization objective function into the waste treatment power generation digital twin to generate a waste power generation control digital twin; using the waste power generation control digital twin to solve the multi-source waste perception data streams for power generation control, determine target power generation control parameters, and perform dynamic power generation scheduling and feedback closed-loop control on the waste treatment power generation equipment based on the target power generation control parameters.

[0006] In a possible implementation, the intelligent optimization control method for waste treatment power generation further performs the following processing: functional system classification of the waste treatment power generation equipment is performed to obtain a waste power generation equipment system architecture, wherein the waste power generation equipment system architecture includes a waste receiving and feeding system, an incinerator system, a flue gas treatment system, a steam turbine system, and a slag sorting system; processing demand analysis is performed on each waste power generation equipment system in the waste power generation equipment system architecture to determine the equipment system waste treatment demand; sensor layout analysis is performed based on the equipment system waste treatment demand to obtain an equipment system sensor layout parameter set; a sensor set is deployed on the waste treatment power generation equipment according to the equipment system sensor layout parameter set and communication networking is performed to obtain the waste sensor network.

[0007] In a possible implementation, the intelligent optimization control method for waste treatment power generation further performs the following processing: performing three-dimensional set modeling based on the characteristic parameter flow of the waste treatment power generation equipment to generate a three-dimensional model of waste treatment power generation; determining the operating constraint parameter set of the waste treatment power generation equipment based on the characteristic parameter flow of the waste treatment power generation equipment; performing waste treatment power generation simulation prediction based on the waste historical power generation data set to obtain a waste power generation simulation prediction network set; performing constraint optimization on the waste treatment power generation three-dimensional model based on the operating constraint parameter set of the waste treatment power generation equipment and the waste power generation simulation prediction network set to obtain a waste treatment power generation digital twin.

[0008] In a possible implementation, the intelligent optimization control method for waste treatment power generation further performs the following processing: obtaining a waste power generation simulation target, performing task extraction on the waste power generation simulation target, and obtaining a waste power generation prediction task set; using the waste power generation prediction task set to associate and integrate the waste historical power generation data set to obtain a waste power generation prediction task sample set; selecting a waste power generation prediction model set based on the waste power generation prediction task sample set; performing prediction training and optimization on the waste power generation prediction task sample set based on the waste power generation prediction model set to obtain the waste power generation simulation prediction network set.

[0009] In a possible implementation, the intelligent optimization control method for waste treatment power generation also performs the following processing: mapping the operating constraint parameter set of the waste treatment power generation equipment to the waste treatment power generation three-dimensional model for constraint coupling to obtain the waste treatment power generation constraint model; associating the waste treatment power generation simulation prediction network set to the waste treatment power generation constraint model for nested twin simulation to generate a waste treatment power generation twin drive model; performing dynamic simulation testing and iterative parameter optimization on the waste treatment power generation twin drive model to obtain the waste treatment power generation digital twin.

[0010] In a possible implementation, the intelligent optimization control method for waste treatment power generation also performs the following processing: decomposing the waste treatment power generation target into power generation index levels to obtain a waste treatment power generation optimization evaluation index system; designing an objective function based on the waste treatment power generation optimization evaluation index system to obtain a multi-level optimization objective function; hierarchically nesting and weighted fusion of the multi-level optimization objective function to construct the waste power generation optimization objective function.

[0011] In a possible implementation, the intelligent optimization control method for waste treatment power generation also performs the following processing: embedding the waste treatment power generation optimization objective function into the waste treatment power generation digital twin to obtain an initial power generation control digital twin; performing operating condition simulation and strategy optimization update on the initial power generation control digital twin to generate the waste power generation control digital twin.

[0012] In a possible implementation, the intelligent optimization control method for waste-to-energy power generation further performs the following processing: initializing a multi-source data filter based on the noise characteristics of the waste sensor network; using the multi-source data filter to filter the multi-source waste sensing data stream to obtain a usable multi-source waste sensing data stream; and solving power generation control for the usable multi-source waste sensing data stream based on the waste-to-energy power generation control digital twin to determine target power generation control parameters.

[0013] In a possible implementation, the intelligent optimization control method for waste treatment power generation further performs the following processing: performing dynamic power generation scheduling and operating status monitoring on the waste treatment power generation equipment based on the target power generation control parameters to obtain the state parameters of the waste power generation equipment; when the state parameters of the waste power generation equipment exceed the preset operating limit, triggering the feedback control loop to perform dynamic closed-loop correction of the target power generation control parameters and waste power generation control.

[0014] The present application also provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing an intelligent optimization control method for waste treatment power generation when executing the executable instructions stored in the memory.

[0015] This application proposes an intelligent optimization control method and electronic equipment for waste-to-energy power generation. A waste sensor network is deployed on waste-to-energy power generation equipment to collect and acquire multi-source waste-sensing data streams. A digital twin simulation is performed based on the characteristic parameter streams of the waste-to-energy power generation equipment and historical waste-to-energy power generation datasets. A waste-to-energy power generation optimization objective function is constructed and embedded in the waste-to-energy power generation digital twin. The waste-to-energy power generation control digital twin is used to solve the power generation control problem for the multi-source waste-to-energy power generation data streams, determine target power generation control parameters, and perform dynamic power generation scheduling and feedback closed-loop control on the waste-to-energy power generation equipment based on the target power generation control parameters. This solves the technical problems of the existing technology, such as the difficulty in dynamically optimizing the waste incineration power generation process, the insufficient multi-source data fusion capabilities, and the poor real-time performance of the control strategy, thereby achieving the technical effect of improving waste-to-energy power generation efficiency and optimizing equipment operational stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A flow chart of an intelligent optimization control method for waste treatment power generation provided in an embodiment of the present application.

[0018] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0019] Description of the accompanying drawings: input device 401, processor 402, memory 403, output device 404. DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The embodiment of the present application provides an intelligent optimization control method for waste treatment power generation, such as Figure 1 As shown, the method includes: Step S100: deploying a garbage sensor network on the garbage processing power generation equipment, and collecting and acquiring multi-source garbage perception data streams through the garbage sensor network.

[0024] Preferably, intelligent sensing equipment distributed in various key links of garbage treatment is used to monitor and collect multi-dimensional data related to garbage characteristics, treatment process and equipment operation status in real time, and then aggregate and transmit them through the Internet of Things to form a continuous multi-source garbage perception data stream. Among them, a variety of sensors constitute a garbage sensor network, which is deployed in various links of garbage treatment, such as feed inlets, incinerators, flue gas treatment systems, etc., including garbage composition sensors for detecting the physical and chemical properties of garbage, such as organic matter content, moisture content, and calorific value; sensors such as floor scales and laser volume scanners for real-time monitoring of garbage feeding amount; temperature sensors such as thermocouples and infrared thermal imagers for monitoring temperature distribution in different areas of the incinerator; gas sensors for detecting the composition of flue gas produced by combustion; humidity sensors for measuring the moisture content of garbage; pressure and flow sensors for monitoring the airflow dynamics in the incinerator and optimizing oxygen supply and combustion efficiency; vibration and acoustic sensors for monitoring; the mechanical state of garbage treatment power generation equipment. The garbage sensor network collects multi-source garbage perception data streams. These data streams are real-time, continuous, and heterogeneous values, images, and spectra from different sensors and different links. These primarily include garbage characteristic data streams such as calorific value, composition, and humidity; combustion process data streams such as furnace temperature, flue gas composition, and combustion efficiency; power generation equipment status data streams such as motor speed, pressure, and vibration; and environmental data streams such as emission pollutant concentrations. This enables precise garbage characteristic perception and equipment status monitoring.

[0025] Furthermore, step S100 also includes step S110, performing functional system classification on the waste treatment power generation equipment to obtain the system architecture of the waste treatment power generation equipment, wherein the system architecture of the waste treatment power generation equipment includes a waste receiving and feeding system, an incinerator system, a flue gas treatment system, a steam turbine system, and a slag sorting system; step S120, performing processing demand analysis on each waste treatment power generation equipment system in the system architecture of the waste treatment power generation equipment to determine the waste treatment demand of the equipment system; step S130, performing sensor layout analysis based on the waste treatment demand of the equipment system to obtain a sensor layout parameter set of the equipment system; step S140, deploying a sensor set on the waste treatment power generation equipment according to the sensor layout parameter set of the equipment system and forming a communication network to obtain the waste sensor network.

[0026] Preferably, the functional systems of the waste treatment power generation equipment are classified to establish a complete waste power generation equipment system architecture, including a waste receiving and feeding system, an incinerator system, a flue gas treatment system, a steam turbine system and a slag sorting system. Among them, the waste receiving and feeding system is responsible for receiving, temporarily storing and transporting the waste to the incinerator. Key equipment includes a waste unloading platform, a crusher, a grab crane, a belt conveyor, etc.; the incinerator system is the core combustion unit, which incinerates the waste at high temperature and releases heat energy. Key equipment includes grate furnace / fluidized bed incinerator, fans and burners and other combustion-supporting equipment; the flue gas treatment system is used to purify the harmful gases produced by combustion to ensure that emissions meet standards. Key equipment includes a deacidification tower, a bag dust collector, an SCR denitrification device, etc.; the steam turbine system is used to convert the heat energy generated by incineration into electrical energy. Key equipment includes a waste heat boiler, a steam turbine, a generator, etc.; the slag sorting system is used to process the residue after incineration and recover reusable materials such as metals. Key equipment includes a magnetic separator, a vibrating screen, a conveyor belt, etc.

[0027] Preferably, for each waste-to-energy system in the waste-to-energy system architecture, its waste treatment requirements are analyzed, namely, the key operating parameters that affect operational efficiency, safety, and environmental protection. For example, the waste receiving and feeding system needs to monitor waste composition, feed volume, metal, and bulky waste; the incinerator system needs to monitor combustion temperature, oxygen concentration, residence time, etc. to ensure sufficient combustion; the flue gas treatment system needs to detect the concentration of pollutants such as SO2, NOx, and dust in real time; the steam turbine system needs to monitor steam pressure, temperature, and flow to ensure power generation efficiency; and the slag sorting system needs to identify metal content and residue thermal loss rate to determine whether combustion is sufficient. Then, based on the results of the processing requirements analysis, the type, quantity, and installation location of the sensors are determined to form a sensor layout parameter set for the equipment system. Sensor types include infrared thermal imagers, gas analyzers, weight sensors, etc., and multiple sensors are required. Installation locations include the incinerator furnace, flue, and feed inlet. For example, weight sensors and moisture meters are deployed in the feeding system to control the waste feed ratio; thermocouples and gas analyzers are deployed in the incinerator to optimize combustion efficiency. Finally, sensors are deployed on the waste treatment power generation equipment according to the equipment system sensor layout parameter set, and communication networking is achieved through industrial Ethernet or LoRa, 5G, etc., and finally a garbage sensor network covering the entire process is built to realize the data collection closed loop of the entire waste power generation process.

[0028] Step S200: Perform digital twin simulation based on the characteristic parameter flow of the waste treatment power generation equipment and the waste historical power generation data set to build a waste treatment power generation digital twin.

[0029] Step S200 further includes step S210, performing three-dimensional set modeling based on the characteristic parameter flow of the waste treatment power generation equipment to generate a three-dimensional model of waste treatment power generation; step S220, determining the operating constraint parameter set of the waste treatment power generation equipment based on the characteristic parameter flow of the waste treatment power generation equipment; step S230, performing waste power generation simulation prediction based on the waste historical power generation data set to obtain a waste power generation simulation prediction network set; step S240, performing constraint optimization on the waste treatment power generation three-dimensional model based on the operating constraint parameter set of the waste treatment power generation equipment and the waste power generation simulation prediction network set to obtain a waste treatment power generation digital twin.

[0030] Preferably, a 3D model of the waste-to-energy power generation system is constructed using CAD or BIM tools based on the geometric structure, mechanical properties, and real-time collected characteristic parameters of the waste-to-energy power generation system, such as incinerator dimensions, heat transfer coefficient, and fan speed, to achieve a visual digital mapping of the physical equipment. The system then analyzes and determines the set of operating constraint parameters for the waste-to-energy power generation system based on the equipment design specifications and real-time operating data such as the incinerator's maximum temperature tolerance threshold, maximum heat load, flue gas emission limits, and steam pressure threshold. These parameters are used to define the simulation boundary conditions of the digital twin and ensure that the virtual model is consistent with the actual operating conditions of the physical equipment. A machine learning model (e.g., LSTM, neural network) or a physical simulation model is then trained based on historical waste-to-energy power generation datasets, such as the calorific value, power generation, and pollutant emission records for the past year, to obtain a waste-to-energy power generation simulation prediction network set, which is used to simulate outputs such as power generation efficiency and pollutant generation under different waste characteristics and operating parameters. Finally, the operating constraint parameter set of the waste treatment power generation equipment and the waste treatment power generation simulation prediction network set are input into the waste treatment power generation three-dimensional model for optimization. That is, the waste treatment power generation three-dimensional model is dynamically adjusted through optimization algorithms such as genetic algorithms and model predictive control, so that it can accurately reproduce the real-time status of the physical equipment and the future waste treatment power generation behavior while meeting safety, environmental protection and other constraints, and finally generate a digital twin of the waste treatment power generation, thereby realizing the virtual and real synchronization of the waste treatment power generation equipment, thereby improving the efficiency and stability of waste treatment power generation.

[0031] Furthermore, step S230 also includes step S231, obtaining a waste-to-energy simulation target, performing task extraction on the waste-to-energy simulation target, and obtaining a waste-to-energy prediction task set; step S232, using the waste-to-energy prediction task set to associate and integrate the waste historical power generation data set to obtain a waste-to-energy prediction task sample set; step S233, selecting a waste-to-energy prediction model set based on the waste-to-energy prediction task sample set; and step S234, performing prediction training and optimization on the waste-to-energy prediction task sample set based on the waste-to-energy prediction model set to obtain the waste-to-energy simulation prediction network set.

[0032] Preferably, obtaining the waste-to-energy simulation goal may include predicting power generation, optimizing combustion efficiency, and reducing pollutant emissions. Task extraction is performed on the waste-to-energy simulation goal, and it is broken down into multiple specific waste-to-energy prediction tasks, such as predicting calorific value based on waste composition, predicting steam production based on operating parameters, etc., thereby forming a waste-to-energy prediction task set. For example, if the waste-to-energy simulation goal is to improve power generation efficiency, the extracted waste-to-energy prediction tasks may include incinerator temperature prediction, garbage calorific value-power generation mapping relationship modeling, etc.; then, the waste-to-energy prediction task set is used to associate and integrate the historical waste power generation data set, that is, according to the prediction task set, related data fields are filtered from the historical power generation data set, such as garbage composition, incineration temperature, power generation, emission indicators, etc., and preprocessing such as time series alignment and missing value filling is performed to form a structured prediction task sample set. For example, for the calorific value-power generation prediction task, historical waste detection data and contemporaneous power generation records are integrated to construct sample pairs of input (waste calorific value, combustion humidity) and output (power generation, gaseous pollutant emissions).

[0033] Preferably, a waste power generation prediction model set is selected according to the waste power generation prediction task sample set, wherein the waste power generation prediction model may include an LSTM time series prediction model, a random forest classification regression model, a combustion kinetics-thermal balance model, etc., and then the waste power generation prediction task sample set is predicted and trained and optimized according to the waste power generation prediction model set, and the prediction accuracy of the waste power generation prediction model is improved through hyperparameter tuning, cross-validation, etc., and finally a set of high-performance prediction models, namely the waste power generation simulation prediction network set, is obtained, which can predict power generation efficiency in real time, simulate equipment behavior under different working conditions, and provide data support for the dynamic optimization of the digital twin.

[0034] Furthermore, step S240 also includes step S241, mapping the operating constraint parameter set of the waste treatment power generation equipment to the waste treatment power generation three-dimensional model for constraint coupling to obtain the waste treatment power generation constraint model; step S242, associating the waste treatment power generation simulation prediction network set to the waste treatment power generation constraint model for nested twin simulation to generate a waste treatment power generation twin driving model; step S243, performing dynamic simulation testing and iterative parameter optimization on the waste treatment power generation twin driving model to obtain the waste treatment power generation digital twin.

[0035] Preferably, the operating constraint parameter set of the waste treatment power generation equipment, such as the temperature limit of the incinerator, the maximum load of the steam turbine, and the environmental emission threshold, is mapped to the waste treatment power generation three-dimensional model for constraint coupling, that is, the constraints such as furnace temperature and flue gas emission concentration are embedded in the waste treatment power generation three-dimensional model in the form of mathematical rules or logical judgments. For example, in the simulation, the temperature parameters are forced to not exceed the safe value, and an emission compliance verification unit is added to the flue gas treatment system. In this way, a virtual model that retains the three-dimensional visualization capability and meets the actual operating constraints is obtained, namely the waste treatment power generation constraint model. Then, the waste treatment power generation simulation prediction network set is associated with the waste treatment power generation constraint model for nested twin simulation, that is, the prediction model and the constraint model are dynamically associated to form a two-way interaction, including forward drive, inputting real-time garbage data, and the prediction model calculating the power generation, pollutants and other results, and feeding them back to the three-dimensional model for dynamic display; reverse constraint, the equipment status in the three-dimensional model is used as a boundary condition to constrain the output range of the prediction model; and then a waste treatment power generation twin drive model that autonomously simulates real working conditions and responds to parameter changes is output. Finally, the twin drive model of waste treatment and power generation was subjected to dynamic simulation testing and iterative parameter optimization. The dynamic simulation test refers to simulating extreme working conditions such as high-humidity garbage entering the furnace and sudden equipment failure in a virtual environment to verify the robustness of the twin drive model; the iterative parameter optimization is through closed-loop feedback, such as comparing actual power generation data with simulation results, and using reinforcement learning to adjust model parameters, such as optimizing the air supply coefficient of the combustion control model and correcting the weight deviation of the calorific value prediction model, and finally outputting a digital twin of waste treatment and power generation that can predict and optimize operating strategies in real time.

[0036] Step S300: Decompose the power generation indicators and design the objective function for the waste treatment power generation target, construct the waste power generation optimization objective function, and embed the waste power generation optimization objective function into the waste treatment power generation digital twin to generate the waste power generation control digital twin.

[0037] Preferably, the power generation indicators of the waste treatment power generation target are decomposed to obtain quantifiable sub-indicators, such as unit waste power generation, steam turbine efficiency, pollutant gas emission concentration, residue thermal ignition loss rate, fuel consumption cost, equipment maintenance frequency, etc., and then a multi-objective optimization function is constructed based on the decomposed indicators in the form of weighted constraints, i.e., the waste power generation optimization objective function. If there are conflicting objectives, such as increasing power generation may lead to increased emissions, the Pareto optimality or constrained optimization method is used to balance the indicators; then the waste power generation optimization objective function is embedded in the waste treatment power generation digital twin, so that it can dynamically calculate the optimal control parameters based on sensor data and simulate the impact of different control strategies on the waste power generation optimization objective function in a virtual environment, and finally form a waste power generation control digital twin, that is, receive real-time sensor data, predict the power generation and emission results of different control schemes based on the waste power generation control digital twin, call the objective function to solve the optimal parameters and then send them to the physical equipment for execution, forming a closed-loop feedback, realizing true intelligent control, and improving the stability and efficiency of waste treatment power generation.

[0038] Furthermore, step S300 also includes step S310, decomposing the power generation index level of the waste treatment power generation target to obtain a waste treatment power generation optimization evaluation index system; step S320, designing the objective function based on the waste treatment power generation optimization evaluation index system to obtain a multi-level optimization objective function; step S330, hierarchically nesting and weighted fusion of the multi-level optimization objective function to construct the waste power generation optimization objective function.

[0039] Preferably, the waste treatment power generation target is decomposed into power generation index levels. Specifically, maximizing energy recovery rate and minimizing comprehensive operating costs are decomposed into primary indicators, including energy efficiency indicators such as power generation efficiency and steam production, environmental protection indicators such as pollutant emission concentration and residue harmlessness rate, and economic indicators such as the cost per ton of waste treatment and equipment maintenance costs. The primary indicators are then decomposed into secondary indicators, including waste calorific value utilization rate, boiler thermal efficiency, pollutant gas emissions, fuel consumption costs, and manual intervention frequency, forming a tree-structured waste treatment power generation optimization evaluation index system that covers all key influencing factors. The objective function is then designed based on the waste treatment power generation optimization evaluation index system, that is, a sub-objective function is designed for each level of indicator and equipment operation restrictions and emission standards are embedded to obtain a multi-level optimization objective function. Finally, the multi-level optimization objective function is hierarchically nested according to the waste treatment power generation optimization evaluation index system, and weighted fusion is performed based on the weights assigned by historical data. Finally, the waste power generation optimization objective function is output, which can be embedded in the waste treatment power generation digital twin for control decision-making.

[0040] Furthermore, step S300 also includes step S340, embedding the waste-to-energy optimization objective function into the waste treatment power generation digital twin to obtain an initial power generation control digital twin; step S350, performing operating condition simulation and strategy optimization update on the initial power generation control digital twin to generate the waste-to-energy control digital twin.

[0041] Preferably, the designed waste-to-energy optimization objective function is embedded into the waste-to-energy digital twin, including a built-in linear programming solver in the waste-to-energy digital twin for real-time calculation of optimal control parameters, establishing a variable mapping relationship between the objective function and the three-dimensional simulation model, and obtaining an initial power generation control digital twin, which can perform decision optimization based on the perception simulation function; then the initial power generation control digital twin is simulated and the strategy optimization is updated. Specifically, typical operating conditions are simulated in the initial power generation control digital twin, including testing control strategies under different waste components (high calorific value / high moisture), simulating emergency plans when equipment fails or environmental protection indicators exceed the standard; then comparing the simulation results with the actual operation data, correcting the model parameters, and when the prediction deviation exceeds the threshold, triggering the reinforcement learning update strategy network of the machine learning model, and updating the operation constraint parameter set in real time; through continuous simulation-optimization cycles, the prediction accuracy and control reliability of the initial power generation control digital twin are improved, and finally a waste-to-energy control digital twin is generated, thereby improving waste-to-energy efficiency and optimizing equipment operation stability.

[0042] Step S400: Using the waste-to-energy control digital twin to solve the power generation control of the multi-source waste sensing data stream, determine the target power generation control parameters, and perform dynamic power generation scheduling and feedback closed-loop control on the waste treatment power generation equipment based on the target power generation control parameters.

[0043] Preferably, a waste-to-energy control digital twin is used to solve the power generation control of multi-source waste sensing data streams, that is, the multi-source waste sensing data streams are injected into the waste-to-energy control digital twin, the overall state of future waste treatment power generation is predicted in a virtual environment, and a multi-objective optimization algorithm is run to solve the optimal solution for power generation control, thereby determining the target power generation control parameters, such as air volume ratio, grate movement speed, etc.; the target power generation control parameters are then converted into control instructions, transmitted to the waste treatment power generation equipment through the industrial bus, and dynamic power generation scheduling is executed, which may include adjusting the waste input amount and feeding speed of the waste receiving and feeding system, optimizing the air door opening and grate movement mode of the incinerator system; then the deviation between the actual power generation and the predicted value is compared in real time, and the key environmental protection indicators are monitored to see whether they meet the standards. When the deviation continues to exceed the threshold, the digital twin model is triggered to retrain and optimize, thereby realizing the feedback update of the target power generation control parameters to complete the feedback closed-loop control, thereby improving the waste treatment power generation efficiency, environmental protection compliance rate and operational stability of the waste treatment power generation equipment.

[0044] Furthermore, step S400 also includes step S410, initializing a multi-source data filter based on the noise characteristics of the waste sensor network; step S420, filtering the multi-source waste sensing data stream using the multi-source data filter to obtain a usable multi-source waste sensing data stream; and step S430, performing power generation control solution on the usable multi-source waste sensing data stream based on the waste-to-energy control digital twin to determine target power generation control parameters.

[0045] Preferably, the garbage sensor network is analyzed and identified to determine the noise characteristics, which may include periodic interference caused by mechanical vibration, signal jumps caused by electromagnetic interference, and baseline drift caused by environmental factors. A hybrid filtering scheme is constructed based on the noise characteristics to initialize the multi-source data filter. Specifically, for fast-fluctuating signals such as furnace temperature, sliding average and wavelet transform are used to eliminate high-frequency noise; for slowly varying signals such as garbage bin level, Kalman filtering is used to predict the true trend; for component analysis data such as calorific value detection, a spectral denoising algorithm is configured to improve the signal-to-noise ratio.

[0046] Preferably, a multi-source data filter is used to filter the multi-source garbage sensing data stream, that is, to process data streams of different sampling frequencies in parallel and dynamically adjust the filtering parameters, such as switching the vibration filter intensity according to the start and stop status of the crusher. Then, sensor readings that clearly exceed physical possibilities are identified and eliminated. Data quality verification is then performed, including physical rationality verification (such as ensuring that the incinerator temperature cannot be lower than the ambient temperature), cross-sensor consistency verification of the coupling relationship between steam flow and power generation, and timing continuity check, thereby obtaining a usable multi-source garbage sensing data stream. The usable multi-source garbage sensing data stream is then input into the waste-to-energy control digital twin for power generation control solution, including predicting the future evolution of the operating state of the waste-to-energy power generation equipment and selecting the optimal comprehensive solution from multiple feasible solutions. Finally, directly executable control instructions are output as target power generation control parameters, such as air volume, grate speed, activated carbon injection rate, and maximum allowable load variation gradient, thereby ensuring the control accuracy, operational stability, and reliability of the waste-to-energy power generation.

[0047] Furthermore, step S400 also includes step S440, performing dynamic power generation scheduling and operating status monitoring on the waste treatment power generation equipment based on the target power generation control parameters to obtain the state parameters of the waste power generation equipment; step S450, when the state parameters of the waste power generation equipment exceed the preset operating limit, triggering the feedback control loop to perform dynamic closed-loop correction and waste power generation control on the target power generation control parameters.

[0048] Preferably, the target power generation control parameters are converted into device-executable control instructions and transmitted to the waste-to-energy power generation equipment to execute dynamic power generation scheduling, including adjusting the damper opening, controlling the waste input rate, and optimizing the activated carbon injection amount. Equipment linkage logic is then established, such as synchronously increasing the air volume and adjusting the grate movement frequency when the calorific value increases, and coordinating the feedwater pump and steam turbine valve adjustment when the steam pressure fluctuates. The sensor network then monitors the operating status in real time to obtain equipment response data, namely, the state parameters of the waste-to-energy power generation equipment, including combustion status, steam pressure and temperature, and flue gas particulate matter concentration. If the state parameters of the waste-to-energy power generation equipment exceed the preset operating limits, a feedback control loop is triggered to dynamically close the target power generation control parameters and control the waste-to-energy power generation. Specifically, the digital twin distinguishes between real anomalies and sensor failures and identifies the anomaly propagation path. Simultaneously, the target power generation control parameters are corrected based on real-time operating conditions. The adjusted control parameters are then used to precisely control the waste-to-energy power generation equipment, thereby improving the efficiency and operational stability of waste-to-energy power generation.

[0049] An intelligent optimization control system for waste treatment power generation provided by an embodiment of the present invention can execute an intelligent optimization control method for waste treatment power generation provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0050] Figure 2 1 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. Figure 2 The electronic device shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present invention. The electronic device is implemented as a general-purpose computing device, and its components may include, but are not limited to, an input device 401, a processor 402, a memory 403, and an output device 404. There may be one or more processors 402; the memory 403 may include computer-readable media and at least one program product, which has a set (at least one) of program modules configured to perform the functions of the various embodiments of the present application.

[0051] The memory 403 shown in the embodiment of the present invention may adopt any combination of one or more computer-readable media; the computer-readable storage medium may be, but is not limited to, an infrared, semiconductor system, device or component, or any combination thereof, for storing software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the intelligent optimization control method for waste treatment power generation in the embodiment of the present invention. The processor 402 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 403, thereby realizing the above-mentioned intelligent optimization control method for waste treatment power generation.

[0052] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0053] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. An intelligent optimization control method for waste treatment power generation, characterized in that: The method comprises: Deploy a garbage sensor network on the garbage processing power generation equipment, and collect and obtain multi-source garbage perception data streams through the garbage sensor network; Conduct digital twin simulation based on the characteristic parameter stream of the waste treatment power generation equipment and the historical waste power generation data set to build a digital twin of the waste treatment power generation equipment; Decomposing power generation indicators and designing objective functions for the waste treatment power generation target, constructing a waste power generation optimization objective function, and embedding the waste power generation optimization objective function into the waste treatment power generation digital twin to generate a waste power generation control digital twin; The waste-to-energy power generation control digital twin is used to solve the power generation control of the multi-source waste sensing data stream, determine the target power generation control parameters, and perform dynamic power generation scheduling and feedback closed-loop control on the waste treatment power generation equipment based on the target power generation control parameters.

2. The intelligent optimization control method for waste treatment power generation according to claim 1, characterized in that: The method of deploying a garbage sensor network on the garbage processing power generation equipment includes: Classifying the waste-to-energy equipment by functional system to obtain a waste-to-energy equipment system architecture, wherein the waste-to-energy equipment system architecture includes a waste receiving and feeding system, an incinerator system, a flue gas treatment system, a steam turbine system, and a slag sorting system; Performing a processing demand analysis on each waste-to-energy equipment system in the waste-to-energy equipment system architecture to determine the waste processing requirements of the equipment system; Perform sensor deployment analysis based on the garbage disposal requirements of the equipment system to obtain a sensor deployment parameter set for the equipment system; A sensor set is deployed on the waste treatment power generation equipment according to the equipment system sensor deployment parameter set and communication networking is established to obtain the waste sensor network.

3. The intelligent optimization control method for waste treatment power generation according to claim 1, characterized in that: The construction of a digital twin for waste treatment power generation includes: Performing three-dimensional collective modeling based on the characteristic parameter flow of the waste treatment power generation equipment to generate a three-dimensional model of the waste treatment power generation equipment; determining an operating constraint parameter set of the waste treatment power generation equipment according to the characteristic parameter flow of the waste treatment power generation equipment; Performing waste-to-energy simulation prediction based on the waste-to-energy historical power generation data set to obtain a waste-to-energy simulation prediction network set; Based on the waste treatment power generation equipment operation constraint parameter set and the waste treatment power generation simulation prediction network set, the waste treatment power generation three-dimensional model is constrained and optimized to obtain a waste treatment power generation digital twin.

4. The intelligent optimization control method for waste treatment power generation according to claim 3, characterized in that: The obtaining of the waste-to-energy simulation prediction network set includes: Acquire a waste-to-energy simulation target, perform task extraction on the waste-to-energy simulation target, and obtain a waste-to-energy prediction task set; Using the waste-to-energy prediction task set to correlate and integrate the waste-to-energy historical power generation data set to obtain a waste-to-energy prediction task sample set; selecting a waste-to-energy prediction model set based on the waste-to-energy prediction task sample set; Based on the waste-to-energy prediction model set, prediction training optimization is performed on the waste-to-energy prediction task sample set to obtain the waste-to-energy simulation prediction network set.

5. The intelligent optimization control method for waste treatment power generation according to claim 3, characterized in that: The digital twin of the waste treatment power generation system is obtained, including: Mapping the operating constraint parameter set of the waste treatment power generation equipment to the waste treatment power generation three-dimensional model for constraint coupling to obtain a waste treatment power generation constraint model; Associating the waste-to-energy power generation simulation prediction network set with the waste-to-energy power generation constraint model to perform nested twin simulation to generate a waste-to-energy power generation twin driving model; Dynamic simulation testing and iterative parameter optimization are performed on the waste treatment power generation twin drive model to obtain the waste treatment power generation digital twin.

6. The intelligent optimization control method for waste treatment power generation according to claim 1, characterized in that: The construction of the waste-to-energy optimization objective function includes: The waste treatment power generation target is broken down into power generation index levels to obtain an optimized evaluation index system for waste treatment power generation; Designing an objective function based on the waste treatment power generation optimization evaluation index system to obtain a multi-level optimization objective function; The multi-level optimization objective function is hierarchically nested and weightedly integrated to construct the waste-to-energy optimization objective function.

7. The intelligent optimization control method for waste treatment power generation according to claim 1, characterized in that: The generation of a waste-to-energy control digital twin includes: Embedding the waste-to-energy optimization objective function into the waste treatment power generation digital twin to obtain an initial power generation control digital twin; The initial power generation control digital twin is subjected to operating condition simulation and strategy optimization and update to generate the waste-to-energy control digital twin.

8. The intelligent optimization control method for waste treatment power generation according to claim 1, characterized in that: The determining of the target power generation control parameter includes: Initializing a multi-source data filter according to the noise characteristics of the garbage sensor network; Using the multi-source data filter to filter the multi-source garbage-aware data stream to obtain a usable multi-source garbage-aware data stream; Based on the waste-to-energy control digital twin, power generation control is solved for the available multi-source waste sensing data stream to determine target power generation control parameters.

9. The intelligent optimization control method for waste treatment power generation according to claim 1, characterized in that: The performing of dynamic power generation scheduling and feedback closed-loop control on the waste treatment power generation equipment based on the target power generation control parameter includes: Performing dynamic power generation scheduling and operating status monitoring on the waste treatment power generation equipment based on the target power generation control parameters to obtain state parameters of the waste treatment power generation equipment; When the state parameters of the waste power generation equipment exceed the preset operating limit, the feedback control loop is triggered to perform dynamic closed-loop correction and waste power generation control on the target power generation control parameters.

10. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the intelligent optimization control method for waste treatment power generation according to any one of claims 1 to 9 when executing the executable instructions stored in the memory.

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