Heat supply mode energy efficiency comparative analysis method, system, equipment and medium

Abstract

The invention discloses a heat supply mode energy efficiency comparative analysis method, system, equipment and medium, and relates to the technical field of energy management and optimizing.The heat supply mode energy efficiency comparative analysis method comprises the steps that multi-parameter water quality analysis data and heat supply thermal parameters are collected, and a feature data set is generated based on dynamic sampling frequency regulation and standardization processing of working condition changes; constructing a multi-target linear programming and grey correlation analysis model according to the feature data set, inputting the standardized heat supply real-time thermal parameters and multi-parameter real-time water quality analysis data into the model, and obtaining a heat supply energy efficiency evaluation result; the fuel blending combustion proportion and the waste water recycling rate are adjusted based on the heat supply energy efficiency evaluation result, the adjustment scheme is dynamically corrected when the heat supply load changes, and a heat supply adjustment scheme is output; according to the method, model parameters are dynamically adjusted through the particle swarm optimization algorithm, and intelligent optimization adjustment of the heat supply system is achieved.

Description

Technical Field

[0001] This invention relates to the field of energy management and optimization technology, specifically to a method, system, equipment, and medium for energy efficiency comparison analysis of heating methods. Background Technology

[0002] As a significant area of ​​energy consumption and carbon emissions, energy efficiency optimization of heating systems is becoming a crucial issue. Most existing heating systems employ fixed fuel ratios and single heating methods, lacking comprehensive analysis and dynamic optimization of the energy efficiency, economic viability, and environmental impact of different heating methods. This results in low energy utilization and significant fuel waste.

[0003] Existing heating energy efficiency analysis methods are mainly based on single-index evaluation, lacking multi-dimensional and multi-objective comprehensive analysis tools, and cannot accurately reflect the energy efficiency characteristics of heating systems under complex operating conditions. Furthermore, in actual production processes, heating loads change frequently, and wastewater reuse rates and fuel blending ratios are difficult to dynamically adjust according to actual operating conditions, resulting in the system's energy efficiency failing to maintain optimal levels. In addition, existing technologies lack the ability to collect and process multi-parameter water quality analysis data and heating thermodynamic parameters in real time, and lack data-driven intelligent optimization models, making it difficult to achieve refined management and efficient operation of heating systems.

[0004] Therefore, there is an urgent need to propose a comparative analysis method for the energy efficiency of heating systems that combines real-time data acquisition of multiple parameters, multi-objective optimization analysis, and intelligent adjustment. By constructing a scientific and reasonable evaluation model and optimization algorithm, the fuel blending ratio and wastewater reuse rate can be dynamically adjusted to achieve a comprehensive improvement in the energy efficiency of the heating system and effective control of carbon emissions, thereby meeting the energy-saving and consumption-reducing requirements of the heating system. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing energy efficiency comparison and analysis methods for heating systems suffer from incomplete data collection, a single energy efficiency evaluation model, and low optimization and adjustment response speed. The problem also involves how to achieve dynamic evaluation and adjustment optimization of heating system energy efficiency through multi-parameter water quality analysis and real-time acquisition of heating thermal parameters, combined with multi-objective optimization and intelligent algorithms.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for energy efficiency comparison analysis of heating methods, including collecting multi-parameter water quality analysis data and heating thermal parameters, and generating a feature dataset based on dynamic sampling frequency adjustment and standardization processing according to changes in operating conditions.

[0008] A multi-objective linear programming and grey relational analysis model is constructed based on the feature dataset. The model weights are determined by the analytic hierarchy process (AHP), and the model parameters are adjusted by the particle swarm optimization algorithm. The standardized real-time heating parameters and multi-parameter real-time water quality analysis data are input into the model to obtain the heating energy efficiency evaluation results.

[0009] The fuel blending ratio and wastewater reuse rate are adjusted based on the heating energy efficiency evaluation results, and the adjustment plan is dynamically revised when the heating load changes, and the heating adjustment plan is output.

[0010] The construction of a multi-objective linear programming and grey relational analysis model includes collecting multi-parameter water quality analysis data and heating thermal parameters, generating a feature dataset based on dynamic sampling frequency control and standardization processing, and then conducting a comprehensive evaluation of heating energy efficiency by combining the multi-objective linear programming and grey relational analysis model.

[0011] Adjusting model parameters includes determining the weights of evaluation indicators using the analytic hierarchy process (AHP) and dynamically optimizing model parameters using the particle swarm optimization algorithm.

[0012] As a preferred embodiment of the energy efficiency comparison analysis method for heating methods described in this invention, the acquisition of multi-parameter water quality analysis data and heating thermal parameters includes installing multi-parameter water quality analyzers, temperature sensors, pressure sensors, and flow meters in the wastewater outlet pipe of wastewater treatment, wastewater reuse pipe, heat exchanger inlet and outlet of heating, and main flue gas exhaust duct of boiler.

[0013] By installing high-precision PT100 temperature sensors and pressure transmitters at the inlet and outlet water pipes of the heat exchanger, the inlet and outlet water temperatures and pipeline pressures are collected in real time. The heating circulation flow data are collected by ultrasonic flow meters or vortex flow meters. The flue gas temperature and combustion conditions are monitored in real time by flue gas temperature sensors and oxygen content analyzers.

[0014] As a preferred embodiment of the energy efficiency comparison analysis method for heating methods described in this invention, the sampling frequency regulation and standardization processing to generate the feature dataset includes: adjusting the sampling period according to the changes in heating load and wastewater flow fluctuations through a dynamic sampling frequency regulation strategy; setting the sampling period to 5 minutes when the load fluctuation rate is less than 10%; automatically adjusting the sampling period to 1 minute when the load fluctuation rate is greater than 10% and the wastewater flow change exceeds a set threshold; performing real-time smoothing processing on the collected data through a moving average algorithm; and normalizing the original data according to a set standardization function to form a complete and time-consistent feature dataset.

[0015] As a preferred embodiment of the energy efficiency comparison analysis method for heating methods described in this invention, the construction of the multi-objective linear programming and grey relational analysis model includes setting the main objective function for energy efficiency evaluation based on the standardized multi-parameter water quality analysis data and heating thermal parameters in the feature dataset.

[0016] The main objective functions include using thermal efficiency, unit energy consumption, unit heating energy consumption, and unit carbon emissions as evaluation indicators, and calculating the energy efficiency value through a comprehensive evaluation function.

[0017] The weighting coefficients are determined using the analytic hierarchy process (AHP). A scoring matrix representing the degree of influence of the indicators is used to calculate the weight values, and a consistency check is performed to determine the final weight allocation.

[0018] In the grey relational analysis process, the correlation between the input indicators and the ideal optimal energy efficiency level is calculated for analysis.

[0019] By calculating the average of all correlation coefficients, the ranking of the indicators' impact on energy efficiency is determined, and the importance ranking of the evaluation indicators is optimized and adjusted by combining a multi-objective linear programming model.

[0020] As a preferred embodiment of the energy efficiency comparison analysis method for heating methods described in this invention, the adjustment of model parameters includes adaptively adjusting each parameter in the model using a particle swarm optimization algorithm, and calculating the heating energy efficiency evaluation result based on the adjusted parameters.

[0021] Initialize the particle swarm optimization algorithm with particle number, inertia weight, individual learning factor and swarm learning factor, maximum number of iterations and convergence accuracy threshold. The particle position represents the variable to be optimized in the heating model.

[0022] The variables to be optimized include the fuel blending ratio, wastewater reuse rate, and the weight coefficients of the evaluation function in the model.

[0023] In each iteration, the particle's current velocity consists of three parts: the inertial part of the previous velocity, the guiding part of the particle's historical optimal solution, and the guiding part of the group's historical optimal solution.

[0024] The new position of the particle is calculated based on the updated velocity, and the new position is subject to boundary constraints and feasibility assessment in the parameter space to complete the update of the particle's velocity and position.

[0025] During the model parameter adjustment process, the parameter combination represented by each group of particles is brought into the multi-objective linear programming and grey relational analysis model. The heating energy efficiency evaluation value is calculated through the comprehensive evaluation function, and the calculation results are fed back to the particle swarm optimization algorithm. Based on the evaluation value, the individual optimal and global optimal solutions in the current particle swarm are determined, and the particle state is updated.

[0026] When the particle swarm optimization algorithm reaches the set maximum number of iterations or the change in the heating energy efficiency evaluation value is less than the set convergence threshold, the iteration stops, and the global optimal solution is the optimal combination of model parameters under the current operating conditions.

[0027] The optimal combination of model parameters includes the final determined fuel blending ratio, wastewater reuse rate, and weighting coefficients for each energy efficiency evaluation index.

[0028] Obtaining heating energy efficiency evaluation results involves inputting the heating thermal parameters and multi-parameter water quality analysis data into the adjusted model, calculating the comprehensive heating energy efficiency evaluation value, and outputting the evaluation value along with the corresponding co-firing ratio and wastewater reuse rate.

[0029] As a preferred embodiment of the energy efficiency comparison analysis method for heating methods described in this invention, the adjustment of fuel blending ratio and wastewater reuse rate includes adjusting fuel supply and wastewater reuse through automated control based on the optimal blending ratio and wastewater reuse rate output from the heating energy efficiency evaluation results.

[0030] Based on the optimal fuel blending ratio calculated by the model, the fuel supply strategy is adjusted. By scheduling the bucket wheel excavator, belt conveyor, and coal blending control, the coal feeding ratio of various coal types is adjusted according to the predetermined ratio to ensure that different coal types are mixed in the coal bunker according to the optimal blending ratio.

[0031] By monitoring the conveying flow of various types of coal in real time, the coal feeding flow is dynamically adjusted using weighing sensors and flow meters, and feedback commands are sent through the controller to maintain the blending ratio.

[0032] Based on the optimal wastewater reuse rate output by the model, the wastewater reuse rate of the wastewater treatment system is adjusted. The wastewater reuse flow rate is regulated by a frequency converter-controlled wastewater reuse pump, and the flow rate and pressure in the wastewater reuse pipeline are controlled by a flow control valve and an automatic regulating valve.

[0033] As a preferred embodiment of the energy efficiency comparison analysis method for heating methods described in this invention, the dynamic correction and adjustment scheme outputs a heating adjustment scheme that includes coordinating the adjustment of fuel blending ratio and wastewater reuse rate through a dispatch center, simultaneously optimizing boiler combustion parameters during the adjustment process, and using a heating load prediction model to predict fuel supply and wastewater reuse demand for a future period of time based on actual heating demand, thereby achieving dynamic balance allocation of fuel and wastewater.

[0034] Another objective of this invention is to provide a heating method energy efficiency comparison and analysis system, which can dynamically adjust model parameters by combining an optimization scheme based on a multi-objective linear programming and grey relational analysis model with a particle swarm optimization algorithm, thereby solving the problems of inaccurate evaluation results, lack of dynamic optimization and adjustment capabilities, and low energy utilization rate in current heating energy efficiency analysis technologies.

[0035] As a preferred embodiment of the energy efficiency comparison and analysis system for heating methods described in this invention, it includes: a data acquisition and processing module, a calculation and evaluation module, and an output adjustment module.

[0036] The acquisition and processing module is used to collect multi-parameter water quality analysis data and heating system thermal parameters, and generate feature datasets based on dynamic sampling frequency adjustment and standardization processing according to changes in operating conditions.

[0037] The calculation and evaluation module is used to construct a multi-objective linear programming and grey relational analysis model based on the feature dataset, determine the model weights through the analytic hierarchy process, and adjust the model parameters using the particle swarm optimization algorithm. The standardized real-time thermal parameters of the heating system and multi-parameter real-time water quality analysis data are input into the model to obtain the heating energy efficiency evaluation results.

[0038] The output adjustment module is used to adjust the fuel blending ratio and wastewater reuse rate based on the heating energy efficiency evaluation results, and dynamically correct the adjustment scheme when the heating load changes, and output the heating adjustment scheme.

[0039] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a method for energy efficiency comparison analysis of heating methods.

[0040] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for comparative analysis of the energy efficiency of heating methods.

[0041] The beneficial effects of this invention are as follows: The energy efficiency comparison and analysis method for heating methods provided by this invention achieves intelligent optimization and regulation of the heating system by dynamically adjusting model parameters using a particle swarm optimization algorithm. By leveraging real-time collected multi-parameter water quality analysis data and heating thermodynamic parameters, the timeliness and completeness of data processing are improved. This invention achieves better results in terms of energy efficiency evaluation accuracy, system optimization response speed, and energy utilization efficiency. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 The first embodiment of the present invention provides an overall flowchart of a heating method energy efficiency comparison analysis method.

[0044] Figure 2 The system structure diagram is provided for a heating method energy efficiency comparison analysis method according to the second embodiment of the present invention. Detailed Implementation

[0045] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. 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 protection scope of the present invention.

[0046] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for energy efficiency comparison analysis of heating methods is provided, comprising:

[0047] S1: Collect multi-parameter water quality analysis data and heating system thermal parameters, and generate a feature dataset based on dynamic sampling frequency adjustment and standardization processing according to changes in operating conditions.

[0048] Multi-parameter water quality analyzers, temperature sensors, pressure sensors, and flow meters are installed in the wastewater outlet pipes of the wastewater treatment system, the wastewater reuse pipes, the heat exchanger inlets and outlets of the heating system, and the main flue gas exhaust duct of the boiler.

[0049] By installing high-precision PT100 temperature sensors and pressure transmitters at the inlet and outlet water pipes of the heat exchanger, the inlet and outlet water temperatures and pipeline pressures are collected in real time. The heating circulation flow data are collected by ultrasonic flow meters or vortex flow meters. The flue gas temperature and combustion conditions are monitored in real time by flue gas temperature sensors and oxygen content analyzers.

[0050] Furthermore, the sampling cycle is adjusted through a dynamic sampling frequency control strategy based on changes in the heating system load and fluctuations in wastewater flow.

[0051] A preferred approach for adjusting the sampling period using a dynamic sampling frequency control strategy is as follows:

[0052]

[0053] Among them, T 采样 This indicates the sampling period time.

[0054] When the load fluctuation rate is less than 10%, the sampling period is set to 5 minutes. When the load fluctuation rate is greater than 10% and the wastewater flow rate changes beyond the set threshold, the sampling period is automatically adjusted to 1 minute. The collected data is smoothed in real time using a moving average algorithm, and the original data is normalized according to the set standardization function to form a complete and time-consistent feature dataset.

[0055] A preferred approach for normalization using a predefined standardization function is as follows:

[0056]

[0057] Where X represents the original collected data value, X 最小 X represents the historical minimum data value. 最大 X represents the historical maximum data value. 标准化 This represents the data value after standardization.

[0058] It should be noted that the multi-parameter water quality analyzer is used to measure the chemical oxygen demand, chloride ion concentration, total salt content and pH value of wastewater in real time, and completes the continuous acquisition and real-time transmission of water quality parameters by configuring automatic sampling combined with optical detection module and electrochemical sensor.

[0059] Various sensors are uniformly connected through a distributed data acquisition controller and transmitted to the data processing center via industrial Ethernet. A time synchronization mechanism is used to timestamp each set of collected data, and the data is classified and organized according to the equipment placement location.

[0060] S2: Construct a multi-objective linear programming and grey relational analysis model based on the feature dataset, determine the model weights through the analytic hierarchy process, and adjust the model parameters using the particle swarm optimization algorithm. Input the standardized real-time heating parameters and multi-parameter real-time water quality analysis data into the model to obtain the heating energy efficiency evaluation results.

[0061] Based on the standardized multi-parameter water quality analysis data and heating thermal parameters in the feature dataset, the main objective function for energy efficiency evaluation is set.

[0062] The main objective functions include using thermal efficiency, unit energy consumption, unit heating energy consumption, and unit carbon emissions as evaluation indicators, and calculating the energy efficiency value through a comprehensive evaluation function.

[0063] A preferred method for calculating energy efficiency values ​​using a comprehensive evaluation function is as follows:

[0064]

[0065] Where E represents the comprehensive energy efficiency evaluation value of heating, α, β, γ, and δ represent the weighting coefficients determined by the analytic hierarchy process, and η represents the thermal efficiency. This represents a standardized value indicating unit energy consumption. This represents the standardized value of energy consumption per unit of heating supply. Standardized value representing unit carbon emissions.

[0066] The weight coefficients are determined using the analytic hierarchy process (AHP). A scoring matrix representing the degree of influence on the indicators is used to calculate each weight value. A consistency check is then performed to ensure that the consistency ratio is less than 0.1, thus determining the final weight allocation.

[0067] In the grey relational analysis process, the correlation between the input indicators and the ideal optimal energy efficiency level is calculated for analysis.

[0068] A preferred approach to grey relational analysis is:

[0069]

[0070] Where, ξ i (k) represents the grey relational coefficient of the i-th indicator at time k, and X0(k) represents the value of the ideal optimal indicator sequence at time k. i (k) represents the value of the i-th actual index sequence at time k, ρ represents the resolution coefficient, and γ i ξ represents the grey relational degree of the i-th indicator, n represents the total number of sampling times, and ξ represents the degree of grey relational degree of the i-th indicator. i (k) represents the grey relational coefficient defined in Formula 4.

[0071] By calculating the average of all correlation coefficients, the ranking of the indicators' impact on energy efficiency is determined, and the importance ranking of the evaluation indicators is optimized and adjusted by combining a multi-objective linear programming model.

[0072] The parameters in the model are adaptively adjusted using the particle swarm optimization algorithm, and the heating energy efficiency evaluation results are calculated based on the adjusted parameters.

[0073] Initialize the particle swarm optimization algorithm with particle number, inertia weight, individual learning factor and swarm learning factor, maximum number of iterations and convergence accuracy threshold. The particle position represents the variable to be optimized in the heating model.

[0074] The variables to be optimized include the fuel blending ratio, wastewater reuse rate, and the weight coefficients of the evaluation function in the model.

[0075] In each iteration, the particle's current velocity consists of three parts: the inertial part of the previous velocity, the guiding part of the particle's historical optimal solution, and the guiding part of the group's historical optimal solution.

[0076] The new position of the particle is calculated based on the updated velocity, and the new position is subject to boundary constraints and feasibility assessment in the parameter space to complete the update of the particle's velocity and position.

[0077] A preferred scheme for particle velocity update is:

[0078]

[0079] in, Let w represent the velocity of the t-th particle in iteration t+1, and w represent the inertia weight. Let c1 represent the velocity of the t-th particle in the t-th iteration, c1 represent the individual learning factor, r1 represent a random number between [0,1], and p i This represents the best position in the history of the i-th particle. Let represent the current position of the i-th particle in the t-th iteration, c2 represent the group learning factor, r2 represent a random number between [0,1] that is not repeated from r1, and g represent the global optimal position of all particles in the current iteration.

[0080] A preferred scheme for particle position updating is:

[0081]

[0082] in, This represents the updated position of particle i in the (t+1)th iteration.

[0083] During the model parameter adjustment process, the parameter combination represented by each group of particles is brought into the multi-objective linear programming and grey relational analysis model. The heating energy efficiency evaluation value is calculated through the aforementioned comprehensive evaluation function. The calculation results are fed back to the particle swarm optimization algorithm. Based on the evaluation value, the individual optimal and global optimal solutions in the current particle swarm are determined, and the particle state is updated.

[0084] When the particle swarm optimization algorithm reaches the set maximum number of iterations or the change in the heating energy efficiency evaluation value is less than the set convergence threshold, the iteration stops, and the global optimal solution is the optimal combination of model parameters under the current operating conditions.

[0085] The optimal combination of model parameters includes the final determined fuel blending ratio, wastewater reuse rate, and weighting coefficients for each energy efficiency evaluation index.

[0086] Obtaining heating energy efficiency evaluation results involves inputting the heating thermal parameters and multi-parameter water quality analysis data into the adjusted model, calculating the comprehensive heating energy efficiency evaluation value, and outputting the evaluation value along with the corresponding co-firing ratio and wastewater reuse rate.

[0087] S3: Adjust the fuel blending ratio and wastewater reuse rate based on the heating energy efficiency evaluation results, and dynamically modify the adjustment plan when the heating load changes, and output the heating adjustment plan.

[0088] Based on the optimal co-firing ratio and wastewater reuse rate output from the heating energy efficiency evaluation results, the fuel supply and wastewater reuse are adjusted through automated control.

[0089] Based on the optimal fuel blending ratio calculated by the model, the feeding strategy of the fuel conveying system is adjusted. By scheduling the bucket wheel excavator, belt conveyor and coal blending control of coal conveying, the coal feeding ratio of various types of coal is adjusted according to the predetermined ratio to ensure that different types of coal are mixed in the coal bunker according to the optimal blending ratio.

[0090] A preferred method for adjusting the fuel blending ratio is as follows:

[0091]

[0092] Among them, P 最优 W represents the optimal fuel blending ratio. i Q represents the supply weight or supply quantity of the i-th type of fuel. i Let m represent the calorific value of the i-th type of fuel, and m represent the number of fuel types.

[0093] Meanwhile, the system monitors the flow rate of various types of coal in real time, dynamically adjusts the coal feeding flow rate using weighing sensors and flow meters, and issues feedback commands through the controller to accurately maintain the blending ratio within the set error range.

[0094] Based on the optimal wastewater reuse rate output by the model, the wastewater reuse of the wastewater treatment system is adjusted. The wastewater reuse flow rate is regulated by a frequency converter-controlled wastewater reuse pump, and the flow rate and pressure in the wastewater reuse pipeline are regulated by a flow control valve and an automatic regulating valve.

[0095] During the adjustment process, the chemical oxygen demand, chloride ion concentration, total salt content and pH value of the wastewater are monitored in real time by the online water quality analyzer in the wastewater reuse system. When the water quality indicators meet the requirements for heating reuse, the wastewater reuse flow rate is automatically increased.

[0096] When the wastewater quality fluctuation exceeds the set threshold, the system reduces the wastewater reuse flow rate and temporarily suspends the reuse process. At the same time, it adjusts the operating status of the heat exchangers in the heating system to prevent the wastewater quality fluctuation from affecting the heat exchange efficiency and equipment safety of the heating system.

[0097] The heating system's dispatch center coordinates the adjustment of fuel blending ratios and wastewater reuse rates, and simultaneously optimizes boiler combustion parameters during the adjustment process.

[0098] Synchronous optimization includes adjusting the ratio of primary air and secondary air, boiler combustion temperature, and main steam parameters.

[0099] Combustion conditions are dynamically adjusted through boiler control and combustion optimization control algorithms to adapt to changes in combustion characteristics caused by new fuel blending ratios. A heating load prediction model, combined with actual heating demand, forecasts future fuel supply and wastewater reuse needs, achieving dynamic balance and allocation of fuel and wastewater to ensure stable operation of the heating system.

[0100] Example 2, refer to Figure 2 As an embodiment of the present invention, a heating method energy efficiency comparison and analysis system is provided, including a data acquisition and processing module 100, a calculation and evaluation module 200, and an output adjustment module 300.

[0101] S4: The acquisition and processing module 100 is used to acquire multi-parameter water quality analysis data and heating system thermal parameters, and generate a feature dataset based on dynamic sampling frequency adjustment and standardization processing according to changes in operating conditions.

[0102] It should also be noted that the data acquisition and processing module 100 first collects multi-parameter water quality analysis data and heating system thermal parameters in real time through a distributed data acquisition device, including key indicators such as wastewater chemical oxygen demand, chloride ion concentration, total salt content, pH value, thermal efficiency, and fuel consumption rate. Based on changes in operating conditions, the sampling frequency is dynamically adjusted, and the data is smoothed using a moving average algorithm and normalized using a standardization function to form a time-consistent and standardized feature dataset. The feature dataset is then passed as input data to the calculation and evaluation module 200.

[0103] S5: The calculation and evaluation module 200 is used to construct a multi-objective linear programming and grey relational analysis model based on the feature dataset, determine the model weights through the analytic hierarchy process, and adjust the model parameters using the particle swarm optimization algorithm. The standardized real-time thermal parameters of the heating system and the multi-parameter real-time water quality analysis data are input into the model to obtain the heating energy efficiency evaluation results.

[0104] It should also be noted that the calculation and evaluation module 200 receives the feature dataset transmitted by the acquisition and processing module 100, performs energy efficiency evaluation based on the multi-objective linear programming model and the grey relational analysis model, calculates the weight of each evaluation index through the analytic hierarchy process, and uses the particle swarm optimization algorithm to adaptively optimize and adjust the model parameters to determine the optimal fuel blending ratio and wastewater reuse rate. It then calculates the comprehensive evaluation result of heating energy efficiency and transmits the calculated evaluation results and optimal parameters to the output adjustment module 300.

[0105] S6: Output adjustment module 300 is used to adjust the fuel blending ratio and wastewater reuse rate based on the heating energy efficiency evaluation results, and dynamically correct the adjustment scheme when the heating load changes, and output the heating adjustment scheme.

[0106] It should also be noted that, based on the heating energy efficiency evaluation results and optimization parameters output by the calculation and evaluation module 200, the output adjustment module 300 adjusts the fuel blending ratio and wastewater reuse rate through automated control. At the same time, it monitors and provides feedback in real time based on changes in heating load, and adjusts the working status of fuel supply and wastewater reuse through control feedback signals to achieve dynamic optimization adjustment of fuel blending ratio and wastewater reuse rate. The adjustment results are then fed back to the acquisition and processing module 100, forming a closed-loop control and continuous optimization dynamic adjustment mechanism. Finally, a heating adjustment scheme is output to maximize operating efficiency and improve energy utilization.

[0107] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0109] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0110] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for comparative analysis of the energy efficiency of heating methods, characterized in that, include: Collect multi-parameter water quality analysis data and heating thermal parameters, and generate a feature dataset based on dynamic sampling frequency adjustment and standardization processing according to changes in operating conditions; A multi-objective linear programming and grey relational analysis model is constructed based on the feature dataset. The model weights are determined by the analytic hierarchy process and the model parameters are adjusted by the particle swarm optimization algorithm. The standardized real-time heating parameters and multi-parameter real-time water quality analysis data are input into the model to obtain the heating energy efficiency evaluation results. Based on the heating energy efficiency evaluation results, the fuel blending ratio and wastewater reuse rate are adjusted, and the adjustment plan is dynamically revised when the heating load changes, and the heating adjustment plan is output. The construction of a multi-objective linear programming and grey relational analysis model includes collecting multi-parameter water quality analysis data and heating thermal parameters, generating a feature dataset based on dynamic sampling frequency control and standardization processing, and combining the multi-objective linear programming and grey relational analysis model to conduct a comprehensive evaluation of heating energy efficiency. Adjusting model parameters includes determining the weights of evaluation indicators using the analytic hierarchy process (AHP) and dynamically optimizing model parameters using the particle swarm optimization algorithm.

2. The energy efficiency comparison analysis method for heating methods as described in claim 1, characterized in that: The collected multi-parameter water quality analysis data and heating thermal parameters include, Multi-parameter water quality analyzers, temperature sensors, pressure sensors, and flow meters are installed in the wastewater outlet pipes of wastewater treatment, wastewater reuse pipes, heat exchanger inlets and outlets of heating, and main flue gas exhaust ducts of boilers. By installing high-precision PT100 temperature sensors and pressure transmitters at the inlet and outlet water pipes of the heat exchanger, the inlet and outlet water temperatures and pipeline pressures are collected in real time. The heating circulation flow data are collected by ultrasonic flow meters or vortex flow meters. The flue gas temperature and combustion conditions are monitored in real time by flue gas temperature sensors and oxygen content analyzers.

3. The energy efficiency comparison analysis method for heating methods as described in claim 2, characterized in that: The sampling frequency adjustment and standardization process generates a feature dataset, including: Based on changes in heating load and wastewater flow fluctuations, the sampling period is adjusted through a dynamic sampling frequency control strategy. When the load fluctuation rate is less than 10%, the sampling period is set to 5 minutes. When the load fluctuation rate is greater than 10% and the wastewater flow change exceeds the set threshold, the sampling period is automatically adjusted to 1 minute. The collected data is smoothed in real time using a moving average algorithm, and the original data is normalized according to a set standardization function to form a complete and time-consistent feature dataset.

4. The energy efficiency comparison analysis method for heating methods as described in claim 3, characterized in that: The construction of the multi-objective linear programming and grey relational analysis model includes, Based on the standardized multi-parameter water quality analysis data and heating thermal parameters in the feature dataset, the main objective function for energy efficiency evaluation is set. The main objective functions include using thermal efficiency, unit energy consumption, unit heating energy consumption, and unit carbon emissions as evaluation indicators, and calculating the energy efficiency value through a comprehensive evaluation function; The weight coefficients are determined by the analytic hierarchy process (AHP). A scoring matrix of the degree of influence of the indicators is used to calculate the weight values, and the final weight allocation is determined by a consistency test. In the grey relational analysis process, the correlation between the input indicators and the ideal optimal energy efficiency level is calculated for analysis. By calculating the average of all correlation coefficients, the ranking of the indicators' impact on energy efficiency is determined, and the importance ranking of the evaluation indicators is optimized and adjusted by combining a multi-objective linear programming model.

5. The energy efficiency comparison analysis method for heating methods as described in claim 4, characterized in that: The adjusted model parameters include, The parameters in the model are adaptively adjusted using the particle swarm optimization algorithm, and the heating energy efficiency evaluation results are calculated based on the adjusted parameters. Initialize the particle number, inertia weight, individual learning factor and swarm learning factor, maximum number of iterations and convergence accuracy threshold of the particle swarm optimization algorithm; the position of the particle represents the variable to be optimized in the heating model. The variables to be optimized include the fuel blending ratio, wastewater reuse rate, and the weight coefficients of the evaluation function in the model; In each iteration, the particle's current velocity consists of three parts: the inertial part of the previous velocity, the guiding part of the particle's historical optimal solution, and the guiding part of the group's historical optimal solution. The new position of the particle is calculated based on the updated velocity, and the new position is subject to boundary constraints and feasibility assessment in the parameter space to complete the update of the particle's velocity and position. During the model parameter adjustment process, the parameter combination represented by each group of particles is brought into the multi-objective linear programming and grey relational analysis model. The heating energy efficiency evaluation value is calculated through the comprehensive evaluation function. The calculation results are fed back to the particle swarm optimization algorithm. Based on the evaluation value, the individual optimal and global optimal solutions in the current particle swarm are determined, and the particle state is updated. When the particle swarm optimization algorithm reaches the set maximum number of iterations or the change in the heating energy efficiency evaluation value is less than the set convergence threshold, the iteration stops, and the global optimal solution is the optimal combination of model parameters under the current operating condition. The optimal combination of model parameters includes the final determined fuel blending ratio, wastewater reuse rate, and weighting coefficients for each energy efficiency evaluation index. Obtaining heating energy efficiency evaluation results involves inputting the heating thermal parameters and multi-parameter water quality analysis data into the adjusted model, calculating the comprehensive heating energy efficiency evaluation value, and outputting the evaluation value along with the corresponding co-firing ratio and wastewater reuse rate.

6. The energy efficiency comparison analysis method for heating methods as described in claim 5, characterized in that: The adjustment of fuel blending ratio and wastewater reuse rate includes, Based on the optimal co-firing ratio and wastewater reuse rate output from the heating energy efficiency evaluation results, the fuel supply and wastewater reuse are adjusted through automated control. Based on the optimal fuel blending ratio calculated by the model, the fuel supply strategy is adjusted. By scheduling the bucket wheel excavator, belt conveyor and coal blending control of coal transportation, the coal feeding ratio of various types of coal is adjusted according to the predetermined ratio to ensure that different types of coal are mixed in the coal bunker according to the optimal blending ratio. By monitoring the conveying flow of various types of coal in real time, the coal feeding flow is dynamically adjusted using weighing sensors and flow meters, and feedback commands are sent through the controller to maintain the blending ratio. Based on the optimal wastewater reuse rate output by the model, the wastewater reuse rate of the wastewater treatment system is adjusted. The wastewater reuse flow rate is regulated by a frequency converter-controlled wastewater reuse pump, and the flow rate and pressure in the wastewater reuse pipeline are controlled by a flow control valve and an automatic regulating valve.

7. The energy efficiency comparison analysis method for heating methods as described in claim 6, characterized in that: The dynamic correction and adjustment scheme outputs a heating adjustment scheme, including... The dispatch center coordinates the adjustment of fuel blending ratio and wastewater reuse rate. During the adjustment process, the boiler combustion parameters are optimized synchronously. By using the heating load prediction model and combining the actual heating demand, the fuel supply and wastewater reuse demand for a period of time are predicted, and dynamic balance and allocation of fuel and wastewater are carried out.

8. A heating method energy efficiency comparison and analysis system, characterized in that: It includes a data acquisition and processing module (100), a calculation and evaluation module (200), and an output adjustment module (300); The acquisition and processing module (100) is used to acquire multi-parameter water quality analysis data and heating system thermal parameters, and generate feature datasets based on dynamic sampling frequency adjustment and standardization processing according to changes in operating conditions. The calculation and evaluation module (200) is used to construct a multi-objective linear programming and grey relational analysis model based on the feature dataset, determine the model weights through the analytic hierarchy process, and adjust the model parameters using the particle swarm optimization algorithm. The standardized real-time thermal parameters of the heating system and the multi-parameter real-time water quality analysis data are input into the model to obtain the heating energy efficiency evaluation results. The output adjustment module (300) is used to adjust the fuel blending ratio and wastewater reuse rate based on the heating energy efficiency evaluation results, and dynamically correct the adjustment scheme when the heating load changes, and output the heating adjustment scheme.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the heating method energy efficiency comparison analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the heating method energy efficiency comparison analysis method according to any one of claims 1 to 7.

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