Intelligent control method and system for heat supply parameters of thermal power plant

Abstract

The invention relates to the technical field of intelligent control, in particular to an intelligent control method and system for heat supply parameters of a thermal power plant, and the method comprises the following steps: capturing temperature sensor data of multiple key parts of the thermal power plant, combining operation time with external weather condition data, carrying out data integration, analyzing the current operation state, and obtaining heat balance basic data. According to the method, comprehensive analysis is carried out in combination with external weather conditions, dynamic adjustment of heat supply parameters is achieved, the reaction speed is increased, the fuel ratio and load are optimized, the energy utilization efficiency is improved, long-term weather prediction data are integrated, heat supply demand changes are predicted and adapted, energy waste is reduced, and the energy utilization efficiency is improved. Real-time load adjustment and fuel efficiency calculation enable the thermal power plant to accurately match the actual heat demand, the combustion efficiency is optimized, and by predicting maintenance points and potential faults, the equipment operation reliability is improved, the maintenance plan is updated in time, the production safety is ensured, the operation flexibility and economic benefits are improved, and the environmental pollution risk is reduced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent control method and system for heating parameters of a thermal power plant. Background Art

[0002] The field of intelligent control technology involves the use of advanced algorithms and technologies to achieve automated and optimized control systems, including machine learning, artificial intelligence, neural networks, and fuzzy logic control. In industrial applications, intelligent control can improve production efficiency, reduce energy consumption, and reduce operational complexity. Its core lies in dynamically adjusting industrial processes through real-time data analysis to respond to changes in environmental and operating conditions. Intelligent control is common in automated manufacturing, aerospace, vehicle power management, and energy management, and aims to achieve the purpose of improving safety, reliability, and economic benefits through precise parameter control.

[0003] Among them, the intelligent control method of heating parameters of thermal power plants is a practice of applying intelligent control technology to the heating system of thermal power plants. The main purpose of this subject is to accurately adjust the heating parameters of thermal power plants through integrated data analysis and automated control strategies. This can not only effectively improve energy utilization efficiency, but also shorten the response time of the method to changing heating demand, thereby reducing energy waste and environmental pollution. By realizing intelligent control of heating parameters, thermal power plants can manage their heating operations more flexibly and efficiently, in line with modern energy management and environmental protection requirements.

[0004] Although existing technologies cover high-end algorithms such as machine learning and artificial intelligence, in actual industrial applications, especially in the control of heating systems in thermal power plants, the technologies are unable to fully utilize on-site real-time data for effective energy management. Existing technologies are deficient in real-time data processing and dynamic parameter adjustment, and rely on preset control logic and fixed parameters, which limits their flexibility and efficiency in the face of sudden environmental changes and immediate adjustments to heating demand. Existing technologies lack adaptive predictions of long-term weather changes, resulting in the inability to guarantee the energy efficiency and heating quality of thermal power plants under extreme weather conditions. The technology limits the performance of thermal power plants in energy management and environmental protection, and cannot meet the requirements of modern energy policies for high efficiency and low pollution. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for intelligently controlling heating parameters of a thermal power plant.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a method for intelligently controlling heating parameters of a thermal power plant, comprising the following steps:

[0007] S1: Capture temperature sensor data from multiple key parts of thermal power plants, combine operating time and external weather conditions data, integrate data, analyze current operating status, and obtain basic heat balance data;

[0008] S2: using the basic heat balance data, analyzing the long-term weather forecast data, adjusting the hot water temperature and flow rate to the optimal initial values, dynamically adjusting the parameters and matching the predicted heating demand, and obtaining the adjusted heating parameters;

[0009] S3: Analyze the matching of fuel consumption and heating demand through the adjusted heating parameters, adjust the fuel ratio and input amount, optimize the combustion efficiency, calculate the fuel efficiency, and obtain the fuel ratio optimization data;

[0010] S4: According to the fuel ratio optimization data, adjust the load of the thermal power plant heating equipment, perform real-time load adjustment and match the real-time heat demand, perform load regulation feedback analysis, and obtain load adjustment feedback data;

[0011] S5: Utilizing the load adjustment feedback data, analyzing the equipment operation status, predicting maintenance points and potential failures, performing failure prediction analysis, and generating a preventive maintenance plan;

[0012] S6: Based on the preventive maintenance plan, update the maintenance and operation schedule, dispatch resources, conduct operation efficiency analysis, monitor the maintenance execution effect, and generate the heating parameter adjustment performance evaluation result.

[0013] As a further solution of the present invention, the basic thermal balance data includes sensor identification, measurement accuracy, and data acquisition frequency; the adjusted heating parameters include optimal flow parameters, temperature adjustment range, and adjustment verification time; the fuel ratio optimization data includes real-time combustion rate, optimized calorific value, and ratio adjustment reaction time; the load adjustment feedback data includes adjusted load balance, feedback efficiency, and real-time adjustment records; the preventive maintenance plan includes a key component inspection list, scheduled maintenance time, and risk assessment indicators; the heating parameter adjustment performance evaluation results include energy-saving effects, improved heating quality, and improved system response speed.

[0014] As a further solution of the present invention, the steps of capturing temperature sensor data of multiple key parts of a thermal power plant, combining the operating time and external weather condition data, integrating the data, analyzing the current operating status, and obtaining basic heat balance data are as follows:

[0015] S101: Capture temperature sensor data of multiple key parts of the thermal power plant, record the operating time of key parts, synchronously capture external weather condition data, integrate through data timestamp tags, merge data fields, and generate monitoring data sets;

[0016] S102: Based on the monitoring data set, remove noise and outliers, match the processed data with the original performance data, adjust analysis parameters and refine the comparison, identify key performance fluctuations, and obtain status analysis results;

[0017] S103: Utilizing the state analysis results, extracting key data of the energy efficiency and operation efficiency of the thermal power plant, analyzing the data to determine the balance point of thermal efficiency, and obtaining basic thermal balance data.

[0018] As a further solution of the present invention, the steps of using the basic heat balance data, analyzing the long-term weather forecast data, adjusting the hot water temperature and flow rate to the optimal initial values, dynamically adjusting the parameters and matching the predicted heating demand, and obtaining the adjusted heating parameters are as follows:

[0019] S201: Based on the heat balance basic data, long-term weather forecast data is integrated, and the temperature trend and weather changes are compared in parallel, and the data are synchronized in time series, and the standard values ​​of the preliminary hot water temperature and flow rate are calibrated to obtain the initial heating setting;

[0020] S202: Analyze the difference between the real-time weather conditions and the forecast data according to the initial heating setting, perform integrated processing of the real-time data stream, adjust the heating parameters and match the demand fluctuations predicted in the short term, and generate adjusted heating parameter settings;

[0021] S203: Utilize the adjusted heating parameter settings to fine-tune the real-time heating parameters, continuously monitor external weather conditions and internal response data, adjust the flow and temperature according to the feedback from the heating equipment, optimize the response sensitivity and heating efficiency of the heating equipment, and generate adjusted heating parameters.

[0022] As a further solution of the present invention, the steps of analyzing the matching of fuel consumption and heating demand through the adjusted heating parameters, adjusting the fuel ratio and input amount, optimizing the combustion efficiency, and performing fuel efficiency calculation to obtain the fuel ratio optimization data are specifically as follows:

[0023] S301: Based on the adjusted heating parameters, fuel consumption data is captured in real time, the associated heating demand is recorded synchronously, and data comparison and analysis are performed to quantitatively measure the matching degree between consumption and demand by identifying the difference between current fuel usage and heating demand, and obtain fuel demand matching data;

[0024] S302: Analyze the efficiency of the current fuel ratio according to the fuel demand matching data, adjust the ratio of differentiated fuel types, determine the optimal fuel ratio by continuously monitoring the combustion effects under multiple ratios, and obtain optimized fuel ratio data;

[0025] S303: Using the optimized fuel ratio data, adjusting the fuel input amount, adjusting the fuel supply amount according to the continuously updated heating demand data, calculating the fuel consumption efficiency, and obtaining the fuel ratio optimization data.

[0026] As a further solution of the present invention, according to the fuel ratio optimization data, the load of the thermal power plant heating equipment is adjusted, real-time load adjustment is performed and matched with the real-time heat demand, and load regulation feedback analysis is performed. The steps of obtaining load adjustment feedback data are specifically as follows:

[0027] S401: Based on the fuel ratio optimization data, real-time monitoring and adjustment of the operating parameters of the heating equipment of the thermal power plant are performed to capture the change in heat demand, and the load setting is adjusted according to the captured data to obtain preliminary load matching data;

[0028] S402: Analyze the difference between real-time and original load data using the preliminary load matching data, adjust the operating parameters of the heating equipment according to demand fluctuations, and optimize the load adjustment through detailed adjustments to obtain detailed load adjustment data;

[0029] S403: Based on the refined load adjustment data, feedback analysis of load adjustment is performed, and the matching degree between the efficiency of load adjustment and the real-time demand is determined through comparative analysis to form load adjustment feedback data.

[0030] As a further solution of the present invention, the steps of using the load adjustment feedback data to analyze the equipment operation status, predict maintenance points and potential failures, perform failure prediction analysis, and generate a preventive maintenance plan are specifically as follows:

[0031] S501: Based on the load adjustment feedback data, monitor the current operating parameters and original performance data of multiple heating equipment, compare and analyze the data item by item, determine the current status and potential risks of equipment operation by identifying abnormal fluctuations and performance degradation in the operating parameters, and generate equipment operation status results;

[0032] S502: using the equipment operation status results, applying linear regression analysis technology, analyzing key maintenance points and equipment performance degradation trends, combining with original equipment failure records, evaluating failure causes and predicting the time of failure occurrence, and obtaining maintenance point and failure prediction data;

[0033] S503: Based on the maintenance points and failure prediction data, maintenance activities, required resources and schedule, potential equipment failures are avoided and a preventive maintenance plan is created.

[0034] As a further solution of the present invention, the formula of the linear regression analysis technology is as follows:

[0035]

[0036] Among them, Y represents the predicted performance index of the equipment, β 0 represents the intercept of the linear regression model, β 1 Represents the direct impact weight of equipment age on performance degradation, β 2 represents the weight of the impact of maintenance times on equipment performance, β 3 represents the adjustment factor, X 1 Represents the service life of the equipment, X 2 Represents the number of maintenance times, X 3 Represents the number of abnormal indicators before the failure occurs, X 4 Represents the time interval since the last maintenance activity.

[0037] As a further solution of the present invention, the steps of updating the maintenance and operation schedule, scheduling resources, performing operation efficiency analysis, monitoring the maintenance execution effect, and generating the heating parameter adjustment performance evaluation result according to the preventive maintenance plan are specifically as follows:

[0038] S601: Based on the preventive maintenance plan, the maintenance and operation schedule of the thermal power plant is adjusted, resources of the maintenance unit and the operation team are reallocated, and a resource scheduling and schedule optimization plan is created through detailed resource and time management;

[0039] S602: Using the resource scheduling and schedule optimization solution, multiple maintenance and operation tasks are started, and data on task execution, including start time, progress, and deviation, are collected in real time, and task execution efficiency results are formed by continuously tracking activities;

[0040] S603: Based on the task execution efficiency results, analyze the maintenance execution effect, analyze the impact of each maintenance activity on the overall equipment performance and heating efficiency, quantify the contribution of the maintenance activities, and formulate the heating parameter adjustment performance evaluation results.

[0041] An intelligent control system for heating parameters of a thermal power plant, the intelligent control system for heating parameters of a thermal power plant is used to execute the above-mentioned intelligent control method for heating parameters of a thermal power plant, the system comprises:

[0042] The initial state assessment module captures temperature sensor data and operation time data of multiple key parts of the thermal power plant, and combines the information with external environmental condition data to analyze the current operation state of the heating configuration and obtain baseline operation parameters;

[0043] The parameter adjustment module uses the baseline operating parameters and combines the long-term weather forecast to adjust the hot water temperature and flow rate to match the expected heat load demand, and controls the temperature and flow rate to obtain adjusted heating parameters;

[0044] The efficiency optimization module evaluates the fuel consumption through the adjusted heating parameters, adjusts the fuel ratio, optimizes the fuel input and ratio, and obtains the optimized fuel configuration;

[0045] The real-time adjustment module dynamically adjusts the load of the heating equipment according to the optimized fuel configuration to match the real-time heat demand, implements continuous monitoring of load adjustment, and obtains load adjustment data;

[0046] The operation and maintenance strategy formulation module uses the load regulation data to regularly monitor the operating status of the equipment, determines the maintenance timing and potential failure points through predictive analysis, and obtains the performance evaluation results of the heating parameter adjustment.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by capturing the temperature sensor data of key parts of the thermal power plant in real time and combining it with the external weather condition data for comprehensive analysis, dynamic adjustment of heating parameters is achieved, which not only improves the reaction speed, but also effectively improves the energy utilization efficiency by optimizing the fuel ratio and load adjustment. By integrating and using long-term weather forecast data, it is possible to predict and adapt to changes in heating demand, thereby reducing energy waste. Real-time load adjustment and fuel efficiency calculation enable thermal power plants to more accurately match actual heat demand and further optimize combustion efficiency. By predicting maintenance points and potential failures, the operating reliability of equipment is improved, and through timely updating of maintenance plans, continuous production safety is ensured, the heating operation flexibility and economic benefits of thermal power plants are improved, and the risk of environmental pollution is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a method for intelligently controlling heating parameters of a thermal power plant, comprising the following steps:

[0060] S1: Capture temperature sensor data from multiple key parts of thermal power plants, combine operating time and external weather conditions data, integrate data, analyze current operating status, and obtain basic heat balance data;

[0061] S2: Using basic heat balance data and analyzing long-term weather forecast data, adjust the hot water temperature and flow rate to the optimal initial values, dynamically adjust the parameters and match the predicted heating demand to obtain the adjusted heating parameters;

[0062] S3: Analyze the matching of fuel consumption and heating demand through the adjusted heating parameters, adjust the fuel ratio and input amount, optimize the combustion efficiency, calculate the fuel efficiency, and obtain the fuel ratio optimization data;

[0063] S4: According to the fuel ratio optimization data, adjust the load of the thermal power plant heating equipment, perform real-time load adjustment and match the real-time heat demand, perform load regulation feedback analysis, and obtain load adjustment feedback data;

[0064] S5: Use load adjustment feedback data to analyze equipment operating conditions, predict maintenance points and potential failures, perform fault prediction analysis, and generate preventive maintenance plans;

[0065] S6: Based on the preventive maintenance plan, update the maintenance and operation schedule, dispatch resources, conduct operation efficiency analysis, monitor the maintenance execution effect, and generate the heating parameter adjustment performance evaluation results.

[0066] The basic data of heat balance include sensor identification, measurement accuracy, and data collection frequency. The adjusted heating parameters include optimal flow parameters, temperature adjustment range, and adjustment verification time. The fuel ratio optimization data include real-time combustion rate, optimized calorific value, and ratio adjustment reaction time. The load adjustment feedback data include adjusted load balance, feedback efficiency, and real-time adjustment records. The preventive maintenance plan includes a key component inspection list, scheduled maintenance time, and risk assessment indicators. The performance evaluation results of heating parameter adjustment include energy-saving effects, improved heating quality, and improved system response speed.

[0067] Specifically, if Figure 2 As shown in the figure, the steps of capturing temperature sensor data of multiple key parts of a thermal power plant, combining the operating time and external weather condition data, integrating the data, analyzing the current operating status, and obtaining the basic data of heat balance are as follows:

[0068] S101: Capture temperature sensor data of multiple key parts of the thermal power plant, record the operation time of key parts, synchronously capture external weather condition data, integrate through data timestamp marking, merge data fields, and generate the monitoring data set. The execution process is as follows;

[0069] In the monitoring equipment of thermal power plants, operating data is captured by temperature sensors installed in multiple key locations. This data is synchronized with external weather condition data to ensure the timeliness and accuracy of the information obtained. The data is marked with a timestamp and integrated in the database to ensure the continuity and integrity of the data. The monitoring of temperature data plays a vital role in predicting equipment failures and optimizing maintenance plans. Through real-time data processing, thermal power plants can monitor temperature changes in key locations in real time and adjust operating strategies accordingly to maximize efficiency and reduce energy consumption. Through data integration and analysis, it can reflect the working status of the equipment and real-time changes in environmental factors in real time, providing decision support for operators.

[0070] S102: Based on the monitoring data set, remove noise and outliers, match the processed data with the original performance data, adjust the analysis parameters and refine the comparison, identify key performance fluctuations, and obtain the execution process of the status analysis results as follows;

[0071] Match the processed data with the original performance data according to the formula:

[0072]

[0073] Calculate performance fluctuations, where P represents performance fluctuations, N represents the total number of data points, and O i represents the observed value, E i represents the expected value;

[0074] Refer to the observed value O of each data point i i and the expected value E i , the overall performance fluctuation P can be calculated by averaging the sum of squared deviations of all N data points. For example, if there are 5 data points with observed values ​​of [100, 105, 98, 103, 101] and expected values ​​of [100, 100, 100, 100, 100], the calculation process is as follows:

[0075]

[0076] The results show that the performance fluctuation is 6.8, which shows the degree of fluctuation shown by the observed data compared with the original performance data, which can guide further performance optimization.

[0077] S103: Using the state analysis results, extract the key data of the energy efficiency and operation efficiency of the thermal power plant, and analyze the data to determine the balance point of thermal efficiency. The execution process of obtaining the basic data of thermal balance is as follows;

[0078] When determining the balance point of thermal efficiency of a thermal power plant, it is necessary to first extract key energy efficiency and operating efficiency data through the state analysis results, and capture the operating point with the best energy efficiency by comparing historical data with current operating conditions. The process involves complex data analysis and thermodynamic calculations. The acquisition of basic thermal balance data is achieved through detailed analysis of the energy consumption data and production data of the equipment, and correlation analysis of the data with environmental factors such as temperature and pressure, so as to determine the optimal operating state of the equipment under differentiated conditions, thereby maximizing energy utilization and optimizing costs. The final thermal balance basic data reflects the actual operating efficiency of the equipment under current external conditions and internal operating conditions, providing a scientific basis for further efficiency improvement and cost control.

[0079] Specifically, if Figure 3 As shown, the basic data of heat balance is used to analyze the long-term weather forecast data, adjust the hot water temperature and flow rate to the optimal initial value, dynamically adjust the parameters and match the predicted heating demand, and obtain the adjusted heating parameters in the following steps:

[0080] S201: Based on the basic data of heat balance, the long-term weather forecast data is integrated, and the time series of the data is synchronized by comparing the temperature trend and weather changes in parallel, and the standard values ​​of the initial hot water temperature and flow rate are calibrated to obtain the execution process of the initial heating setting as follows;

[0081] Based on the basic data of heat balance, long-term weather forecast data is integrated, and parallel comparison is made by comparing temperature trends and weather changes to ensure the time series synchronization of all data. Data integration is completed through advanced data processing algorithms, including data alignment and time stamping, to ensure the continuity and comparability of data. This synchronization is achieved by using advanced data analysis techniques, including data interpolation and outlier processing. The technology helps to accurately predict future temperature and weather changes, thereby providing a scientific basis for thermal power plants to formulate preliminary standard values ​​for hot water temperature and flow rate. The preliminary heating setting standard values ​​obtained from data analysis provide initial parameters for subsequent operations and adjustments.

[0082] S202: According to the initial heating setting, the difference between the real-time weather conditions and the forecast data is analyzed, and the real-time data stream is integrated and processed to adjust the heating parameters and match the demand fluctuations predicted in the short term. The execution process of generating the adjusted heating parameter settings is as follows;

[0083] Analyze the difference between real-time weather conditions and forecast data, according to the formula:

[0084] A=α×(W t -W p )+β×D

[0085] Calculate the adjusted heating parameters, where A represents the adjustment amount of the heating parameters, α and β represent the adjustment coefficients, and W t Represents real-time weather data, W p represents predicted weather data, and D represents demand fluctuation;

[0086] Refer to the difference between real-time and forecast weather data W t -W p And demand fluctuation D, according to the deviation of real-time data and the change of demand, the heating parameter A that needs to be adjusted can be calculated by adjusting the coefficients α and β. For example, if the real-time weather temperature is 15°C, the predicted weather temperature is 18°C, the demand fluctuation is 20, and the adjustment coefficients α = 0.5 and β = 0.3, the calculation process is as follows:

[0087] A=0.5×(15-18)+0.3×20=-1.5+6=4.5

[0088] The results show that the heating parameters need to be adjusted by 4.5 units to adapt to weather changes and demand fluctuations.

[0089] S203: Using the adjusted heating parameter settings, fine-tune the real-time heating parameters, continuously monitor external weather conditions and internal response data, adjust the flow and temperature according to the feedback of the heating equipment, optimize the reaction sensitivity and heating efficiency of the heating equipment, and generate the execution process of the adjusted heating parameters as follows;

[0090] The adjusted heating parameter settings are used to monitor and fine-tune the heating parameters in real time. By continuously monitoring external weather conditions and internal response data, the flow and temperature of the heating equipment can be dynamically adjusted according to the real-time data. The process involves complex data analysis and real-time feedback mechanisms, including real-time data acquisition and processing to ensure the accuracy and real-time nature of the data. By comparing and analyzing equipment feedback and external weather data, the heating parameters are slightly adjusted to optimize the heating efficiency and equipment response sensitivity. Dynamic adjustment ensures that the equipment can maintain the best operating state under changing environmental conditions. The generated adjusted heating parameters provide a basis for precise control of thermal power plants in actual operations, thereby achieving effective energy utilization and cost optimization.

[0091] Specifically, if Figure 4 As shown, by adjusting the heating parameters, analyzing the matching of fuel consumption and heating demand, adjusting the fuel ratio and input amount, optimizing the combustion efficiency, and calculating the fuel efficiency, the steps of obtaining the fuel ratio optimization data are as follows:

[0092] S301: Based on the adjusted heating parameters, the fuel consumption data is captured in real time, the associated heating demand is recorded synchronously, and data comparison and analysis are performed. By identifying the difference between the current fuel usage and the heating demand, the matching degree between the consumption and the demand is quantitatively measured. The execution process of obtaining the fuel demand matching data is as follows;

[0093] Based on the adjusted heating parameters, fuel consumption data is captured in real time, data associated with heating demand is recorded synchronously, and data comparison and analysis are performed. This process mainly relies on advanced data acquisition technology and analysis methods, among which data comparison and analysis involves the use of statistical and machine learning techniques to identify and interpret patterns and trends in the data. By quantitatively analyzing the difference between current fuel usage and heating demand, the degree of match between consumption and demand can be effectively measured. It includes not only data collection and processing, but also control and evaluation of data quality to ensure the accuracy of the analysis results. The obtained fuel demand matching data provides a basis for adjusting the heating system and optimizing fuel use, thereby improving the system's energy efficiency and reducing operating costs.

[0094] S302: According to the fuel demand matching data, the efficiency of the current fuel ratio is analyzed, the ratio of the differentiated fuel types is adjusted, and the optimal fuel ratio is determined by continuously monitoring the combustion effect under multiple ratios. The execution process of obtaining the optimized fuel ratio data is as follows;

[0095] Adjust the proportion of differentiated fuel types according to the formula:

[0096]

[0097] Calculate the optimal fuel ratio, where R represents the optimal fuel ratio, Ti Represents the combustion efficiency of each type of fuel, F i Represents the amount of each type of fuel used;

[0098] Refer to the combustion efficiency T of various types of fuel i and the corresponding usage F i , the optimal fuel ratio R can be calculated by the weighted average method. There are three fuels with combustion efficiencies of 0.8, 0.75, and 0.78, and usage of 100, 150, and 120, respectively. The calculation process is as follows:

[0099]

[0100] The results show that the optimal fuel ratio is about 0.773, which means that the overall combustion efficiency of the fuel can be optimized by adjusting the usage ratio of differentiated types of fuel.

[0101] S303: Using the optimized fuel ratio data, adjusting the fuel input, adjusting the fuel supply according to the continuously updated heating demand data, calculating the fuel consumption efficiency, and obtaining the fuel ratio optimization data, the execution process is as follows;

[0102] By optimizing fuel ratio data, adjusting fuel input, and adjusting fuel supply according to continuously updated heating demand data, the process involves advanced control algorithms and real-time data processing technology. By calculating fuel consumption efficiency, not only can fuel use be optimized, but fuel supply can also be dynamically adjusted according to real-time heating demand to ensure efficient operation of the heating system. This real-time adjustment mechanism relies on continuous data monitoring and analysis to ensure that each fuel is supplied in the optimal ratio, thereby achieving energy saving and cost reduction. The fuel ratio optimization data finally obtained provides a scientific basis for thermal power plants to adjust heating parameters in real time to adapt to changing operational needs.

[0103] Specifically, if Figure 5 As shown, according to the fuel ratio optimization data, the load of the thermal power plant heating equipment is adjusted, real-time load adjustment is performed and matched with the real-time heat demand, and load regulation feedback analysis is performed. The specific steps for obtaining load adjustment feedback data are as follows:

[0104] S401: Based on the fuel ratio optimization data, the operation parameters of the heating equipment of the thermal power plant are monitored and adjusted in real time, the changes in heat demand are captured, and the load setting is adjusted according to the captured data. The execution process of obtaining preliminary load matching data is as follows;

[0105] Adjust the load setting based on the captured data, according to the formula:

[0106]

[0107] Calculate preliminary load matching data, where Q represents load, ΔH represents heat demand change, R represents fuel consumption rate, and W represents time interval;

[0108] Assuming that within a certain time interval W, the heat demand increases by ΔH units and the fuel consumption rate is R, the load Q that needs to be adjusted can be calculated using the above formula. For example, if the heat demand increases by 200 units, the fuel consumption rate is 0.5 units / hour, and the time interval is 4 hours, the calculation process is as follows:

[0109]

[0110] The results show that the load needs to be adjusted by 100 units to match the increase in heat demand, which reflects the accuracy and real-time responsiveness of the real-time monitoring and adjustment process.

[0111] S402: Analyze the difference between real-time and original load data using preliminary load matching data, adjust the operating parameters of the heating equipment according to demand fluctuations, and optimize the load adjustment through detailed adjustments to obtain detailed load adjustment data. The execution process is as follows;

[0112] Utilize preliminary load matching data to analyze the difference between real-time and original load data, and adjust the operating parameters of heating equipment according to demand fluctuations. Through this detailed adjustment, load adjustment can be effectively optimized to ensure the flexibility and responsiveness of the heating system. During the adjustment process, various environmental factors and actual operating conditions are referred to. By using advanced control algorithms and real-time data processing technology, each adjustment is based on the most accurate data analysis to achieve the best operating effect. This adjustment not only improves the energy efficiency of the system, but also ensures the quality of heating and user satisfaction. The final refined load adjustment data provides a scientific basis for future operating strategies.

[0113] S403: Based on the refined load adjustment data, a feedback analysis of load adjustment is performed, and the matching degree between the efficiency of load adjustment and the real-time demand is determined through comparative analysis. The execution process of forming the load adjustment feedback data is as follows;

[0114] Based on the refined load adjustment data, feedback analysis of load regulation is carried out, including comparative analysis to determine the efficiency of load regulation and the degree of matching with real-time demand. Through the feedback mechanism, strategies can be adjusted in time to cope with real-time changes. Feedback analysis relies on advanced data analysis and real-time monitoring technology. Through continuous data updates and model adjustments, it ensures that each load adjustment can match real-time demand to the greatest extent. This not only improves operating efficiency, but also reduces energy waste. The resulting load adjustment feedback data provides valuable information resources for the continuous optimization of the heating system, enabling the system to respond to market and environmental changes more accurately.

[0115] Specifically, if Figure 6 As shown in the figure, the steps of using load adjustment feedback data to analyze equipment operating conditions, predict maintenance points and potential failures, conduct fault prediction analysis, and generate a preventive maintenance plan are as follows:

[0116] S501: Based on the load adjustment feedback data, the current operating parameters and original performance data of multiple heating equipment are monitored, and the data are compared and analyzed item by item. By identifying abnormal fluctuations and performance degradation in the operating parameters, the current status and potential risks of the equipment operation are determined, and the execution process of generating the equipment operation status result is as follows;

[0117] Based on load adjustment feedback data, the current operating parameters and original performance data of multiple heating equipment are monitored, and the data are compared and analyzed item by item to identify abnormal fluctuations in operating parameters and performance degradation. Data analysis technologies such as cluster analysis and anomaly detection algorithms are used to identify abnormal patterns in the data to ensure that potential operating problems of the equipment are discovered in a timely manner. Through analysis, the current status and potential risks of equipment operation can be accurately determined. The generated equipment operating status results provide key decision support information for the maintenance team, assisting in the formulation of more effective maintenance strategies and measures.

[0118] S502: Using the equipment operation status results, applying linear regression analysis technology, analyzing the key maintenance points and the performance degradation trend of the equipment, combining with the original equipment failure records, evaluating the cause of the failure and predicting the time of the failure, and obtaining the maintenance point and failure prediction data. The execution process is as follows;

[0119] The formula for the linear regression analysis technique is as follows:

[0120]

[0121] Among them, Y represents the predicted performance index of the equipment, β 0 represents the intercept of the linear regression model, β 1 Represents the direct impact weight of equipment age on performance degradation, β 2 represents the weight of the impact of maintenance times on equipment performance, β 3 represents the adjustment factor, X 1 Represents the service life of the equipment, X 2 Represents the number of maintenance times, X 3 Represents the number of abnormal indicators before the failure occurs, X 4 represents the time interval between the last maintenance activity;

[0122] Detailed explanation of the formula and the process of formula calculation and derivation:

[0123] β 0It is the intercept of the linear regression model, obtained by fitting historical data. This value represents the expected value of the basic performance of the equipment without any external factors. For example, based on the analysis of equipment failure data in the past five years, the intercept value is set to 85, which represents the performance index of the equipment in its initial state.

[0124] β 1 is the weight of the equipment's service life on performance degradation. This parameter is obtained by analyzing the data relationship between historical performance and service life. If the equipment performance decreases by 2.5% for each additional year, then β 1 Set to -0.025;

[0125] β 2 It is the weight of the impact of maintenance times on equipment performance, which is determined by statistical analysis of the correlation between maintenance activities and performance improvement. It is assumed that maintenance can improve performance, and each maintenance improves the performance index by 0.03, that is, β 2 =0.03;

[0126] X 1 The service life of the equipment is set to be 3 years.

[0127] X 2 is the number of maintenance times, assuming that the equipment has been maintained 5 times in the past year;

[0128] X 3 Represents the number of abnormal indicators before the fault occurs, for example, the number of abnormalities in a certain period is 8;

[0129] X 4 is the time since the last maintenance, set to 6 months, with a conversion day of 180 days;

[0130] Specific calculation process:

[0131] According to the formula Substitute the set parameters and coefficients,

[0132]

[0133] in

[0134] Then calculate 0.2×0.596=0.1192;

[0135] Finally, Y = 85 + 0.075 + 0.15 + 0.1192 = 85.3442;

[0136] The results show that under the conditions of the current equipment service life, maintenance frequency and number of abnormal indicators, the predicted equipment performance index is 85.3442. This performance index helps the maintenance team understand the equipment performance degradation trend and failure time, thereby optimizing maintenance plans and failure prevention measures. In this way, early maintenance or adjustments can effectively extend the life of the equipment and reduce unexpected downtime.

[0137] S503: The execution process of creating a preventive maintenance plan based on maintenance points and failure prediction data, maintenance activities, required resources and schedule to avoid potential equipment failures is as follows;

[0138] Planning maintenance activities, required resources and schedules based on maintenance points and failure prediction data involves optimal resource allocation and time management strategies. Through detailed planning, potential equipment failures can be effectively avoided, and a preventive maintenance plan can be created. This plan includes necessary maintenance operations, a list of required spare parts and tools, and estimated maintenance time. This approach not only reduces the predictability of equipment failures, but also improves the operating efficiency and reliability of the equipment, ensuring the continuity and safety of the production process. The creation of a preventive maintenance plan provides scientific and systematic support for equipment management, enabling the maintenance team to more effectively respond to various failure situations that arise.

[0139] Specifically, if Figure 7 As shown in the figure, according to the preventive maintenance plan, the maintenance and operation schedules are updated, resources are dispatched, operation efficiency analysis is performed, maintenance execution effects are monitored, and the steps for generating the heating parameter adjustment performance evaluation results are as follows:

[0140] S601: Based on the preventive maintenance plan, the maintenance and operation schedule of the thermal power plant is adjusted, and the resources of the maintenance unit and the operation team are reallocated. Through detailed resource and time management, the execution process of creating a resource scheduling and schedule optimization plan is as follows;

[0141] Based on the preventive maintenance plan, the maintenance and operation schedule of the thermal power plant was adjusted, the resource and time management were refined, and the resource allocation of the maintenance units and operation teams was optimized. The refinement process included determining the specific amount of resources and scheduling time required for each maintenance unit, and adjusting the operation schedule through algorithm optimization calculation to ensure the maximum efficiency of resource utilization. The operation team performed its daily tasks according to the optimized resource allocation. The reallocation of resources involved referring to the maintenance frequency of the equipment and the urgency of the maintenance tasks, and dynamically adjusting the operation schedule to adapt to changes in maintenance cycles and operation requirements. At the same time, advanced resource management systems were used to monitor and schedule resources in real time to ensure that the timing of each operation was optimized under resource constraints, thereby achieving overall equipment performance and heating efficiency Improved, and ultimately formed a resource scheduling and schedule optimization plan.

[0142] S602: Using the resource scheduling and schedule optimization solution, multiple maintenance and operation tasks are initiated, and data on task execution, including start time, progress, and deviation, are collected in real time. By continuously tracking activities, the execution process of forming task execution efficiency results is as follows;

[0143] Collect data about task execution in real time, according to the formula:

[0144]

[0145] Calculate the task execution efficiency results, where T a Represents the actual time required to complete the task, T p represents the time planned to complete the task, and B represents the percentage of task execution efficiency;

[0146] Assuming that the actual completion time of a specific task is 50 hours and the planned time is 60 hours, the task execution efficiency is calculated as follows:

[0147]

[0148] This shows that the efficiency of task execution is 83.33%, indicating that task execution is more efficient than the expected plan, which helps to further optimize the resource allocation and time management of subsequent tasks.

[0149] S603: Based on the task execution efficiency results, analyze the maintenance execution effect, analyze the impact of each maintenance activity on the overall equipment performance and heating efficiency, quantify the contribution of the maintenance activity, and formulate the execution process of the heating parameter adjustment performance evaluation results as follows;

[0150] Based on the results of task execution efficiency, the impact of maintenance activities was quantitatively analyzed. This analysis process included evaluating the specific contribution of each maintenance activity to equipment performance and heating efficiency. By analyzing the data, the maintenance activities that have the greatest impact on efficiency were identified, and the maintenance plan was optimized accordingly. The maintenance execution effect analysis covered the comparative analysis of the equipment's operating data and historical maintenance records. Advanced statistical methods and performance evaluation techniques were used to determine the contribution of each maintenance activity. A simulation model was used to predict and evaluate maintenance activities in future time periods in order to achieve optimal resource input and time allocation, thereby improving the overall heating efficiency. A heating parameter adjustment performance evaluation result based on the above analysis was developed to ensure the maximum benefit of maintenance activities and achieve continuous optimization of equipment performance.

[0151] like Figure 8 As shown, an intelligent control system for heating parameters of a thermal power plant, the system includes:

[0152] The initial state assessment module captures temperature sensor data and operation time data of multiple key parts of the thermal power plant, and combines the information with external environmental condition data to analyze the current operation state of the heating configuration and obtain baseline operation parameters;

[0153] The parameter adjustment module uses baseline operating parameters and long-term weather forecasts to adjust the hot water temperature and flow rate to match the expected heat load demand, and controls the temperature and flow rate to obtain the adjusted heating parameters;

[0154] The efficiency optimization module adjusts the heating parameters, evaluates the fuel consumption, adjusts the fuel ratio, optimizes the fuel input and ratio, and obtains the optimized fuel configuration;

[0155] The real-time regulation module dynamically adjusts the load of the heating equipment according to the optimized fuel configuration to match the real-time heat demand, implements continuous monitoring of load regulation, and obtains load regulation data;

[0156] The operation and maintenance strategy formulation module uses load regulation data to regularly monitor the operating status of the equipment, determines maintenance opportunities and potential failure points through predictive analysis, and obtains performance evaluation results for heating parameter adjustment.

[0157] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for intelligently controlling heating parameters of a thermal power plant, characterized in that: The following steps are involved: Capture temperature sensor data from multiple key locations in thermal power plants, combine operating time with external weather conditions, integrate data, analyze current operating status, and obtain basic thermal balance data; The heat balance basic data is used to analyze the long-term weather forecast data, adjust the hot water temperature and flow rate to the optimal initial values, dynamically adjust the parameters and match the predicted heating demand, and obtain the adjusted heating parameters; By using the adjusted heating parameters, the matching of fuel consumption and heating demand is analyzed, the fuel ratio and input amount are adjusted, the combustion efficiency is optimized, the fuel efficiency is calculated, and the fuel ratio optimization data is obtained; According to the fuel ratio optimization data, the load of the thermal power plant heating equipment is adjusted, real-time load adjustment is performed and matched with real-time heat demand, load regulation feedback analysis is performed, and load adjustment feedback data is obtained; Utilizing the load adjustment feedback data, analyzing the equipment operation status, predicting maintenance points and potential failures, performing failure prediction analysis, and generating a preventive maintenance plan; According to the preventive maintenance plan, the maintenance and operation schedules are updated, resources are dispatched, operation efficiency analysis is performed, maintenance execution effects are monitored, and heating parameter adjustment performance evaluation results are generated.

2. The intelligent control method for heating parameters of a thermal power plant according to claim 1 is characterized in that: The basic data of thermal balance include sensor identification, measurement accuracy, and data acquisition frequency; the adjusted heating parameters include optimal flow parameters, temperature adjustment range, and adjustment verification time; the fuel ratio optimization data include real-time combustion rate, optimized calorific value, and ratio adjustment reaction time; the load adjustment feedback data include adjusted load balance, feedback efficiency, and real-time adjustment records; the preventive maintenance plan includes a key component inspection list, scheduled maintenance time, and risk assessment indicators; the heating parameter adjustment performance evaluation results include energy-saving effects, improved heating quality, and improved system response speed.

3. The intelligent control method for heating parameters of a thermal power plant according to claim 1, characterized in that: The steps to capture the temperature sensor data of multiple key parts of the thermal power plant, combine the operation time and external weather conditions data, integrate the data, analyze the current operation status, and obtain the basic data of heat balance are as follows: Capture temperature sensor data from multiple key parts of thermal power plants, record the operating time of key parts, synchronously capture external weather condition data, integrate through data timestamp tags, merge data fields, and generate monitoring data sets; Based on the monitoring data set, noise and outliers are removed, the processed data is matched with the original performance data, analysis parameters are adjusted and compared in detail, key performance fluctuations are identified, and status analysis results are obtained; The state analysis results are used to extract key data on the energy efficiency and operation efficiency of the thermal power plant, and the data are analyzed to determine the balance point of thermal efficiency and obtain basic thermal balance data.

4. The intelligent control method for heating parameters of a thermal power plant according to claim 1, characterized in that: The steps of using the basic heat balance data, analyzing the long-term weather forecast data, adjusting the hot water temperature and flow rate to the optimal initial values, dynamically adjusting the parameters and matching the predicted heating demand, and obtaining the adjusted heating parameters are as follows: Based on the basic heat balance data, long-term weather forecast data is integrated, and the data is synchronized in time series by comparing temperature trends and weather changes in parallel, and the standard values ​​of the initial hot water temperature and flow rate are calibrated to obtain the initial heating settings; According to the initial heating setting, analyzing the difference between the real-time weather conditions and the forecast data, performing integrated processing of the real-time data stream, adjusting the heating parameters and matching the demand fluctuations predicted in the short term, and generating adjusted heating parameter settings; The adjusted heating parameter settings are used to fine-tune the real-time heating parameters, continuously monitor external weather conditions and internal response data, adjust the flow and temperature based on feedback from the heating equipment, optimize the response sensitivity and heating efficiency of the heating equipment, and generate adjusted heating parameters.

5. The intelligent control method for heating parameters of a thermal power plant according to claim 1, characterized in that: The steps of analyzing the matching of fuel consumption and heating demand through the adjusted heating parameters, adjusting the fuel ratio and input amount, optimizing the combustion efficiency, and performing fuel efficiency calculation to obtain the fuel ratio optimization data are as follows: Based on the adjusted heating parameters, fuel consumption data is captured in real time, the associated heating demand is recorded synchronously, and data comparison and analysis are performed to quantitatively measure the degree of matching between consumption and demand by identifying the difference between current fuel usage and heating demand, and obtain fuel demand matching data; According to the fuel demand matching data, the efficiency of the current fuel ratio is analyzed, the ratio of the differentiated fuel types is adjusted, and the optimal fuel ratio is determined by continuously monitoring the combustion effect under multiple ratios to obtain optimized fuel ratio data; The optimized fuel ratio data is used to adjust the fuel input amount, adjust the fuel supply amount according to the continuously updated heating demand data, calculate the fuel consumption efficiency, and obtain the fuel ratio optimization data.

6. The intelligent control method for heating parameters of a thermal power plant according to claim 1, characterized in that: According to the fuel ratio optimization data, the load of the thermal power plant heating equipment is adjusted, real-time load adjustment is performed and matched with real-time heat demand, and load regulation feedback analysis is performed. The steps of obtaining load adjustment feedback data are specifically as follows: Based on the fuel ratio optimization data, real-time monitoring and adjustment of the operating parameters of the thermal power plant's heating equipment are performed to capture changes in heat demand, and load settings are adjusted according to the captured data to obtain preliminary load matching data; Utilizing the preliminary load matching data, analyzing the difference between the real-time and original load data, adjusting the operating parameters of the heating equipment according to the demand fluctuation, optimizing the load adjustment through detailed adjustment, and obtaining detailed load adjustment data; Based on the refined load adjustment data, feedback analysis of load adjustment is performed, and the matching degree between the efficiency of load adjustment and the real-time demand is determined through comparative analysis to form load adjustment feedback data.

7. The intelligent control method for heating parameters of a thermal power plant according to claim 1, characterized in that: The steps of using the load adjustment feedback data to analyze the equipment operation status, predict maintenance points and potential failures, perform failure prediction analysis, and generate a preventive maintenance plan are as follows: Based on the load adjustment feedback data, the current operating parameters and original performance data of multiple heating equipment are monitored, and the data are compared and analyzed item by item. By identifying abnormal fluctuations and performance degradation in the operating parameters, the current status and potential risks of the equipment operation are determined, and the equipment operation status results are generated; Using the equipment operation status results, analyzing key maintenance points and equipment performance degradation trends, combined with original equipment failure records, evaluating failure causes and predicting the time of failure occurrence, and obtaining maintenance point and failure prediction data; Based on the maintenance points and failure prediction data, maintenance activities, required resources and schedule are identified to avoid potential equipment failures and create preventive maintenance plans.

8. The intelligent control method for heating parameters of a thermal power plant according to claim 7, characterized in that: The formula for the linear regression analysis technique is as follows: Among them, Y represents the predicted performance index of the equipment, β0 represents the intercept of the linear regression model, β1 represents the direct impact weight of the equipment service life on performance degradation, β2 represents the impact weight of the number of maintenance times on equipment performance, β3 represents the adjustment coefficient, X1 represents the equipment service life, X2 represents the number of maintenance times, X3 represents the number of abnormal indicators before the failure occurs, and X4 represents the time interval between the last maintenance activity.

9. The intelligent control system for heating parameters of a thermal power plant according to claim 1, characterized in that: The specific steps of updating the maintenance and operation schedule, scheduling resources, performing operation efficiency analysis, monitoring the maintenance execution effect, and generating the heating parameter adjustment performance evaluation results according to the preventive maintenance plan are as follows: Based on the preventive maintenance plan, adjust the maintenance and operation schedule of the thermal power plant, reallocate the resources of the maintenance unit and the operation team, and create a resource scheduling and schedule optimization plan through detailed resource and time management; Using the resource scheduling and schedule optimization solution, multiple maintenance and operation tasks are initiated, and data on task execution, including start time, progress, and deviation, is collected in real time, and task execution efficiency results are formed by continuously tracking activities; Based on the task execution efficiency results, the maintenance execution effect is analyzed, the impact of each maintenance activity on the overall equipment performance and heating efficiency is analyzed, the contribution of maintenance activities is quantified, and the heating parameter adjustment performance evaluation results are formulated.

10. An intelligent control system for heating parameters of a thermal power plant, characterized in that: According to the method for intelligent control of heating parameters of a thermal power plant according to any one of claims 1 to 9, the system comprises: The initial state assessment module captures temperature sensor data and operation time data of multiple key parts of the thermal power plant, and combines the information with external environmental condition data to analyze the current operation state of the heating configuration and obtain baseline operation parameters; The parameter adjustment module uses the baseline operating parameters and combines the long-term weather forecast to adjust the hot water temperature and flow rate to match the expected heat load demand, and controls the temperature and flow rate to obtain adjusted heating parameters; The efficiency optimization module evaluates the fuel consumption through the adjusted heating parameters, adjusts the fuel ratio, optimizes the fuel input and ratio, and obtains the optimized fuel configuration; The real-time adjustment module dynamically adjusts the load of the heating equipment according to the optimized fuel configuration to match the real-time heat demand, implements continuous monitoring of load adjustment, and obtains load adjustment data; The operation and maintenance strategy formulation module uses the load regulation data to regularly monitor the operating status of the equipment, determines the maintenance timing and potential failure points through predictive analysis, and obtains the performance evaluation results of the heating parameter adjustment.

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