Boiler water protective agent online monitoring system and method for energy saving and emission reduction

Abstract

The present invention discloses an online monitoring system and method for boiler water protective agent for energy conservation and emission reduction, which relates to the field of water quality analysis. The system includes: a spatial monitoring point distribution module for evaluating the complexity of equipment structure and distributing spatial monitoring points; a spatial sampling module for activating water quality monitoring sensors and performing water quality sampling; a strategy response module for reading real-time operation data and matching boiler operation scenarios; a prediction module for predicting equipment load changes; and a response module for synchronizing water quality analysis results, equipment load change prediction results, and scenario-based response strategies to a response feedback network, and performing monitoring and management according to the response feedback results. The system solves the technical problems of insufficient comprehensiveness and real-time monitoring in the existing monitoring and management of boiler water protective agents, which leads to poor boiler operation efficiency and energy conservation and emission reduction results, and achieves the technical effect of realizing comprehensive and real-time monitoring of boiler water protective agents and improving boiler operation efficiency and energy conservation and emission reduction results.

Description

Technical Field

[0001] The present application relates to the field of water quality analysis, and in particular to an online monitoring system and method for boiler water protective agents for energy conservation and emission reduction. Background Art

[0002] In industrial production, boilers are important heat energy conversion equipment, and their operating efficiency and environmental performance are directly related to the economic benefits and environmental protection responsibilities of enterprises. The quality of boiler water is one of the key factors affecting the operating efficiency and life of the boiler, and the addition of boiler water protective agents is an important means to improve the quality of boiler water and achieve energy conservation and emission reduction. In existing technologies, the monitoring and management of boiler water and boiler water protective agents mainly rely on manual regular sampling and offline analysis. This method is not only time-consuming and labor-intensive, and it is difficult to cover all key monitoring points, but also difficult to reflect the actual condition of the boiler water in real time, resulting in the addition of boiler water protective agents often lagging behind water quality changes, and the protection strategy cannot be adjusted in a timely and effective manner.

[0003] Among the current related technologies, the monitoring and management of boiler water protective agents lacks comprehensiveness and real-time performance, which leads to technical problems such as poor boiler operating efficiency and poor energy conservation and emission reduction results. Summary of the invention

[0004] The present application provides an online monitoring system and method for boiler water protective agents for energy conservation and emission reduction, evaluates the equipment structure complexity of boiler equipment through a spatial monitoring point distribution module, distributes spatial monitoring points based on the evaluation results, integrates a spatial sampling module, a strategy response module, a prediction module and a response module, automatically reads the real-time operation data of the boiler equipment, matches the boiler operation scenario, predicts equipment load changes, and synchronizes this information with the water quality analysis results to a response feedback network, generates response feedback results, and realizes online monitoring management and other technical means, thereby achieving the technical effect of realizing comprehensive and real-time monitoring of boiler water protective agents and improving boiler operation efficiency and energy conservation and emission reduction results.

[0005] The present application provides an online monitoring system for boiler water protective agent for energy saving and emission reduction, including:

[0006] The spatial monitoring point distribution module is used to evaluate the equipment structure complexity of the boiler equipment and distribute the spatial monitoring points based on the equipment structure complexity evaluation results. The spatial monitoring points include water inlet monitoring points, water outlet monitoring points, and steam recovery pipeline monitoring points. The spatial sampling module is used to add boiler water protective agents to the boiler. When the boiler equipment is started, the water quality monitoring sensors distributed at the spatial monitoring points are synchronously activated to perform water quality sampling and establish a spatially distributed data set. The strategy response module is used to read the real-time operation data of the boiler equipment, match the boiler operation scenario based on the real-time operation data, and establish a scenario-based response strategy. The prediction module is used to predict the equipment load changes of the boiler equipment and establish the equipment load change prediction results. The response module is used to input the spatially distributed data set into the water quality analysis model, establish the water quality analysis results, and synchronize the water quality analysis results, equipment load change prediction results, and scenario-based response strategies to the response feedback network to generate response feedback results, and perform online monitoring and management according to the response feedback results.

[0007] In a possible implementation, the policy response module is used to:

[0008] Obtain the operating time series data of the boiler equipment, and distinguish the operating stage based on the operating time series data; if the distinction result is the initial debugging stage, call the first time series window, perform water quality steady-state identification, and establish the basic response strategy for the debugging stage scenario with the first water quality steady-state identification result; if the distinction result is the stable operation stage, call the second time series window, perform water quality steady-state identification, and establish the basic response strategy for the stable operation stage scenario with the second water quality steady-state identification result.

[0009] In a possible implementation, the spatial monitoring point distribution module is used to:

[0010] Obtain the distribution of structural components of the boiler equipment, perform spatial complexity calculation based on the distribution of structural components, and establish the spatial complexity calculation results; perform water flow path fitting for the boiler equipment, perform flow path complexity and flow dead angle analysis based on the water flow path fitting results, and establish the water flow complexity calculation results; perform heat exchange complexity analysis on the boiler equipment and establish the heat exchange complexity calculation results; perform weighted fitting on the spatial complexity calculation results, water flow complexity calculation results, and heat exchange complexity calculation results, and establish the equipment structure complexity evaluation results.

[0011] In a possible implementation, the response module is used to:

[0012] The water quality parameters of each spatial monitoring point in the spatial distribution data set are converted into a time-space matrix; the point association of the spatial monitoring points is established using the water flow path fitting results and heat conduction correlation, and the water quality analysis model is optimized based on the point association; the time-space matrix is ​​used as input data and input into the optimized water quality analysis model to establish the water quality analysis results.

[0013] In a possible implementation, the response module is used to:

[0014] The water quality analysis results, equipment load change prediction results, and scenario-based response strategies are converted into input matrices through the preprocessing layer of the response feedback network; the weighted feature extraction of the input matrix is ​​performed using the preprocessing layer of the response feedback network, as follows: ;in, Characterized in the The feature value extracted at each moment, Characterization Water quality analysis results data, , is the total number of water quality analysis results data, Characterization The load change prediction result data of each device, , The total number of equipment load change prediction data, Characterize the scenario-based response strategy, , , are the weights of water quality parameters, load parameters, and scenario strategy parameters respectively; the decision layer of the response feedback network is used to make decisions based on the weighted feature extraction results to generate response feedback results, as follows: ;in, Characterization Feedback decision output at all times, is the decision weight matrix, is the bias term, is the activation function, .

[0015] In a possible implementation, the prediction module is used to:

[0016] Call the historical operation data of the boiler equipment, extract data features from the historical operation data, and establish a historical feature set, wherein the historical feature set carries a load change identifier; use the real-time operation data to perform feature similarity matching on the historical feature set to establish a matching result; and generate an equipment load change prediction result using the load change identifier corresponding to the matching result.

[0017] In a possible implementation, the system further includes:

[0018] The self-enhancement module is used to create an enhanced feedback node based on the response feedback result, use the enhanced feedback node to call the water quality monitoring sensor of the spatial monitoring point for enhanced monitoring, establish a monitoring data set, perform a consistency analysis of the fitting results of the response feedback result on the monitoring data set, establish a consistency deviation value, establish a step self-enhancement result with the consistency deviation value, and enhance and optimize the response feedback result at the enhanced feedback node based on the step self-enhancement result.

[0019] The present application also provides an online monitoring method for boiler water protective agent for energy saving and emission reduction, including:

[0020] The boiler equipment is evaluated for its equipment structure complexity, and spatial monitoring points are distributed based on the evaluation results of the equipment structure complexity, wherein the spatial monitoring points include water inlet monitoring points, water outlet monitoring points, and steam recovery pipeline monitoring points; a boiler water protective agent is added to the boiler, and when the boiler equipment is started, the water quality monitoring sensors distributed at the spatial monitoring points are synchronously activated to perform water quality sampling and establish a spatially distributed data set; the real-time operation data of the boiler equipment is read, and the boiler operation scenario is matched based on the real-time operation data to establish a scenario-based response strategy; the equipment load change of the boiler equipment is predicted, and the equipment load change prediction result is established; the spatially distributed data set is input into the water quality analysis model to establish the water quality analysis result, and the water quality analysis result, the equipment load change prediction result, and the scenario-based response strategy are synchronized to the response feedback network to generate the response feedback result, and online monitoring and management are performed according to the response feedback result.

[0021] It is intended to adopt the online monitoring system and method for boiler water protective agent for energy conservation and emission reduction proposed in this application, evaluate the equipment structure complexity of boiler equipment through the spatial monitoring point distribution module, distribute spatial monitoring points based on the equipment structure complexity evaluation results, and add boiler water protective agent to the boiler. When the boiler equipment is started, the water quality monitoring sensors distributed at the spatial monitoring points are synchronously activated through the spatial sampling module, water quality sampling is performed, and a spatial distribution data set is established. The real-time operation data of the boiler equipment is read through the strategy response module, and the boiler operation scenario is matched based on the real-time operation data to establish a scenario-based response strategy. The equipment load change of the boiler equipment is predicted through the prediction module, and the equipment load change prediction result is established. The spatial distribution data set is input into the water quality analysis model through the response module to establish a water quality analysis result, and the water quality analysis result, the equipment load change prediction result, and the scenario-based response strategy are synchronized to the response feedback network to generate a response feedback result. Online monitoring and management are performed according to the response feedback result, thereby achieving the technical effect of realizing comprehensive and real-time monitoring of boiler water protective agent and improving boiler operation efficiency and energy conservation and emission reduction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 A schematic diagram of the structure of an online monitoring system for boiler water protective agent for energy conservation and emission reduction provided in an embodiment of the present application.

[0024] Figure 2 A schematic flow chart of an online monitoring method for boiler water protective agent for energy conservation and emission reduction provided in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: spatial monitoring point distribution module 10, spatial sampling module 20, strategy response module 30, prediction module 40, response module 50. DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0029] The present application embodiment provides an online monitoring system for boiler water protective agent for energy saving and emission reduction, such as Figure 1 As shown, the system comprises:

[0030] The spatial monitoring point distribution module 10 is used to evaluate the equipment structure complexity of the boiler equipment, and to distribute the spatial monitoring points based on the equipment structure complexity evaluation results, wherein the spatial monitoring points include water inlet monitoring points, water outlet monitoring points, and steam recovery pipeline monitoring points. Specifically, the detailed structural information of the boiler equipment is collected, including the distribution of each structural component. Based on the collected structural component distribution information, the spatial complexity is calculated, that is, the complexity of the internal space of the boiler equipment, including the geometric shape and size of the internal space of the equipment and the relative position relationship between the components. Based on the equipment structure complexity evaluation results, spatial monitoring points are distributed at different locations of the boiler equipment, including key locations such as the water inlet, water outlet, and steam recovery pipeline.

[0031] In one possible implementation, the spatial monitoring point distribution module 10 is used to: obtain the distribution of structural components of the boiler equipment, perform spatial complexity calculation based on the distribution of structural components, and establish a spatial complexity calculation result; perform water flow path fitting on the boiler equipment, perform flow path complexity and flow dead angle analysis based on the water flow path fitting result, and establish a water flow complexity calculation result; perform heat exchange complexity analysis on the boiler equipment and establish a heat exchange complexity calculation result; perform weighted fitting on the spatial complexity calculation results, the water flow complexity calculation results, and the heat exchange complexity calculation results, and establish an equipment structure complexity evaluation result.

[0032] Specifically, collect detailed design drawings of boiler equipment, lists of structural components, and connection relationships between components. Based on the collected structural component distribution information, divide the internal space of the boiler equipment, and calculate the volume, shape complexity and other parameters of each space. The higher the spatial complexity, the more dense the distribution of monitoring points needs to be to capture more water quality change information. Use fluid dynamics software or methods to simulate and fit the water flow path in the boiler equipment to obtain a detailed map of the water flow path. Based on the map, analyze the complexity of the flow path (such as the degree of tortuosity of the path, the number of branches, etc.) and potential flow dead ends (areas where water flow is difficult to reach or stay, which are prone to water quality deterioration), and determine the key positions in the water flow path. These positions are more likely to have water quality problems, so they need to be monitored. Analyze the heat exchanger structure, materials, heat exchange efficiency and other parameters of the boiler equipment to evaluate the complexity of the heat exchange process. In areas with high heat exchange complexity, water quality changes are more drastic, so the number of monitoring points needs to be increased to improve the accuracy and sensitivity of monitoring. The calculation results of spatial complexity, water flow complexity and heat exchange complexity are weighted and fitted, and the weights are adjusted according to the actual situation to reflect the importance of different complexities to water quality monitoring, and the comprehensive evaluation results of equipment structure complexity are obtained. This implementation method makes the distribution of monitoring points more scientific and reasonable by comprehensively evaluating the structural complexity of boiler equipment, and improves the accuracy and efficiency of the online monitoring system of boiler water protective agent.

[0033] The spatial sampling module 20 is used to add a boiler water protective agent to the boiler. When the boiler equipment is started, the water quality monitoring sensors distributed at the spatial monitoring points are synchronously activated to perform water quality sampling and establish a spatially distributed data set. Specifically, an appropriate amount of boiler water protective agent is added to the boiler. The boiler water protective agent is a chemical additive used to protect the boiler equipment from corrosion and sediment. When the boiler equipment is started, the water quality monitoring sensors distributed at various spatial monitoring points are synchronously activated. The water quality monitoring sensors are devices used to monitor changes in water quality in the boiler and can measure various chemical components and physical parameters in the water in real time. The water quality in the boiler is sampled using sensors to collect water quality data. The collected water quality data is sorted according to the location of the spatial monitoring points to form a spatially distributed data set.

[0034] The strategy response module 30 is used to read the real-time operation data of the boiler equipment, match the boiler operation scenario based on the real-time operation data, and establish a scenario-based response strategy. Specifically, the real-time operation data of the boiler equipment is collected, including the operation status of the equipment, load changes, etc. Based on the collected real-time operation data, the current operation scenario of the boiler equipment is judged (such as the initial commissioning stage or the stable operation stage). According to the judgment result, the stable state of the water quality in the boiler is identified and judged. Based on the identification result of the stable state of water quality, a scenario-based response strategy suitable for the current operation stage is established, that is, the basic response measures and strategies formulated for specific operation scenarios.

[0035] In one possible implementation, the strategy response module 30 is used to: obtain the operating time sequence data of the boiler equipment, and perform operating stage identification based on the operating time sequence data; if the identification result is the initial debugging stage, call the first timing window, perform water quality steady-state identification, and establish a debugging stage scenario-based response strategy based on the first water quality steady-state identification result; if the identification result is the stable operation stage, call the second timing window, perform water quality steady-state identification, and establish a stable operation stage scenario-based response strategy based on the second water quality steady-state identification result.

[0036] Specifically, it is connected to the control system or data recording system of the boiler equipment to obtain the operation time series data of the boiler equipment (data arranged in time series during the operation of the boiler equipment) in real time, including but not limited to the start-up time of the boiler, load changes, water quality parameters (such as pH value, conductivity, dissolved oxygen, etc.), steam production, etc. The obtained operation time series data are analyzed using preset rules to determine which operation stage the boiler equipment is currently in. Among them, the operation stage of the boiler equipment is divided into the initial debugging stage and the stable operation stage. If the judgment result is the initial debugging stage, the first time series window is called, and the first time series window is shorter, because the water quality changes faster in the initial debugging stage, and more frequent sampling and analysis are required. In this window, water quality steady-state identification is performed to determine whether the water quality has reached a stable state. If the judgment result is the stable operation stage, the second time series window is called, and the second time series window is relatively long, because the water quality changes more steadily in the stable operation stage. Similarly, water quality steady-state identification is performed in this window. Among them, water quality steady-state identification refers to judging whether the water quality is in a stable state based on the change trend and statistical characteristics of water quality parameters. Based on the results of water quality steady-state identification, combined with the real-time operation data of boiler equipment and the load change prediction results, the scenario-based response strategy for the commissioning stage and the stable operation stage is formulated, including adjusting the dosage of boiler water protective agent, adjusting the operating parameters of the boiler, and conducting regular water quality testing, etc., to ensure that the boiler equipment can maintain stable water quality in different operation stages, thereby achieving the goal of energy conservation and emission reduction. This implementation method accurately understands the operating status and water quality of the boiler equipment through operation stage discrimination and water quality steady-state identification, improves the accuracy of the establishment of the scenario-based response strategy, and thus improves the adaptability and flexibility of the online monitoring system of boiler water protective agent.

[0037] The prediction module 40 is used to predict the load change of the boiler equipment and establish the prediction result of the load change of the boiler equipment. Specifically, the future load change of the boiler equipment is predicted and judged based on the historical operation data and real-time operation data of the boiler equipment to obtain the prediction result of the load change of the equipment.

[0038] In one possible implementation, the prediction module 40 is used to: call the historical operation data of the boiler equipment, extract data features of the historical operation data, and establish a historical feature set, wherein the historical feature set has a load change identifier; use the real-time operation data to perform feature similarity matching on the historical feature set to establish a matching result; and generate an equipment load change prediction result using the load change identifier corresponding to the matching result.

[0039] Specifically, the historical operation data of the boiler equipment is called from the database, including but not limited to parameters such as load size, temperature, pressure, flow rate, etc. Key features that can reflect the change of boiler load are extracted from the historical operation data, such as periodic changes in load, peak and valley values ​​of load, etc. The extracted data features are sorted to form a set with load change identifiers, which include the type of load change (such as increase, decrease, stability), the amplitude of change, and the time period of change. The historical feature set is matched with feature similarity using real-time operation data. Real-time operation data refers to the data currently running the boiler, which also includes parameters such as load, temperature, and pressure. The features of the real-time operation data are compared with the features in the historical feature set to find the most similar historical feature set, and the load change identifier in the feature set is extracted. This load change identifier is used as the equipment load change prediction result, indicating the possible change trend of the boiler load in the future. This implementation method realizes accurate prediction of boiler equipment load changes by mining and utilizing feature information in historical data and combining it with real-time operation data, providing strong support for optimizing the use of boiler water protective agents and energy conservation and emission reduction.

[0040] The response module 50 is used to input the spatial distribution data set into the water quality analysis model, establish the water quality analysis results, and synchronize the water quality analysis results, equipment load change prediction results, and scenario-based response strategies to the response feedback network to generate response feedback results, and perform online monitoring and management based on the response feedback results. Specifically, the water quality parameters of each spatial monitoring point in the spatial distribution data set are input into the water quality analysis model (a mathematical model used to analyze and predict the water quality in the boiler) to obtain the water quality analysis results. The water quality analysis results, equipment load change prediction results, and scenario-based response strategies are synchronized to the response feedback network, which is used to process and analyze the water quality analysis results, equipment load change prediction results, and scenario-based response strategies, generate response feedback results and guide online monitoring and management, adjust the operating parameters of the boiler equipment, or take other necessary countermeasures. The embodiment of the present application evaluates the equipment structure complexity of the boiler equipment through the spatial monitoring point distribution module, and distributes the spatial monitoring points based on the evaluation results, integrates the spatial sampling module, the strategy response module, the prediction module and the response module, automatically reads the real-time operation data of the boiler equipment, matches the boiler operation scenario, predicts the equipment load change, and synchronizes this information with the water quality analysis results to the response feedback network, generates response feedback results, and realizes online monitoring management and other technical means, thereby achieving the technical effect of realizing comprehensive and real-time monitoring of boiler water protective agents and improving boiler operation efficiency and energy conservation and emission reduction results.

[0041] In one possible implementation, the response module 50 is used to: perform time-space matrix conversion on the water quality parameters of each spatial monitoring point in the spatial distribution data set; establish point associations of the spatial monitoring points using the water flow path fitting results and heat conduction associations, and optimize the water quality analysis model based on the point associations; use the time-space matrix as input data, input it into the optimized water quality analysis model, and establish water quality analysis results.

[0042] Specifically, the time-space matrix refers to a matrix formed by arranging water quality parameters (such as pH value, dissolved oxygen, hardness, etc.) according to time and monitoring point location. Each row represents the data of a time point, and each column represents the data of a monitoring point. The water quality parameters of each spatial monitoring point in the spatial distribution data set (such as the data of monitoring points such as the water inlet, water outlet, steam recovery pipeline, etc.) are sorted according to the time sequence and the location of the monitoring point to form a time-space matrix. By analyzing the path of the water flow in the boiler equipment, the path information of the water flow from the water inlet to the water outlet and the steam recovery pipeline is obtained, and the relative position relationship between each monitoring point is determined by using the water flow path fitting result. Due to the flow of water and steam inside the boiler equipment, heat conduction will occur. This heat conduction will affect the change of water quality parameters. Combined with the heat conduction correlation, the mutual influence between water quality parameters at different monitoring points is analyzed. The mutual influence relationship between different monitoring points due to the water flow path and heat conduction constitutes a point-to-point association. Based on the point-to-point association, the water quality analysis model is optimized so that it can more accurately reflect the actual situation of the water quality inside the boiler equipment. The data after time-space matrix conversion is used as input data and input into the optimized water quality analysis model. The model calculates the water quality analysis results based on the input data and information such as point association. This implementation method fully considers the changes in water quality parameters in space and time, as well as the mutual influence between different monitoring points, through the establishment and optimization of time-space matrix conversion and point association, and improves the accuracy of water quality analysis.

[0043] In a possible implementation, the response module 50 is used to: convert the water quality analysis results, the equipment load change prediction results, and the scenario-based response strategy into an input matrix through a preprocessing layer of a response feedback network; and perform weighted feature extraction of the input matrix using the preprocessing layer of the response feedback network, as follows: ;in, Characterized in the The feature value extracted at each moment, Characterization Water quality analysis results data, , is the total number of water quality analysis results data, Characterization The load change prediction result data of each device, , The total number of equipment load change prediction data, Characterize the scenario-based response strategy, , , are the weights of water quality parameters, load parameters, and scenario strategy parameters respectively; the decision layer of the response feedback network is used to make decisions based on the weighted feature extraction results to generate response feedback results, as follows: ;in, Characterization Feedback decision output at all times, is the decision weight matrix, is the bias term, is the activation function, .

[0044] Specifically, the input matrix is ​​a data matrix that converts the water quality analysis results, equipment load change prediction results and scenario-based response strategies into a unified format. This matrix contains all the necessary information for subsequent feature extraction and decision processing. On the basis of the input matrix, by assigning different weights to different water quality parameters, load parameters and scenario strategy parameters, more representative eigenvalues ​​are extracted, that is, at a specific moment, weighted data values ​​are extracted from the input matrix, which are used to reflect the comprehensive impact of water quality, load and scenario strategy. The decision layer is part of the response feedback network, responsible for making decisions based on the weighted feature extraction results and generating the final response feedback results. The decision weight matrix is ​​used in the decision layer to convert the eigenvalues ​​into the final feedback decision output. The bias term is a constant term used in the decision processing to adjust the offset of the output result. The activation function is used to convert the output of the decision layer into the final response feedback result, which is used to introduce nonlinear factors and improve the expression ability of the model. This implementation method integrates water quality analysis results, equipment load change prediction results and scenario-based response strategies through three steps: input matrix conversion, weighted feature extraction and decision processing, and generates the final response feedback results, which comprehensively considers the influence of multiple factors and improves the accuracy and efficiency of decision-making.

[0045] In one possible implementation, the system also includes: a self-enhancement module, which is used to create an enhanced feedback node based on the response feedback result, use the enhanced feedback node to call the water quality monitoring sensor of the spatial monitoring point for enhanced monitoring, establish a monitoring data set, perform a fitting result consistency analysis of the response feedback result of the monitoring data set, establish a consistency deviation value, establish a step self-enhancement result with the consistency deviation value, and enhance and optimize the response feedback result at the enhanced feedback node based on the step self-enhancement result.

[0046] Specifically, the enhanced feedback node is a specific node in the system for storing and processing enhanced monitoring data. These nodes are created based on the response feedback results to further verify and optimize the accuracy of the response feedback results. The self-enhancement module first creates the corresponding enhanced feedback node according to the response feedback results. After the enhanced feedback node is created, the self-enhancement module calls the water quality monitoring sensor of the spatial monitoring point for enhanced monitoring, that is, collects more water quality data at a specific time point or under specific conditions to obtain more detailed and accurate water quality information. The self-enhancement module organizes the enhanced monitoring data into a monitoring data set, which is a collection of all water quality data collected during the enhanced monitoring process. The self-enhancement module performs consistency analysis on the monitoring data set, compares the water quality data in the monitoring data set with the expected water quality data in the response feedback results, and calculates the deviation or consistency deviation value between the water quality data in the monitoring data set and the response feedback results. Among them, the consistency deviation value is a numerical indicator that quantifies the degree of inconsistency between the monitoring data set and the response feedback results, reflecting the accuracy and reliability of the response feedback results. The self-enhancement module takes different levels of self-enhancement measures according to the size of the consistency deviation value, including adjusting the parameters of the water quality analysis model, optimizing the weights of the response feedback network, etc. That is, the step self-enhancement result is the result of a series of self-enhancement processing based on the consistency deviation value, so as to gradually reduce the consistency deviation value and improve the accuracy of the response feedback result. Finally, the self-enhancement module applies the step self-enhancement result to the enhanced feedback node to enhance and optimize the response feedback result, including updating the response feedback result to reflect more accurate water quality information and equipment operation status. This implementation method achieves further optimization and verification of the response feedback result by creating an enhanced feedback node, calling water quality monitoring sensors for enhanced monitoring, establishing a monitoring data set, performing consistency analysis, establishing a consistency deviation value, establishing a step self-enhancement result, and enhancing and optimizing the response feedback result at the enhanced feedback node based on these results, more accurately evaluating the water quality status and the operation status of the boiler equipment, and improving the effect of energy conservation and emission reduction.

[0047] In the above, refer to Figure 1 The online monitoring system for boiler water protective agent for energy saving and emission reduction according to an embodiment of the present invention is described in detail. Figure 2 The following describes an online monitoring method for boiler water protective agent for energy saving and emission reduction according to an embodiment of the present invention.

[0048] The online monitoring method of boiler water protective agent for energy conservation and emission reduction according to the embodiment of the present invention is used to solve the technical problems of insufficient comprehensiveness and real-time monitoring of existing boiler water protective agent monitoring and management, resulting in poor boiler operating efficiency and energy conservation and emission reduction effects, so as to achieve the technical effect of realizing comprehensive and real-time monitoring of boiler water protective agent and improving boiler operating efficiency and energy conservation and emission reduction results.

[0049] The online monitoring methods of boiler water protective agents for energy saving and emission reduction include:

[0050] The boiler equipment is evaluated for its equipment structure complexity, and spatial monitoring points are distributed based on the evaluation results of the equipment structure complexity, wherein the spatial monitoring points include water inlet monitoring points, water outlet monitoring points, and steam recovery pipeline monitoring points; a boiler water protective agent is added to the boiler, and when the boiler equipment is started, the water quality monitoring sensors distributed at the spatial monitoring points are synchronously activated to perform water quality sampling and establish a spatially distributed data set; the real-time operation data of the boiler equipment is read, and the boiler operation scenario is matched based on the real-time operation data to establish a scenario-based response strategy; the equipment load change of the boiler equipment is predicted, and the equipment load change prediction result is established; the spatially distributed data set is input into the water quality analysis model to establish the water quality analysis result, and the water quality analysis result, the equipment load change prediction result, and the scenario-based response strategy are synchronized to the response feedback network to generate the response feedback result, and online monitoring and management are performed according to the response feedback result.

[0051] Among them, matching the boiler operation scenario based on real-time operation data and establishing a scenario-based response strategy can further include: obtaining the operation time series data of the boiler equipment, and distinguishing the operation stage based on the operation time series data; if the distinction result is the initial debugging stage, calling the first time series window, performing water quality steady-state identification, and establishing the debugging stage scenario-based response strategy with the first water quality steady-state identification result; if the distinction result is the stable operation stage, calling the second time series window, performing water quality steady-state identification, and establishing the stable operation stage scenario-based response strategy with the second water quality steady-state identification result.

[0052] Among them, the equipment structure complexity evaluation of the boiler equipment and the distribution of spatial monitoring points based on the equipment structure complexity evaluation results can further include: obtaining the distribution of structural components of the boiler equipment, performing spatial complexity calculation based on the distribution of structural components, and establishing spatial complexity calculation results; performing water flow path fitting on the boiler equipment, performing flow path complexity and flow dead angle analysis based on the water flow path fitting results, and establishing water flow complexity calculation results; performing heat exchange complexity analysis on the boiler equipment and establishing heat exchange complexity calculation results; performing weighted fitting on the spatial complexity calculation results, water flow complexity calculation results and heat exchange complexity calculation results to establish equipment structure complexity evaluation results.

[0053] Among them, the inputting of the spatially distributed data set into the water quality analysis model to establish the water quality analysis results may further include: converting the water quality parameters of each spatial monitoring point in the spatially distributed data set into a time-space matrix; establishing point associations of the spatial monitoring points using the water flow path fitting results and heat conduction associations, and optimizing the water quality analysis model based on the point associations; and inputting the time-space matrix as input data into the optimized water quality analysis model to establish the water quality analysis results.

[0054] The step of synchronizing the water quality analysis results, the equipment load change prediction results, and the scenario-based response strategy to the response feedback network to generate the response feedback results may further include: converting the water quality analysis results, the equipment load change prediction results, and the scenario-based response strategy into an input matrix through a preprocessing layer of the response feedback network; and performing weighted feature extraction of the input matrix using the preprocessing layer of the response feedback network, as follows: ;in, Characterized in the The feature value extracted at each moment, Characterization Water quality analysis results data, , is the total number of water quality analysis results data, Characterization The load change prediction result data of each device, , The total number of equipment load change prediction data, Characterize the scenario-based response strategy, , , are the weights of water quality parameters, load parameters, and scenario strategy parameters respectively; the decision layer of the response feedback network is used to make decisions based on the weighted feature extraction results to generate response feedback results, as follows: ;in, Characterization Feedback decision output at all times, is the decision weight matrix, is the bias term, is the activation function, .

[0055] Among them, the predicting of equipment load changes for boiler equipment and establishing equipment load change prediction results can further include: calling historical operation data of the boiler equipment, extracting data features from the historical operation data, and establishing a historical feature set, wherein the historical feature set carries a load change identifier; performing feature similarity matching on the historical feature set using the real-time operation data to establish a matching result; and generating an equipment load change prediction result using the load change identifier corresponding to the matching result.

[0056] Among them, the method may further include: creating an enhanced feedback node based on the response feedback result, using the enhanced feedback node to call the water quality monitoring sensor of the spatial monitoring point for enhanced monitoring, establishing a monitoring data set, performing a fitting result consistency analysis of the response feedback result on the monitoring data set, establishing a consistency deviation value, establishing a step self-enhancement result with the consistency deviation value, and enhancing and optimizing the response feedback result at the enhanced feedback node based on the step self-enhancement result.

[0057] The online monitoring system for boiler water protective agent for energy saving and emission reduction provided by the embodiment of the present invention can execute the online monitoring method for boiler water protective agent for energy saving and emission reduction provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0058] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0059] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. The boiler water protective agent online monitoring system for energy saving and emission reduction is characterized by: The system comprises: A spatial monitoring point distribution module is used to evaluate the equipment structure complexity of the boiler equipment and distribute the spatial monitoring points based on the evaluation results of the equipment structure complexity. The spatial monitoring points include water inlet monitoring points, water outlet monitoring points, and steam recovery pipeline monitoring points. The spatial sampling module is used to add boiler water protective agent to the boiler. When the boiler equipment is started, the water quality monitoring sensors distributed at the spatial monitoring points are synchronously activated to perform water quality sampling and establish a spatial distribution data set; The strategy response module is used to read the real-time operation data of the boiler equipment, match the boiler operation scenario based on the real-time operation data, and establish a scenario-based response strategy; The prediction module is used to predict the equipment load change of the boiler equipment and establish the equipment load change prediction result; The response module is used to input the spatial distribution data set into the water quality analysis model, establish the water quality analysis results, and synchronize the water quality analysis results, equipment load change prediction results, and scenario-based response strategies to the response feedback network to generate response feedback results, and perform online monitoring and management based on the response feedback results; The policy response module is used to: Acquiring operation time data of the boiler equipment, and distinguishing the operation stage based on the operation time data; If the judgment result is the initial debugging stage, the first time series window is called to perform water quality steady-state identification, and the basic response strategy for the debugging stage scenario is established based on the first water quality steady-state identification result; If the judgment result is a stable operation stage, the second timing window is called to perform water quality steady-state identification, and the basic response strategy for the stable operation stage scenario is established based on the second water quality steady-state identification result.

2. The online monitoring system for boiler water protective agent for energy saving and emission reduction according to claim 1, characterized in that: The spatial monitoring point distribution module is used for: Obtain the distribution of structural components of boiler equipment, perform spatial complexity calculation based on the distribution of structural components, and establish spatial complexity calculation results; Fit the water flow path of the boiler equipment, analyze the flow path complexity and flow dead angle based on the water flow path fitting results, and establish the water flow complexity calculation results; Conduct heat exchange complexity analysis on boiler equipment and establish heat exchange complexity calculation results; The calculation results of space complexity, water flow complexity and heat exchange complexity are weightedly fitted to establish the evaluation results of equipment structure complexity.

3. The online monitoring system for boiler water protective agent for energy saving and emission reduction according to claim 2, characterized in that: The response module is used to: The water quality parameters of each spatial monitoring point in the spatial distribution data set are converted into a time-space matrix; The point correlation of spatial monitoring points is established using the water flow path fitting results and heat conduction correlation, and the water quality analysis model is optimized based on the point correlation; The time-space matrix is ​​used as input data to the optimized water quality analysis model to establish the water quality analysis results.

4. The online monitoring system for boiler water protective agent for energy saving and emission reduction according to claim 1, characterized in that: The response module is used to: The water quality analysis results, equipment load change prediction results, and scenario-based response strategies are converted into input matrices through the preprocessing layer of the response feedback network; The weighted feature extraction of the input matrix is ​​performed using the preprocessing layer of the response feedback network as follows: ； in, Characterized in the The feature value extracted at each moment, Characterization Water quality analysis results data, , is the total number of water quality analysis result data, Characterization The load change prediction result data of each device, , The total number of equipment load change prediction data, Characterize the scenario-based response strategy, , , are the weights of water quality parameters, load parameters, and scenario strategy parameters, respectively; The decision layer of the response feedback network is used to make decisions based on the weighted feature extraction results to generate response feedback results as follows: ； in, Characterization Feedback decision output at all times, is the decision weight matrix, is the bias term, is the activation function, .

5. The online monitoring system for boiler water protective agent for energy saving and emission reduction according to claim 1, characterized in that: The prediction module is used to: Calling historical operation data of the boiler equipment, extracting data features from the historical operation data, and establishing a historical feature set, wherein the historical feature set carries a load change identifier; Using the real-time operation data to perform feature similarity matching on the historical feature set to establish a matching result; The equipment load change prediction result is generated using the load change identifier corresponding to the matching result.

6. The boiler water protective agent online monitoring system for energy saving and emission reduction according to claim 1, characterized in that: The system further comprises: The self-enhancement module is used to create an enhanced feedback node based on the response feedback result, use the enhanced feedback node to call the water quality monitoring sensor of the spatial monitoring point for enhanced monitoring, establish a monitoring data set, perform a consistency analysis of the fitting results of the response feedback result on the monitoring data set, establish a consistency deviation value, establish a step self-enhancement result with the consistency deviation value, and enhance and optimize the response feedback result at the enhanced feedback node based on the step self-enhancement result.

7. An online monitoring method for boiler water protective agent for energy saving and emission reduction, characterized in that: The method is implemented by the online monitoring system for boiler water protective agent for energy saving and emission reduction according to any one of claims 1 to 6, and the method comprises: Evaluate the complexity of the equipment structure of the boiler equipment, and distribute spatial monitoring points based on the evaluation results of the equipment structure complexity, wherein the spatial monitoring points include a water inlet monitoring point, a water outlet monitoring point, and a steam recovery pipeline monitoring point; Add boiler water protective agent to the boiler. When the boiler equipment is started, the water quality monitoring sensors distributed at the spatial monitoring points are activated synchronously to perform water quality sampling and establish a spatial distribution data set. Read the real-time operation data of boiler equipment, match boiler operation scenarios based on real-time operation data, and establish scenario-based response strategies; Predict equipment load changes for boiler equipment and establish equipment load change prediction results; Input the spatial distribution data set into the water quality analysis model, establish the water quality analysis results, and synchronize the water quality analysis results, equipment load change prediction results, and scenario-based response strategies to the response feedback network to generate response feedback results, and perform online monitoring and management based on the response feedback results; The method of matching boiler operation scenarios based on real-time operation data and establishing scenario-based response strategies includes: obtaining operation time series data of boiler equipment and distinguishing the operation stage based on the operation time series data; if the distinction result is the initial debugging stage, calling the first time series window, performing water quality steady-state identification, and establishing a debugging stage scenario-based response strategy based on the first water quality steady-state identification result; if the distinction result is the stable operation stage, calling the second time series window, performing water quality steady-state identification, and establishing a stable operation stage scenario-based response strategy based on the second water quality steady-state identification result.

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