A comprehensive monitoring intelligent control system based on waste heat recovery

Abstract

The present invention discloses a comprehensive monitoring intelligent control system based on waste heat recovery, which specifically relates to the field of energy management technology, including monitoring waste heat sources and all recovery pathways, obtaining monitoring data set one and monitoring data set two, dividing the waste heat sources into regions according to the connected areas of the recovery pathways and calculating the waste heat, calculating the recovery value of the recovery pathways, and then judging whether a feedback operation is required, sending signal one to a feedback module when a feedback operation is required, and sending signal two to the feedback module when a feedback operation is not required, then generating a positive feedback signal or a negative feedback signal, and dynamically adjusting the parameters of the target recovery pathway according to the type of the feedback signal; the present invention can combine big data and artificial intelligence technology to perform intelligent management and control of the waste heat recovery system, realize accurate evaluation and optimal control of the waste heat recovery potential, and effectively recycle and utilize waste heat resources in industrial processes.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and more specifically, to a comprehensive monitoring intelligent control system based on waste heat recovery. Background Art

[0002] In the modern industrial production process, a large amount of heat energy is consumed and discharged in various production links, forming waste heat resources. Waste heat resources include high-temperature waste gas, waste water, waste liquid, etc., which have high energy utilization potential. However, traditional waste heat recovery systems often have the following problems: Traditional waste heat recovery systems usually adopt fixed recovery schemes and cannot be dynamically adjusted according to changes in the actual production environment, resulting in low recovery efficiency and inability to fully utilize waste heat resources. Existing systems mostly rely on manual management and control, lack intelligent means, and cannot achieve refined and intelligent management of the waste heat recovery process, which is prone to management omissions and resource waste. In addition, the monitoring means of traditional systems are single, and it is impossible to comprehensively and real-time monitor multiple parameters of waste heat sources and recovery pathways, resulting in an incomplete understanding of the system operation status and difficulty in precise regulation. Therefore, a comprehensive monitoring intelligent control system based on waste heat recovery is proposed to solve the above problems. Summary of the invention

[0003] To achieve the above object, the present invention provides the following technical solutions:

[0004] A comprehensive monitoring intelligent control system based on waste heat recovery, comprising a monitoring module 1, a monitoring module 2, an analysis module 1, an analysis module 2, a comprehensive analysis module, a feedback module, and a control module;

[0005] The monitoring module 1 is used to monitor a plurality of preset first parameters of the waste heat source, obtain a monitoring data set 1 and transmit it to the analysis module 1;

[0006] The monitoring module 2 is used to monitor multiple preset second parameters of the target pathways corresponding to all recycling pathways, obtain monitoring data subsets of multiple recycling pathways, aggregate all monitoring data subsets to obtain monitoring data set 2 and transmit it to the analysis module 2;

[0007] The analysis module 1 is used to divide the waste heat source into regions according to the connected regions of the recovery paths, and then calculate the waste heat of the connected regions corresponding to each recovery path according to the monitoring data set 1 to obtain the waste heat value of the connected regions;

[0008] The second analysis module is used to calculate the recovery value of the target recovery pathway according to the monitoring data subset of the target recovery pathway to obtain the recovery value of the target recovery pathway;

[0009] The comprehensive analysis module is used to determine whether a feedback operation is required based on the residual heat value of the connected area and the recovery value of the target recovery path, and sends a signal one to the feedback module when a feedback operation is required, and sends a signal two to the feedback module when a feedback operation is not required;

[0010] The feedback module is used to judge and generate a positive feedback signal or a negative feedback signal to the control module;

[0011] The control module dynamically adjusts the parameters of the target recycling pathway according to the type of feedback signal.

[0012] In a preferred embodiment, the analysis module 1 calculates the residual heat of the connected area corresponding to each recovery path according to the monitoring data set 1, and the residual heat value of the connected area is:

[0013] The temperature score, flow score and pressure score are calculated by summing up the temperature change rate, flow change rate and pressure change rate of the connected area corresponding to the normalized recovery path and the corresponding real-time temperature, real-time flow and real-time pressure respectively. The temperature score, flow score and pressure score are weighted according to the preset strategy to determine the weight values ​​of the temperature, flow and pressure of the recovery path. Then the real-time temperature, real-time flow and real-time pressure are weightedly calculated to obtain the waste heat value of the connected area.

[0014] In a preferred embodiment, the second analysis module is used to calculate the recovery value of the target recovery pathway according to the monitoring data subset of the target recovery pathway, and the recovery value of the target recovery pathway is:

[0015] A standard form is preset in the analysis module 2, and the standard form is set with the corresponding preset weight values ​​of all monitoring indicators in the monitoring data set 2. According to the monitoring data subset of the target recycling pathway, the types of all the monitoring data inside it are obtained, and the preset weight values ​​corresponding to the monitoring data types are queried from the standard form. Then, all the monitoring data in the monitoring data subset are weighted, and the recycling value of the target recycling pathway is obtained.

[0016] In a preferred embodiment, the comprehensive analysis module is used to determine whether a feedback operation is required based on the residual heat value of the connected area and the recovery value of the target recovery path, which means:

[0017] The environmental data corresponding to each preset environmental indicator of the target connected area are obtained respectively to obtain an environmental data group, a machine learning model is selected, and the waste heat value of the connected area, the recovery value of the target recovery path, and multiple environmental data in the environmental data group are used as input data of the machine learning model. The recovery potential of the target connected area is judged by the output of the machine learning model. When the target connected area does not have the recovery potential, the early warning mechanism is triggered and a signal one is sent to the feedback module. When the target connected area has the recovery potential, the early warning mechanism is not triggered and a signal two is sent to the feedback module.

[0018] In a preferred embodiment, the feedback module is used to determine and generate a positive feedback signal or a negative feedback signal to the control module, which means:

[0019] For the waste heat source, the evaluation model constructed by the Logistic regression analysis method is used to evaluate the adjustable range of its various control parameters as a whole, and the overall evaluation value is obtained as one;

[0020] For each recycling pathway, the evaluation model constructed by the Logistic regression analysis method was used to conduct an overall evaluation of the adjustable range of various control parameters, and the overall evaluation value 2 was obtained;

[0021] If the overall evaluation value one is greater than or equal to the overall evaluation value two, a positive feedback signal is generated for the target recycling pathway; if the overall evaluation value one is less than the overall evaluation value two, a negative feedback signal is generated for the target recycling pathway.

[0022] In a preferred embodiment, the control module dynamically adjusts the parameters of the target recycling pathway according to the type of feedback signal, which means:

[0023] The control module dynamically adjusts the parameters of the waste heat source connected area corresponding to the target recovery path according to the positive feedback signal;

[0024] The control module dynamically adjusts the parameters of the equipment corresponding to the target recycling pathway according to the negative feedback signal.

[0025] In a preferred embodiment, when the target connected area does not have the recovery potential to trigger the early warning mechanism, the target connected area is closed.

[0026] In a preferred embodiment, when the control module performs dynamic parameter adjustment, the following requirement is met: the flow of the closed connected area is evenly distributed among the connected areas corresponding to all positive feedback signals.

[0027] Technical effects and advantages of the present invention:

[0028] The present invention can maximize the recovery rate of waste heat resources by real-time monitoring of multiple parameters of waste heat sources and various recovery pathways, and then analyze and apply the results of the analysis to intelligent regulation, thereby significantly improving energy utilization efficiency. Combined with big data and artificial intelligence technology, the present invention can intelligently manage and control the waste heat recovery system. Through machine learning models and comprehensive analysis modules, it can achieve accurate evaluation and optimal control of waste heat recovery potential, reduce manual intervention, and improve the automation level of system operation.

[0029] The present invention can effectively recycle and utilize waste heat resources in industrial processes, reduce energy waste, lower energy consumption and carbon emissions, meet the requirements of green development, and help achieve energy conservation and emission reduction goals. The present invention can monitor the system operation status in real time, promptly discover and warn of abnormal situations, and close the target connected area when the target connected area does not have the recovery potential to trigger the warning mechanism.

[0030] The present invention uses a comprehensive analysis module to evaluate the waste heat recovery potential of each connected area in real time, which is conducive to dynamically adjusting the recovery strategy. Utilizing a machine learning model, the potential of the recovery area can be accurately judged to achieve intelligent feedback and regulation. By using a machine learning model for evaluation and prediction, it is possible to quickly respond to changes in the system, optimize the recovery plan in real time, improve the system response efficiency and operating results, and ensure that the system always operates in the best state. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0032] Figure 1 This is a schematic diagram of a comprehensive monitoring intelligent control system based on waste heat recovery. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Example 1

[0035] A comprehensive monitoring intelligent control system based on waste heat recovery includes monitoring module one, monitoring module two, analysis module one, analysis module two, comprehensive analysis module, feedback module and control module; the monitoring module one, monitoring module two, analysis module one, analysis module two, comprehensive analysis module, feedback module and control module are communicatively connected with each other.

[0036] The monitoring module 1 is used to monitor multiple preset first parameters of the waste heat source, obtain a monitoring data set 1 and transmit it to the analysis module 1; the first parameters include but are not limited to the following:

[0037] Temperature: The real-time temperature of the waste heat source is an important indicator for measuring waste heat energy;

[0038] Flow rate: The fluid flow rate of the waste heat source, usually expressed as the volume flowing through per hour, such as liters / hour, reflects the heat transfer capacity of the waste heat source;

[0039] Pressure: The pressure of the waste heat source, usually measured in Pascals or MPa, affects the efficiency and safety of waste heat transfer.

[0040] The monitoring module 2 is used to monitor multiple preset second parameters of the target pathways corresponding to all recycling pathways, obtain monitoring data subsets of multiple recycling pathways, aggregate all monitoring data subsets to obtain monitoring data set 2 and transmit it to the analysis module 2; the second parameters include but are not limited to the following:

[0041] Recovery temperature: the real-time temperature of each recovery path, reflecting the efficiency of waste heat recovery;

[0042] Recovery flow rate: The fluid flow rate of each recovery path, usually expressed as the volume flowing through per hour (such as liters / hour), reflects the heat transfer capacity of the recovery process;

[0043] Recovery pressure: The pressure of each recovery path, usually measured in Pascals (Pa) or Megapascals (MPa), affects the efficiency and safety of the recovery process;

[0044] Heat recovery rate: The heat recovery rate of each recovery pathway, usually expressed as a percentage, reflects the efficiency of the recovery process;

[0045] Energy consumption: The energy consumption of each recycling pathway, usually expressed in kilowatt-hours (kWh), is used to assess the energy consumption of the recycling process.

[0046] The analysis module one is used to divide the waste heat source into regions according to the connected areas of the recovery pathways, and then calculate the waste heat of the connected areas corresponding to each recovery pathway according to the monitoring data set to obtain the waste heat value of the connected areas; the function of the analysis module one is to divide the waste heat source into regions according to the connected areas of the recovery pathways, and then calculate the waste heat of the connected areas corresponding to each recovery pathway according to the monitoring data set to obtain the waste heat value of the connected areas; the specific steps are as follows: regional division: according to the connection between the waste heat source and each recovery pathway, the waste heat source is divided into several connected areas. For example, if the waste heat source, such as a boiler, is connected to different recovery equipment, such as a water heater, a heater, and a preheater, through a pipeline system, each pipeline system represents a connected area. The monitoring data set 1 monitors the data of the connected area, and the monitoring module 2 monitors the data of the recovery equipment. The monitoring data set 1 is as follows: real-time temperature: 200°C; real-time flow: 5000L / h; real-time pressure: 2MPa. Calculate the temperature, flow, and pressure change rates: The normalized temperature change rate, flow change rate, and pressure change rate are used to calculate the temperature score, flow score, and pressure score. Assuming there is historical data or a benchmark value, calculate the change rate, for example:

[0047] Temperature change rate = (real-time temperature - reference temperature) / reference temperature;

[0048] Traffic change rate = (real-time traffic - benchmark traffic) / benchmark traffic;

[0049] Pressure change rate = (real-time pressure - reference pressure) / reference pressure;

[0050] Calculate the score: Sum the normalized rate of change and the real-time value to get the score as follows:

[0051] Assume that the reference temperature, flow rate, and pressure are 190°C, 4800L / h, and 1.9MPa respectively;

[0052] Temperature change rate = (200°C-190°C) / 190°C≈0.0526;

[0053] Flow rate change rate = (5000L / h-4800L / h) / 4800L / h≈0.0417;

[0054] Pressure change rate = (2MPa-1.9MPa) / 1.9MPa≈0.0526;

[0055] Temperature score = real-time temperature + temperature change rate ≈ 200 + 0.0526 = 200.0526;

[0056] Traffic score = real-time traffic + traffic change rate ≈ 5000 + 0.0417 = 5000.0417;

[0057] Pressure score = real-time pressure + pressure change rate ≈ 2 + 0.0526 = 2.0526;

[0058] Obviously, the greater the rate of change of the variable per unit time and / or the larger the real-time variable, the greater the score of the variable, the more it needs to be focused on, that is, the more important the variable is. The variable can be temperature, flow, or pressure, and the greater the consideration weight it occupies. The unit time can be set according to actual needs, which will not be elaborated here. The rate of change of the variable per unit time and the real-time variable can also be weighted and calculated according to actual conditions, which will not be elaborated here. It should be noted that when assigning weights to a specific variable, the other variables are based on the standard conditions, that is, when assigning weights to the temperature, the flow and pressure in the connected area are set at standard values, and finally their respective assignment weights are obtained. The weight assignment can be performed using the hierarchical analysis method.

[0059] The real-time temperature, real-time flow, and real-time pressure are weighted and calculated to obtain the residual heat value of the connected area, such as: ;w T is the temperature weight, w F is the traffic weight, w P is the pressure weight, T, F, P, and Q are the real-time temperature, real-time flow, real-time pressure, and the residual heat value of the connected area, respectively.

[0060] Analysis module 2 is used to calculate the recovery value of the target recovery pathway according to the monitoring data subset of the target recovery pathway, and obtain the recovery value of the target recovery pathway; a standard form is preset in analysis module 2, and the standard form is set with the corresponding preset weight values ​​of all monitoring indicators in monitoring data set 2, and the preset standard form contains the preset weight values ​​of all monitoring indicators in monitoring data set 2. These weight values ​​reflect the importance of each monitoring indicator in evaluating the recovery effect, and the types of all monitoring data in the monitoring data subset of the target recovery pathway are obtained, and the preset weight values ​​corresponding to the types of monitoring data are queried from the standard form, and then all monitoring data in the monitoring data subset are weighted to obtain the recovery value of the target recovery pathway, as shown below: Table 1 Standard form

[0061] :

[0062] The monitoring data subset of the target recovery pathway is set to contain n monitoring indicators, the weight value of each indicator is wi, and the monitoring data value is xi. The calculation formula for the recovery value R is: The recovery value R is used to evaluate the overall performance of the hot water recovery system. The higher the value, the better the recovery effect of the target recovery path.

[0063] The comprehensive analysis module is used to determine whether a feedback operation is needed based on the waste heat value of the connected area and the recovery value of the target recovery path. When a feedback operation is needed, a signal one is sent to the feedback module, and when a feedback operation is not needed, a signal two is sent to the feedback module. Assuming that the environmental data group contains ambient temperature, ambient humidity, ambient pressure and the thickness of the outer wrapping layer of the pipeline as features, these features are used to train a decision tree model.

[0064] Decision tree operation steps:

[0065] Prepare a historical data set, including the waste heat value of the connected area, the recovery value of the target recovery pathway, and environmental data, and divide the data set into a feature set (including the waste heat value of the connected area, the recovery value of the target recovery pathway, and environmental data) and a label set (the judgment result of the recovery potential).

[0066] Train a decision tree model: Use the feature set and label set to train a decision tree model. The decision tree model will build decision rules based on different values ​​of the features to make judgments on recycling potential.

[0067] Decision tree operation: The decision tree will make judgments based on the value of each feature. Each node on the decision path corresponds to a feature and a judgment condition. When new data is input, the decision tree will make judgments along the decision path based on the value of the feature and eventually reach the leaf node, which corresponds to the judgment result of the recycling potential.

[0068] Output judgment result: When the waste heat value of the connected area, the recovery value of the target recovery path, and environmental data are input, the decision tree model will output the judgment result of the recovery potential based on the learned decision rules.

[0069] For example: Assume the following data form: Table 2 Data form

[0070] Suppose a connected area is judged, and its data is: the waste heat value is 1700, the recovery value is 260, the ambient temperature is 28°C, the humidity is 78%, the pressure is 1.1MPa, and the thickness of the pipe outer wrapping layer is 5mm.

[0071] According to the operation of the decision tree: the residual heat value is greater than 1600, enter the right subtree, the recovery value is greater than 250, enter the right subtree, and the environmental humidity is 78%, which does not have recovery potential. Therefore, according to the judgment of the decision tree, this connected area does not have recovery potential and the early warning mechanism should be triggered.

[0072] The feedback module is used to determine and generate a positive feedback signal or a negative feedback signal to the control module, and the control module dynamically adjusts the parameters of the target recycling pathway according to the type of the feedback signal.

[0073] The analysis module 1 calculates the residual heat of the connected area corresponding to each recycling path according to the monitoring data set 1, and obtains the residual heat value of the connected area, which is:

[0074] The temperature score, flow score and pressure score are calculated by summing up the temperature change rate, flow change rate and pressure change rate of the connected area corresponding to the normalized recovery path and the corresponding real-time temperature, real-time flow and real-time pressure respectively. The temperature score, flow score and pressure score are weighted according to the preset strategy to determine the weight values ​​of the temperature, flow and pressure of the recovery path. Then the real-time temperature, real-time flow and real-time pressure are weightedly calculated to obtain the waste heat value of the connected area.

[0075] The second analysis module is used to calculate the recovery value of the target recovery pathway according to the monitoring data subset of the target recovery pathway, and the recovery value of the target recovery pathway is:

[0076] A standard form is preset in the analysis module 2, and the standard form is set with the corresponding preset weight values ​​of all monitoring indicators in the monitoring data set 2. According to the monitoring data subset of the target recycling pathway, the types of all the monitoring data inside it are obtained, and the preset weight values ​​corresponding to the monitoring data types are queried from the standard form. Then, all the monitoring data in the monitoring data subset are weighted, and the recycling value of the target recycling pathway is obtained.

[0077] The comprehensive analysis module is used to determine whether feedback operation is required based on the residual heat value of the connected area and the recovery value of the target recovery path.

[0078] The environmental data corresponding to each preset environmental indicator of the target connected area are obtained respectively to obtain an environmental data group, a machine learning model is selected, and the waste heat value of the connected area, the recovery value of the target recovery path, and multiple environmental data in the environmental data group are used as input data of the machine learning model. The recovery potential of the target connected area is judged by the output of the machine learning model. When the target connected area does not have the recovery potential, the early warning mechanism is triggered and a signal one is sent to the feedback module. When the target connected area has the recovery potential, the early warning mechanism is not triggered and a signal two is sent to the feedback module.

[0079] The feedback module is used to determine and generate positive feedback signals or negative feedback signals to the control module:

[0080] For the waste heat source, the evaluation model constructed by the Logistic regression analysis method is used to evaluate the adjustable range of its various control parameters as a whole, and the overall evaluation value is obtained as one;

[0081] For each recycling pathway, the evaluation model constructed by the Logistic regression analysis method was used to conduct an overall evaluation of the adjustable range of various control parameters, and the overall evaluation value 2 was obtained;

[0082] The overall evaluation value 1 and the overall evaluation value 2 both adopt the basic form of the Logistic regression model, such as: , where P(y=1|X) is the probability that the dependent variable is 1, X is the independent variable vector, i.e., the various indicators of the control parameters, β0, β1, ..., βn are regression coefficients, indicating the degree of influence of each control parameter on the system performance, the dependent variable y can take two values, usually marked as 0 and 1, and Logistic regression estimates the probability that y takes the value of 1 by mapping the linear combination of the independent variables to the interval [0,1]. When judging the value of y, a threshold is generally used, usually 0.5. If it is greater than or equal to this threshold, it is judged as category 1, and if it is less than the threshold, it is judged as category 0. The larger the overall evaluation value 1, the larger the comprehensive control range of the control parameters at the heat source input end of the recovery path corresponding to the waste heat source, and the larger the overall evaluation value 2, the larger the comprehensive control range of the control parameters at the output end of the recovery path. For example, the output end corresponding to the recovery path is the preheating process production line for production and processing.

[0083] If the overall evaluation value one is greater than or equal to the overall evaluation value two, a positive feedback signal is generated for the target recovery path, indicating that the comprehensive control range of the control parameters at the heat source input end of the recovery path corresponding to the waste heat source is larger, and more space can be left for the next regulation after this regulation, which can effectively avoid the problem of waste heat energy being wasted when the parameters have reached the limit requirements when the regulation operation is required. If the overall evaluation value one is less than the overall evaluation value two, a negative feedback signal is generated for the target recovery path, indicating that the comprehensive control range of the control parameters at the output end corresponding to the recovery path is larger, and the recovery path meets the preset heat recovery rate. In this case, the control parameters at the output end corresponding to the recovery path can be regulated, such as the regulation module can increase the number of preheated workpieces per unit time of the preheating process production line within a reasonable range to seek the optimal heat recovery rate.

[0084] The control module dynamically adjusts the parameters of the target recycling pathway according to the type of feedback signal:

[0085] The control module dynamically adjusts the parameters of the waste heat source connection area corresponding to the target recovery path according to the positive feedback signal; the control module dynamically adjusts the parameters of the equipment corresponding to the target recovery path according to the negative feedback signal. A strategy for dynamically adjusting the parameters of the equipment corresponding to the target recovery path according to the negative feedback signal is preset. For example, each time the value of the adjustable parameter with the maximum adjustment range related to the recovery heat utilization is increased, the increase ratio is determined by the preset strategy, which is not repeated here. When the target connection area does not have the recovery potential to trigger the early warning mechanism, it means that the recovery path of the target connection area does not meet the preset heat recovery rate, and the target connection area can be closed. Closing the target connection area means that after improving the various control parameters of the output end corresponding to the recovery path, the preset heat recovery rate is still not achieved, indicating that the target connection area is seriously losing heat, and the target connection area may need to be inspected for pipelines. When the control module performs dynamic parameter adjustment, it meets the following requirements: the flow of the closed connection area is evenly distributed in all the connection areas corresponding to the positive feedback signals, which can ensure that the waste heat energy is fully utilized and the overall recovery quality of the waste heat recovery is guaranteed.

[0086] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0087] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0088] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0089] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0090] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A comprehensive monitoring intelligent control system based on waste heat recovery, It is characterized in that It includes monitoring module 1, monitoring module 2, analysis module 1, analysis module 2, comprehensive analysis module, feedback module and control module; The monitoring module 1 is used to monitor a plurality of preset first parameters of the waste heat source, obtain a monitoring data set 1 and transmit it to the analysis module 1; The monitoring module 2 is used to monitor multiple preset second parameters of the target pathways corresponding to all recycling pathways, obtain monitoring data subsets of multiple recycling pathways, aggregate all monitoring data subsets to obtain monitoring data set 2 and transmit it to the analysis module 2; The analysis module 1 is used to divide the waste heat source into regions according to the connected regions of the recovery paths, and then calculate the waste heat of the connected regions corresponding to each recovery path according to the monitoring data set 1 to obtain the waste heat value of the connected regions; The second analysis module is used to calculate the recovery value of the target recovery pathway according to the monitoring data subset of the target recovery pathway to obtain the recovery value of the target recovery pathway; The comprehensive analysis module is used to determine whether a feedback operation is required based on the residual heat value of the connected area and the recovery value of the target recovery path, and sends a signal one to the feedback module when a feedback operation is required, and sends a signal two to the feedback module when a feedback operation is not required; The feedback module is used to judge and generate a positive feedback signal or a negative feedback signal to the control module; The control module dynamically adjusts the parameters of the target recycling pathway according to the type of feedback signal; The analysis module 1 calculates the residual heat of the connected area corresponding to each recycling path according to the monitoring data set 1, and obtains the residual heat value of the connected area, which is: The temperature score, flow score and pressure score are calculated by summing the temperature change rate, flow change rate and pressure change rate of the connected area corresponding to the normalized recovery path and the corresponding real-time temperature, real-time flow and real-time pressure, and weighting is performed according to the temperature score, flow score and pressure score according to the preset strategy to determine the weight values ​​of the temperature, flow and pressure of the recovery path, and then the real-time temperature, real-time flow and real-time pressure are weightedly calculated to obtain the waste heat value of the connected area; The second analysis module is used to calculate the recovery value of the target recovery pathway according to the monitoring data subset of the target recovery pathway, and the recovery value of the target recovery pathway is: The analysis module 2 is preset with a standard form, and the standard form is set with the corresponding preset weight values ​​of all monitoring indicators in the monitoring data set 2. The types of all monitoring data in the monitoring data subset of the target recycling pathway are obtained, and the preset weight values ​​corresponding to the monitoring data types are queried from the standard form. Then, all monitoring data in the monitoring data subset are weighted, and the recycling value of the target recycling pathway is obtained; The comprehensive analysis module is used to determine whether feedback operation is required based on the residual heat value of the connected area and the recovery value of the target recovery path. Respectively obtain environmental data corresponding to various preset environmental indicators of the target connected area to obtain an environmental data group, select a machine learning model, use the residual heat value of the connected area, the recovery value of the target recovery path, and multiple environmental data in the environmental data group as input data of the machine learning model, and judge the recovery potential of the target connected area through the output of the machine learning model. When the target connected area does not have the recovery potential, trigger the early warning mechanism and send signal one to the feedback module. When the target connected area has the recovery potential, do not trigger the early warning mechanism and send signal two to the feedback module. The feedback module is used to determine and generate positive feedback signals or negative feedback signals to the control module: For the waste heat source, the evaluation model constructed by the Logistic regression analysis method is used to evaluate the adjustable range of its various control parameters as a whole, and the overall evaluation value is obtained as one; For each recycling pathway, the evaluation model constructed by the Logistic regression analysis method was used to conduct an overall evaluation of the adjustable range of various control parameters, and the overall evaluation value 2 was obtained; If the overall evaluation value one is greater than or equal to the overall evaluation value two, a positive feedback signal is generated for the target recycling pathway; if the overall evaluation value one is less than the overall evaluation value two, a negative feedback signal is generated for the target recycling pathway.

2. According to claim 1, a comprehensive monitoring intelligent control system based on waste heat recovery, It is characterized in that The control module dynamically adjusts the parameters of the target recycling pathway according to the type of feedback signal: The control module dynamically adjusts the parameters of the waste heat source connected area corresponding to the target recovery path according to the positive feedback signal; The control module dynamically adjusts the parameters of the equipment corresponding to the target recycling pathway according to the negative feedback signal.

3. According to claim 2, a comprehensive monitoring intelligent control system based on waste heat recovery, It is characterized in that When the target connected area does not have the recovery potential to trigger the early warning mechanism, the target connected area will be closed.

4. According to claim 3, a comprehensive monitoring intelligent control system based on waste heat recovery, It is characterized in that When the control module performs dynamic parameter adjustment, the following requirements are met: the flow of the closed connected area is evenly distributed among the connected areas corresponding to all positive feedback signals.

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