Multi-heat-source coupling heat pump control system for recycling waste heat of industrial circulating cooling water

Abstract

The invention discloses a multi-heat-source coupling heat pump control system for recycling waste heat of industrial circulating cooling water, which comprises the following steps: monitoring key parameters of various heat sources in real time through an environment sensing module, screening and preprocessing through a data processing unit, screening out a heat source set suitable for the current condition, and sending the heat source set to a control module; the recovery potential and the use efficiency of the heat source are evaluated by utilizing an adaptive learning model, the use priority and the distribution proportion of the heat source are intelligently determined in combination with real-time load change, and the working time and the switching time of the heat source are dynamically adjusted by adopting a heat source time window distribution mechanism. The operation parameters and the heat source coupling mode of the heat pump are optimized through the intelligent complementary adjusting module, the heat recovery path is dynamically adjusted through the autonomous temperature control unit, the scheduling strategy and the operation mode of the heat source are optimized through the self-adaptive feedback mechanism and the intelligent learning module, and the adaptability and the energy-saving effect of the system are improved. According to the system, efficient cooperative utilization of multiple heat sources is achieved, and the heat recovery efficiency and the system stability are improved.

Description

Technical Field

[0001] The invention relates to the technical field of industrial energy saving and waste heat recovery, and in particular to a multi-heat source coupled heat pump control system for recovering waste heat of industrial circulating cooling water. Background Art

[0002] In the field of industrial waste heat recovery, existing technologies mainly focus on the recovery and utilization of a single heat source, usually using a traditional heat pump system or directly using waste heat for heating. This method has many shortcomings in practical applications, especially when faced with complex environments with multiple heat sources and dynamic load changes, its defects are more obvious.

[0003] The single heat source mode of existing technology has low efficiency. In industrial environments, multiple low-temperature heat sources such as cooling water waste heat, exhaust gas heat sources and ambient air usually coexist, but traditional systems often only recycle and utilize a single heat source and cannot comprehensively utilize the characteristics of multiple heat sources. Cooling water waste heat is stable, while the temperature of exhaust gas heat sources and ambient air fluctuates greatly. Traditional systems cannot flexibly schedule and optimize the utilization of different heat sources according to real-time needs, resulting in a large amount of waste of available heat sources and difficulty in maximizing overall energy efficiency.

[0004] Traditional heat pump systems have limited regulation capabilities and are difficult to adapt to dynamic load fluctuations. In industrial production processes, heat loads usually fluctuate with the production rhythm. In existing technologies, heat pump systems usually operate using preset parameters and lack dynamic adjustment capabilities, resulting in the inability to adapt heat source usage strategies in real time when load changes, resulting in insufficient heat supply or excessive waste. Due to the lack of a real-time feedback mechanism, the system lags in adjustment when load changes, further reducing the efficiency of waste heat recovery.

[0005] The existing technology has weak multi-heat source coupling capabilities. In industrial applications, there are often temperature differences and instabilities between multiple heat sources, and traditional systems usually use a single heat source independently, making it difficult to coordinate the temperature gradients, flow differences and heat transfer characteristics of different heat sources. Even if some technologies can achieve the parallel use of multiple heat sources, they cannot dynamically adjust the coupling state between the heat sources, resulting in insufficient utilization of high-efficiency heat sources and excessive use of inefficient heat sources, reducing the overall system efficiency.

[0006] Existing technologies are insufficient in optimizing the heat recovery path of heat sources. Traditional heat recovery paths usually adopt a fixed mode and are unable to adjust the path design according to real-time operating conditions, which can easily lead to excessive temperature differences or uneven flow distribution, thereby reducing heat transfer efficiency. This lack of flexibility in path design is particularly evident when dynamic loads change and multiple heat sources work together, further affecting the effect of heat recovery.

[0007] Existing technologies lack intelligent learning and feedback mechanisms. During the heat recovery process, traditional technologies are usually controlled based on fixed algorithms or rules, and it is difficult to combine historical operating data and real-time monitoring data for self-learning and optimization. This control method makes it impossible for the system to dynamically adjust the operating mode according to environmental changes and load demands, making it difficult to achieve long-term energy-saving effects. Due to the lack of the ability to predict future load fluctuations and heat source status, traditional systems have poor adaptability in complex industrial environments, which can easily cause instability in system operation.

[0008] Therefore, how to provide a multi-heat source coupled heat pump control system for recovering waste heat from industrial circulating cooling water is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0009] One purpose of the present invention is to propose a multi-heat source coupled heat pump control system for recovering waste heat of industrial circulating cooling water. The present invention uses an intelligent adaptive learning and optimization mechanism to monitor and adjust the working status of different heat sources in real time and optimize the heat source coupling and heat recovery path for a complex environment where multiple heat sources work in parallel. The system adopts technical means such as environmental perception, data processing, and dynamic load adjustment to ensure that the waste heat of cooling water, waste gas heat sources, and low-grade thermal energy in the ambient air can be effectively recovered to maximize the utilization of industrial waste heat resources. The present invention makes full use of advanced technologies such as adaptive learning models, intelligent complementary adjustment modules, dynamic scheduling and feedback mechanisms, monitors the temperature, flow, pressure and efficiency data of multiple heat sources in real time, and continuously optimizes the use strategy and heat recovery path of the heat source through the feedback mechanism, thereby solving the problems of single heat source, lagging adjustment, and waste of heat source in traditional technologies. The present invention has the advantages of high efficiency, intelligence, and strong dynamic adaptability.

[0010] A multi-heat source coupled heat pump control system for recovering waste heat of industrial circulating cooling water according to an embodiment of the present invention comprises the following steps: S1. Before the system is started, the key parameters of multiple heat sources such as cooling water waste heat, exhaust gas heat source, and ambient air are monitored in real time through the environmental sensing system. These data are preliminarily screened through the data processing unit. According to the current environmental conditions and load requirements, the parameters of each heat source are preprocessed to select the heat source set that best suits the current conditions. S2. Based on the obtained heat source set, the adaptive learning model is used to evaluate the recovery potential of each heat source. Based on historical data and real-time monitoring information, the utilization efficiency of each heat source under the current environment and load conditions is predicted. In combination with external load changes, the order, proportion and priority of each heat source are intelligently determined. S3. When multiple heat sources work in parallel, a heat source time window allocation mechanism is used to intelligently divide the use time windows of each heat source based on the characteristics of different heat sources, external load fluctuations and the needs of each production link, so that different heat sources can be dynamically switched according to real-time load changes, avoiding heat source waste and optimizing overall recovery efficiency; S4. After the heat source is input into the heat pump system, the system automatically senses the temperature gradient, flow difference and heat matching between the heat sources through the intelligent complementary regulation module, adjusts the fluid dynamics state of the heat pump system according to the real-time information, optimizes the heat transfer efficiency of each heat source, and maximizes the coupling and energy utilization of the heat source in each working cycle by adjusting the operating parameters of the pump and the fluid path of the heat exchanger; S5. During the recovery process, the heat recovery path of the heat pump is dynamically adjusted in real time through the built-in autonomous temperature control unit and self-optimizing heat recovery path to avoid low heat transfer efficiency caused by excessive temperature difference, so as to maximize heat recovery and precise distribution; S6. Introduce an adaptive feedback mechanism to automatically adjust the heat source usage strategy based on real-time monitored heat source operation data, external load changes and system working status, optimize the scheduling and coupling efficiency of the heat source in real time, generate improvement suggestions and make intelligent adjustments; S7. Through the built-in intelligent learning module, the heat source coupling mode and heat recovery path are optimized according to historical operation data and real-time feedback. The system can self-learn and predict future load fluctuations, heat source status and efficiency changes, and optimize the adaptability, energy-saving effect and stability of the overall system.

[0011] Optionally, the S2 specifically includes: S21. Based on the obtained set of heat sources, the recovery potential of each heat source is evaluated through an adaptive learning model, wherein the evaluation process includes real-time monitoring and data analysis of key parameters of each heat source to determine its recovery potential under current environmental and load conditions; S22. Based on historical data and real-time monitoring information, combined with current environmental changes and external load requirements, the adaptive model is used to predict the efficiency of each heat source, and the real-time optimization of the heat source efficiency is achieved through dynamic adjustment. The prediction process can take into account the impact of external load fluctuations on the heat source efficiency, and predict the optimal use of the heat source in combination with load changes, and dynamically predict the efficiency of each heat source: ; in, For the The recovery efficiency of each heat source is For the The recovered heat from each heat source, For the The temperature of the heat source, Input power to the heat source; S23. Based on the heat source recovery potential and utilization efficiency obtained through evaluation and combined with the changes in external load, the order, proportion and priority of each heat source are intelligently determined, and the scheduling strategy of the heat source is dynamically adjusted according to real-time feedback to maximize the recovery efficiency and avoid energy waste; S24. During the prediction process, the system adjusts the heat source usage strategy in real time through adaptive learning and feedback mechanisms, so that the system can adjust the input sequence and proportion of each heat source according to load fluctuations and environmental changes, automatically generate improvement suggestions and adjust the scheduling of heat sources to optimize the overall recovery efficiency and system performance.

[0012] Optionally, the S3 specifically includes: S31. When multiple heat sources work in parallel, a heat source time window allocation mechanism is adopted according to the characteristics of each heat source and its relationship with external load fluctuations. This mechanism intelligently divides the use time window of the heat source based on the real-time performance data of each heat source, combined with the fluctuation of external load and the needs of the production link, so that each heat source works in an appropriate period of time, avoiding inefficient and unnecessary waste of heat sources; S32. According to the external load fluctuations monitored in real time, combined with the working status of different heat sources and environmental changes, dynamically adjust the time window of the heat source, and adjust the activation order of the heat source according to the current demand by monitoring the real-time load and environmental parameters, so that the system can maximize the use of available heat sources and optimize energy waste; S33. Through real-time data feedback of multiple heat sources, the adaptive learning model is used to dynamically adjust the time window length and switching timing of the heat source, while optimizing the synergistic relationship between high-efficiency heat sources and low-efficiency heat sources. The impact of external load changes on heat source efficiency is predicted through historical load fluctuation data and real-time status, and the start-stop sequence and usage time of each heat source are intelligently predicted and optimized; S34. Through the mechanism of dynamically adjusting the heat source time window, the system can achieve optimized switching of different heat sources. The working cycle of each heat source is automatically switched based on real-time load changes. In the process of optimizing heat source switching and usage strategy, the optimal switching time point of the heat source is calculated: ; in, For the The switching time of each heat source, For the The flow rate of a heat source, For external load changes, , , is the adjustment coefficient, S35. Based on the above adjustment and optimization mechanism, the system can intelligently decide the input priority, usage ratio and optimal switching time of each heat source. By optimizing the working hours of different heat sources, the system can dynamically adjust the ratio between heat sources during the entire recovery process, adapt to load changes and production needs, and optimize the overall recovery efficiency and energy utilization.

[0013] Optionally, the S4 specifically includes: S41. After the heat source is input into the heat pump system, the system monitors the temperature, flow and heat data of each heat source in real time through the intelligent complementary regulation module, obtains the real-time temperature, flow and heat key information from each heat source, and automatically identifies the matching degree between the heat sources according to the temperature gradient and flow difference between the heat sources; S42. Using the acquired real-time monitoring data, the intelligent complementary regulation module analyzes the working status of each heat source, determines its energy transfer efficiency in the heat pump system, and based on this evaluation, the regulation module adjusts the coupling mode between the heat sources, giving priority to ensuring that the heat sources with higher efficiency participate in the work, and avoiding excessive use of inefficient heat sources; S43. After optimizing the heat source coupling, the intelligent complementary regulation module optimizes the dynamic state of the fluid in the heat pump system based on the obtained heat source state data, specifically including adjusting the operating parameters of the pump and the flow path of the fluid, adjusting the heat transfer efficiency between the heat sources by feeding back the temperature and flow data of the heat source, maximizing the efficiency of each heat source, and transmitting the real-time state data to the optimization control system; S44. Based on the adjustment result, the heat transfer efficiency of each heat source in the heat pump system The following formula can be used for real-time optimization to maximize the heat utilization of efficient heat sources, while dynamically compensating for the heat loss of inefficient heat sources to achieve overall energy optimization: ; in, For the Heat source at time The heat transfer efficiency, For the Heat source at time The heat output, is the operating power of the pump, is the pressure difference of the pump, For the Heat source at time The temperature, is the ambient temperature; S45. Based on the adjustment results and real-time feedback data, the system continuously adjusts the coupling state, heat transfer path and dynamic parameters between the heat sources, so that the heat pump system can adapt to load changes and environmental fluctuations, optimize the operation of the heat source and maximize the heat recovery efficiency; S46. With multiple heat sources working together, the system can intelligently learn historical operation data and optimize the scheduling strategy between heat sources based on real-time feedback, so as to continuously optimize the heat recovery efficiency and ultimately minimize the overall energy consumption.

[0014] Optionally, the S5 specifically includes: S51. During the heat source recovery process, the temperature, flow rate and temperature gradient between heat sources are monitored in real time through the built-in autonomous temperature control unit, and real-time data is dynamically collected and analyzed to obtain the working status information of the heat source; S52. Based on the real-time monitoring data, the intelligent control module analyzes the energy transfer state of the heat source, and dynamically adjusts the fluid dynamics state of the heat recovery path in combination with the current load and system requirements, including adjusting the operating parameters of the pump and adjusting the flow path of the fluid to optimize the coupling of the heat source and the energy transfer efficiency; S53, through the role of the autonomous temperature control unit in the recovery path, according to the real-time temperature difference and flow rate changes, avoid low heat transfer efficiency caused by excessive temperature difference, and achieve maximum recovery and precise distribution of heat by adjusting the temperature and flow rate distribution of the heat pump system; S54, when adjusting the heat recovery path, the self-optimization module dynamically optimizes the heat distribution and path selection based on the temperature, flow rate and working status data of each heat source, so as to optimally match the working cycles of different heat sources and ensure the stability and efficiency of the heat transfer process; S55. Through the real-time feedback of the heat source status data, the intelligent control module calculates the optimal switching timing and usage ratio of each heat source in the heat recovery path, and dynamically adjusts the heat recovery efficiency of the heat source through the following innovative formula: ; in, For the Heat source at time Optimized heat recovery efficiency, For the Heat source at time The temperature difference, For the The flow rate of a heat source, For the Constants related to heat source characteristics is the target heat source temperature, is the target flow value, , For the The weight adjustment coefficient related to the temperature and flow changes of each heat source; S56, by optimizing the heat recovery efficiency , combined with real-time monitored load demand, dynamically adjust the working status of each heat source to keep the system heat transfer path in the optimal state during each working cycle, maximize heat recovery efficiency and optimize energy waste.

[0015] Optionally, the S6 specifically includes: S61. Based on the real-time monitored heat source operation data, external load changes and system working status, the system collects and analyzes the temperature, flow, pressure, energy efficiency and environmental data from each heat source through an adaptive feedback mechanism, obtains the working status of each heat source in real time, and evaluates the adaptability of the current heat source to the system requirements; S62. After comprehensively analyzing the data collected in real time, the system uses an adaptive learning model to evaluate the impact of external load changes on the operation of the heat source, and dynamically adjusts the heat source usage strategy according to the current system workload and external demand forecast, including the priority of the heat source, the start-stop sequence, and the load distribution strategy, so that the heat source can be maximized during the working process; S63. After completing the optimization of the heat source use strategy, the system dynamically adjusts the heat source scheduling strategy based on the calculation results. The adjustment strategy is based on real-time load fluctuations, system requirements and heat source performance predictions, and reasonably allocates the working time and usage ratio of each heat source to optimize the overall efficiency of the system and avoid ineffective and excessive operation. S64. After the scheduling strategy is adjusted, the system generates improvement suggestions based on the optimization results, transmits the feedback data to the intelligent learning module, and updates the adaptive model in real time, so that it can dynamically adjust the scheduling algorithm according to environmental fluctuations and load changes, and optimize the operation mode and load distribution of the heat source; S65. Based on real-time data feedback and external load fluctuations, the system continuously optimizes the scheduling and coupling efficiency of heat sources. By real-time monitoring of changes in external loads and the working status of internal heat sources, the system adjusts the operating sequence and load distribution of each heat source so that each heat source operates at the appropriate time and load conditions.

[0016] Optionally, the S7 specifically includes: S71. Through the built-in intelligent learning module, the system collects historical operation data and real-time feedback information, monitors the operation status of each heat source in real time, including temperature, flow, pressure, and energy efficiency parameters, and analyzes the performance change trend of the heat source based on these data; S72. Based on historical data and real-time feedback, combined with external load changes and internal operating status, the system uses an adaptive learning model to predict future load fluctuations, evaluate the impact of load fluctuations on the performance of each heat source, and predict the efficiency and operating status of the heat source in the future; S73. Based on the predicted load fluctuations and heat source performance changes, the system dynamically optimizes the heat source coupling mode and heat recovery path. Combined with real-time data, the system adjusts the start and stop sequence, usage ratio and load distribution strategy of the heat sources to achieve collaborative work among the heat sources. The system also evaluates the overall optimization effect of the system in real time through the following formula: ; in, For the system at time The overall optimization efficiency, For the The workload of a heat source, is the total system load, For the Heat source at time Adjusted efficiency S74. In the process of optimizing the heat source coupling mode and the recovery path, the system feeds back the prediction results in real time, makes adjustments based on the following formula, and generates an optimization strategy for the heat source: ; in, For the Heat source at time The adjusted efficiency, , , For the The weighting coefficients associated with each heat source, For the Heat source at time The temperature, For the ideal working temperature, For the Heat source at time of traffic, is the average flow rate of all heat sources, is the external load at time The value of is the historical average value of external load, For the Heat source at time The power, is the optimal power value of the heat source, For the Power deviation adjustment factor for each heat source; S75. The system continuously makes adaptive adjustments through the intelligent learning module, optimizes the scheduling and coupling efficiency of the heat source in real time according to the working status of the heat source, changes in the external load and the energy efficiency requirements of the system, optimizes the adaptability, energy-saving effect and stability of the overall system, and enables the system to continue to operate in the optimal state under changing environments.

[0017] Optional modules include: Environmental sensing module, used to monitor key parameters of multiple heat sources including cooling water waste heat, exhaust gas heat source, and ambient air in real time; The data processing unit screens and pre-processes the real-time monitoring data, and selects the most suitable heat source set for the current conditions according to the environmental conditions and load requirements; Adaptive learning model, used to evaluate the recovery potential of heat sources, combine historical data and real-time monitoring information to predict the efficiency of each heat source, and intelligently determine the order, proportion and priority of each heat source; The heat source time window allocation mechanism dynamically adjusts the heat source usage time window based on real-time monitoring of external load fluctuations and production needs, so that the heat source can be dynamically switched according to real-time load changes; Intelligent complementary regulation module, used to optimize the fluid dynamics state of the heat pump system, adjust the heat source coupling mode and operating parameters based on the heat source temperature gradient, flow difference and heat matching; The autonomous temperature control unit dynamically adjusts the heat recovery path in real time, optimizes the heat transfer efficiency of the heat source according to the temperature difference and flow rate changes, and avoids low heat transfer efficiency due to excessive temperature difference; Adaptive feedback mechanism dynamically optimizes the heat source scheduling strategy and generates improvement suggestions based on heat source operation data, external load changes and system working status; The intelligent learning module predicts future load fluctuations based on historical operating data and real-time feedback, dynamically optimizes the heat source coupling mode and heat recovery path, and optimizes the energy-saving effect and stability of the system.

[0018] The beneficial effects of the present invention are: The present invention monitors the key parameters of various heat sources such as cooling water waste heat, exhaust gas heat source, ambient air, etc. in real time, and combines environmental sensing technology and data processing units to accurately screen out the heat source set that best suits the current environment and load conditions. This process avoids the heat waste caused by improper heat source selection in traditional systems and ensures efficient use of heat sources.

[0019] The present invention dynamically evaluates the heat source recovery potential through an adaptive learning model, so that the order of heat source input, usage ratio and priority can be intelligently adjusted according to real-time environmental changes and load requirements. Compared with traditional technologies, the system can more accurately predict load fluctuations and heat source state changes, thereby optimizing the scheduling of heat sources, avoiding the problem of excessive or insufficient use of heat sources due to excessive load fluctuations in the past system, and effectively improving the flexibility and accuracy of heat source scheduling. In the process of heat source coupling and heat recovery, the system uses an intelligent complementary adjustment module to automatically sense the temperature gradient and flow difference between heat sources, and adjust the fluid dynamics state of the heat pump system according to real-time information to ensure the maximum efficiency of heat transfer. This method not only optimizes the heat transfer efficiency of each heat source by adjusting the operating parameters of the pump and the fluid path of the heat exchanger, but also avoids the low heat transfer efficiency caused by excessive temperature difference, thereby ensuring efficient coupling of heat sources and maximum energy utilization.

[0020] The self-optimizing heat recovery path and autonomous temperature control unit of the present invention can dynamically adjust the recovery path according to real-time monitoring data, optimize heat distribution, and avoid low heat recovery efficiency caused by improper path setting or excessive temperature difference in traditional systems. Through feedback analysis of real-time data, the system can accurately adjust the start and stop sequence and working mode of the heat source, thereby further improving the overall stability and energy-saving effect of the system. Through continuously optimized scheduling strategies and adaptive feedback mechanisms, the present invention realizes efficient coupling and reasonable scheduling of heat sources, so that different heat sources can adapt to external load fluctuations to the maximum extent during operation, avoid energy waste, and improve the overall energy-saving performance and operational stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a multi-heat source coupled heat pump control system for recovering waste heat of industrial circulating cooling water proposed by the present invention; Figure 2 A schematic diagram of the heat source time window allocation mechanism proposed by the present invention; Figure 3 A schematic diagram of fluid dynamics regulation and heat transfer optimization of a heat pump system is proposed for the present invention; Figure 4 A schematic diagram of the adaptive feedback mechanism proposed in the present invention for optimizing heat source scheduling and coupling efficiency. DETAILED DESCRIPTION

[0022] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0023] refer to Figure 1-4 , a multi-heat source coupled heat pump control system for recovering waste heat of industrial circulating cooling water, comprising the following steps: S1. Before the system is started, the key parameters of multiple heat sources such as cooling water waste heat, exhaust gas heat source, and ambient air are monitored in real time through the environmental sensing system. These data are preliminarily screened through the data processing unit. According to the current environmental conditions and load requirements, the parameters of each heat source are preprocessed to select the heat source set that best suits the current conditions. S2. Based on the obtained heat source set, the adaptive learning model is used to evaluate the recovery potential of each heat source. Based on historical data and real-time monitoring information, the utilization efficiency of each heat source under the current environment and load conditions is predicted. In combination with external load changes, the order, proportion and priority of each heat source are intelligently determined. S3. When multiple heat sources work in parallel, a heat source time window allocation mechanism is used to intelligently divide the use time windows of each heat source based on the characteristics of different heat sources, external load fluctuations and the needs of each production link, so that different heat sources can be dynamically switched according to real-time load changes, avoiding heat source waste and optimizing overall recovery efficiency; S4. After the heat source is input into the heat pump system, the system automatically senses the temperature gradient, flow difference and heat matching between the heat sources through the intelligent complementary regulation module, adjusts the fluid dynamics state of the heat pump system according to the real-time information, optimizes the heat transfer efficiency of each heat source, and maximizes the coupling and energy utilization of the heat source in each working cycle by adjusting the operating parameters of the pump and the fluid path of the heat exchanger; S5. During the recovery process, the heat recovery path of the heat pump is dynamically adjusted in real time through the built-in autonomous temperature control unit and self-optimizing heat recovery path to avoid low heat transfer efficiency caused by excessive temperature difference, so as to maximize heat recovery and precise distribution; S6. Introduce an adaptive feedback mechanism to automatically adjust the heat source usage strategy based on real-time monitored heat source operation data, external load changes and system working status, optimize the scheduling and coupling efficiency of the heat source in real time, generate improvement suggestions and make intelligent adjustments; S7. Through the built-in intelligent learning module, the heat source coupling mode and heat recovery path are optimized according to historical operation data and real-time feedback. The system can self-learn and predict future load fluctuations, heat source status and efficiency changes, and optimize the adaptability, energy-saving effect and stability of the overall system.

[0024] In this implementation, S2 specifically includes: S21. Based on the obtained set of heat sources, the recovery potential of each heat source is evaluated through an adaptive learning model, wherein the evaluation process includes real-time monitoring and data analysis of key parameters of each heat source to determine its recovery potential under current environmental and load conditions; S22. Based on historical data and real-time monitoring information, combined with current environmental changes and external load requirements, the adaptive model is used to predict the efficiency of each heat source, and the real-time optimization of the heat source efficiency is achieved through dynamic adjustment. The prediction process can take into account the impact of external load fluctuations on the heat source efficiency, and predict the optimal use of the heat source in combination with load changes, and dynamically predict the efficiency of each heat source: ; in, For the The recovery efficiency of each heat source is For the The recovered heat from each heat source, For the The temperature of the heat source, Input power to the heat source; S23. Based on the heat source recovery potential and utilization efficiency obtained through evaluation and combined with the changes in external load, the order, proportion and priority of each heat source are intelligently determined, and the scheduling strategy of the heat source is dynamically adjusted according to real-time feedback to maximize the recovery efficiency and avoid energy waste; S24. During the prediction process, the system adjusts the heat source usage strategy in real time through adaptive learning and feedback mechanisms, so that the system can adjust the input sequence and proportion of each heat source according to load fluctuations and environmental changes, automatically generate improvement suggestions and adjust the scheduling of heat sources to optimize the overall recovery efficiency and system performance.

[0025] In this implementation, S3 specifically includes: S31. When multiple heat sources work in parallel, a heat source time window allocation mechanism is adopted according to the characteristics of each heat source and its relationship with external load fluctuations. This mechanism intelligently divides the use time window of the heat source based on the real-time performance data of each heat source, combined with the fluctuation of external load and the needs of the production link, so that each heat source works in an appropriate period of time, avoiding inefficient and unnecessary waste of heat sources; S32. According to the external load fluctuations monitored in real time, combined with the working status of different heat sources and environmental changes, dynamically adjust the time window of the heat source, and adjust the activation order of the heat source according to the current demand by monitoring the real-time load and environmental parameters, so that the system can maximize the use of available heat sources and optimize energy waste; S33. Through real-time data feedback of multiple heat sources, the adaptive learning model is used to dynamically adjust the time window length and switching timing of the heat source, while optimizing the synergistic relationship between high-efficiency heat sources and low-efficiency heat sources. The impact of external load changes on heat source efficiency is predicted through historical load fluctuation data and real-time status, and the start-stop sequence and usage time of each heat source are intelligently predicted and optimized; S34. Through the mechanism of dynamically adjusting the heat source time window, the system can achieve optimized switching of different heat sources. The working cycle of each heat source is automatically switched based on real-time load changes. In the process of optimizing heat source switching and usage strategy, the optimal switching time point of the heat source is calculated: ; in, For the The switching time of each heat source, For the The flow rate of a heat source, For external load changes, , , is the adjustment coefficient, S35. Based on the above adjustment and optimization mechanism, the system can intelligently decide the input priority, usage ratio and optimal switching time of each heat source. By optimizing the working hours of different heat sources, the system can dynamically adjust the ratio between heat sources during the entire recovery process, adapt to load changes and production needs, and optimize the overall recovery efficiency and energy utilization.

[0026] In this implementation, S4 specifically includes: S41. After the heat source is input into the heat pump system, the system monitors the temperature, flow and heat data of each heat source in real time through the intelligent complementary regulation module, obtains the real-time temperature, flow and heat key information from each heat source, and automatically identifies the matching degree between the heat sources according to the temperature gradient and flow difference between the heat sources; S42. Using the acquired real-time monitoring data, the intelligent complementary regulation module analyzes the working status of each heat source, determines its energy transfer efficiency in the heat pump system, and based on this evaluation, the regulation module adjusts the coupling mode between the heat sources, giving priority to ensuring that the heat sources with higher efficiency participate in the work, and avoiding excessive use of inefficient heat sources; S43. After optimizing the heat source coupling, the intelligent complementary regulation module optimizes the dynamic state of the fluid in the heat pump system based on the obtained heat source state data, specifically including adjusting the operating parameters of the pump and the flow path of the fluid, adjusting the heat transfer efficiency between the heat sources by feeding back the temperature and flow data of the heat source, maximizing the efficiency of each heat source, and transmitting the real-time state data to the optimization control system; S44. Based on the adjustment result, the heat transfer efficiency of each heat source in the heat pump system The following formula can be used for real-time optimization to maximize the heat utilization of efficient heat sources, while dynamically compensating for the heat loss of inefficient heat sources to achieve overall energy optimization: ; in, For the Heat source at time The heat transfer efficiency, For the Heat source at time The heat output, is the operating power of the pump, is the pressure difference of the pump, For the Heat source at time The temperature, is the ambient temperature; S45. Based on the adjustment results and real-time feedback data, the system continuously adjusts the coupling state, heat transfer path and dynamic parameters between the heat sources, so that the heat pump system can adapt to load changes and environmental fluctuations, optimize the operation of the heat source and maximize the heat recovery efficiency; S46. With multiple heat sources working together, the system can intelligently learn historical operation data and optimize the scheduling strategy between heat sources based on real-time feedback, so as to continuously optimize the heat recovery efficiency and ultimately minimize the overall energy consumption.

[0027] In this implementation manner, S5 specifically includes: S51. During the heat source recovery process, the temperature, flow rate and temperature gradient between heat sources are monitored in real time through the built-in autonomous temperature control unit, and real-time data is dynamically collected and analyzed to obtain the working status information of the heat source; S52. Based on the real-time monitoring data, the intelligent control module analyzes the energy transfer state of the heat source, and dynamically adjusts the fluid dynamics state of the heat recovery path in combination with the current load and system requirements, including adjusting the operating parameters of the pump and adjusting the flow path of the fluid to optimize the coupling of the heat source and the energy transfer efficiency; S53, through the role of the autonomous temperature control unit in the recovery path, according to the real-time temperature difference and flow rate changes, avoid low heat transfer efficiency caused by excessive temperature difference, and achieve maximum recovery and precise distribution of heat by adjusting the temperature and flow rate distribution of the heat pump system; S54, when adjusting the heat recovery path, the self-optimization module dynamically optimizes the heat distribution and path selection based on the temperature, flow rate and working status data of each heat source, so as to optimally match the working cycles of different heat sources and ensure the stability and efficiency of the heat transfer process; S55. Through the real-time feedback of the heat source status data, the intelligent control module calculates the optimal switching timing and usage ratio of each heat source in the heat recovery path, and dynamically adjusts the heat recovery efficiency of the heat source through the following innovative formula: ; in, For the Heat source at time Optimized heat recovery efficiency, For the Heat source at time The temperature difference, For the The flow rate of a heat source, For the Constants related to heat source characteristics is the target heat source temperature, is the target flow value, , For the The weight adjustment coefficient related to the temperature and flow changes of each heat source; S56, by optimizing the heat recovery efficiency , combined with real-time monitored load demand, dynamically adjust the working status of each heat source to keep the system heat transfer path in the optimal state during each working cycle, maximize heat recovery efficiency and optimize energy waste.

[0028] In this implementation manner, S6 specifically includes: S61. Based on the real-time monitored heat source operation data, external load changes and system working status, the system collects and analyzes the temperature, flow, pressure, energy efficiency and environmental data from each heat source through an adaptive feedback mechanism, obtains the working status of each heat source in real time, and evaluates the adaptability of the current heat source to the system requirements; S62. After comprehensively analyzing the data collected in real time, the system uses an adaptive learning model to evaluate the impact of external load changes on the operation of the heat source, and dynamically adjusts the heat source usage strategy according to the current system workload and external demand forecast, including the priority of the heat source, the start-stop sequence, and the load distribution strategy, so that the heat source can be maximized during the working process; S63. After completing the optimization of the heat source use strategy, the system dynamically adjusts the heat source scheduling strategy based on the calculation results. The adjustment strategy is based on real-time load fluctuations, system requirements and heat source performance predictions, and reasonably allocates the working time and usage ratio of each heat source to optimize the overall efficiency of the system and avoid ineffective and excessive operation. S64. After the scheduling strategy is adjusted, the system generates improvement suggestions based on the optimization results, transmits the feedback data to the intelligent learning module, and updates the adaptive model in real time, so that it can dynamically adjust the scheduling algorithm according to environmental fluctuations and load changes, and optimize the operation mode and load distribution of the heat source; S65. Based on real-time data feedback and external load fluctuations, the system continuously optimizes the scheduling and coupling efficiency of heat sources. By real-time monitoring of changes in external loads and the working status of internal heat sources, the system adjusts the operating sequence and load distribution of each heat source so that each heat source operates at the appropriate time and load conditions.

[0029] In this implementation manner, the S7 specifically includes: S71. Through the built-in intelligent learning module, the system collects historical operation data and real-time feedback information, monitors the operation status of each heat source in real time, including temperature, flow, pressure, and energy efficiency parameters, and analyzes the performance change trend of the heat source based on these data; S72. Based on historical data and real-time feedback, combined with external load changes and internal operating status, the system uses an adaptive learning model to predict future load fluctuations, evaluate the impact of load fluctuations on the performance of each heat source, and predict the efficiency and operating status of the heat source in the future; S73. Based on the predicted load fluctuations and heat source performance changes, the system dynamically optimizes the heat source coupling mode and heat recovery path. Combined with real-time data, the system adjusts the start and stop sequence, usage ratio and load distribution strategy of the heat sources to achieve collaborative work among the heat sources. The system also evaluates the overall optimization effect of the system in real time through the following formula: ; in, For the system at time The overall optimization efficiency, For the The workload of a heat source, is the total system load, For the Heat source at time Adjusted efficiency S74. In the process of optimizing the heat source coupling mode and the recovery path, the system feeds back the prediction results in real time, makes adjustments based on the following formula, and generates an optimization strategy for the heat source: ; in, For the Heat source at time The adjusted efficiency, , , For the The weighting coefficients associated with each heat source, For the Heat source at time The temperature, For the ideal working temperature, For the Heat source at time of traffic, is the average flow rate of all heat sources, is the external load at time The value of is the historical average value of external load, For the Heat source at time The power, is the optimal power value of the heat source, For the Power deviation adjustment factor for each heat source; S75. The system continuously makes adaptive adjustments through the intelligent learning module, optimizes the scheduling and coupling efficiency of the heat source in real time according to the working status of the heat source, changes in the external load and the energy efficiency requirements of the system, optimizes the adaptability, energy-saving effect and stability of the overall system, and enables the system to continue to operate in the optimal state under changing environments.

[0030] In this embodiment, the following modules are included: Environmental sensing module, used to monitor key parameters of multiple heat sources including cooling water waste heat, exhaust gas heat source, and ambient air in real time; The data processing unit screens and pre-processes the real-time monitoring data, and selects the most suitable heat source set for the current conditions according to the environmental conditions and load requirements; Adaptive learning model, used to evaluate the recovery potential of heat sources, combine historical data and real-time monitoring information to predict the efficiency of each heat source, and intelligently determine the order, proportion and priority of each heat source; The heat source time window allocation mechanism dynamically adjusts the heat source usage time window based on real-time monitoring of external load fluctuations and production needs, so that the heat source can be dynamically switched according to real-time load changes; Intelligent complementary regulation module, used to optimize the fluid dynamics state of the heat pump system, adjust the heat source coupling mode and operating parameters based on the heat source temperature gradient, flow difference and heat matching; The autonomous temperature control unit dynamically adjusts the heat recovery path in real time, optimizes the heat transfer efficiency of the heat source according to the temperature difference and flow rate changes, and avoids low heat transfer efficiency due to excessive temperature difference; Adaptive feedback mechanism dynamically optimizes the heat source scheduling strategy and generates improvement suggestions based on heat source operation data, external load changes and system working status; The intelligent learning module predicts future load fluctuations based on historical operating data and real-time feedback, dynamically optimizes the heat source coupling mode and heat recovery path, and optimizes the energy-saving effect and stability of the system.

[0031] Embodiment 1: In order to verify the feasibility of the present invention in implementation, the present invention is applied to the actual production process of a steel plant to explore how to recover the waste heat of circulating cooling water through the system and optimize the scheduling, coupling and heat recovery efficiency of the heat source. This embodiment focuses on demonstrating how the system solves the problems of heat source waste, low energy efficiency and unreasonable scheduling in the prior art, and gives specific operating data and beneficial effects.

[0032] A steel plant is carrying out high-temperature smelting production. During the production process, a large amount of waste gas, waste heat, circulating cooling water and other heat sources are generated. These heat sources include waste heat in the waste gas, heat in the circulating cooling water and heat in the ambient air. The traditional recovery method mainly relies on a fixed heat source access method. The recovery process cannot be dynamically adjusted according to the actual environment and production load, resulting in low heat source utilization efficiency. In addition, excessive temperature differences often occur, resulting in low heat transfer efficiency. In order to achieve more efficient heat recovery and utilization, the steel plant introduced the multi-heat source coupled heat pump control system of the present invention.

[0033] In this application scenario, before the system is started, the steel plant's environmental perception system monitors the key parameters of exhaust gas, cooling water and air in real time, including temperature, flow and heat. Through the data processing unit, the system preliminarily screens these data and selects the heat source set that best suits the current production load conditions. Through this process, the status data of each heat source is obtained in real time, and the system automatically evaluates the current utilization potential of each heat source. For example, at a certain moment, the cooling water temperature is 45°C, the flow rate is 1,000 cubic meters per hour, the exhaust gas temperature is 250°C, the flow rate is 1,500 cubic meters per hour, and the ambient air temperature is 30°C. Through the adaptive learning model, the system predicts the efficiency of each heat source under the current environment and load conditions based on historical data and real-time monitoring information, and intelligently determines the order, proportion and priority of each heat source in combination with external load changes. Through this process, the system can ensure the optimal heat source input, so that the utilization ratio of exhaust gas, cooling water and ambient air can be optimized.

[0034] When multiple heat sources work in parallel, the system uses the heat source time window allocation mechanism to intelligently divide the usage time windows of each heat source. Different heat sources are dynamically switched according to real-time load changes to avoid excessive or inefficient use of a certain heat source. For example, when the load of the steel plant is high, the exhaust gas heat source has a higher priority, and the system automatically adjusts the flow and temperature of the exhaust gas to make it more in line with current production needs. When the load is low, the cooling water heat source will be given an increased priority to ensure full utilization of the cooling water at low loads.

[0035] In addition, the system automatically senses the temperature gradient, flow difference and heat matching between heat sources through the intelligent complementary regulation module, and adjusts the fluid dynamics state of the heat pump system according to the real-time information, thereby optimizing the heat transfer efficiency of each heat source. For example, when the temperature of the exhaust gas is too high, the system automatically adjusts the fluid path of the heat pump to prevent the high-temperature exhaust gas from excessively heating the cooling water, ensuring that the heat transfer efficiency is not affected. Through this precise regulation, the coupling efficiency of the heat source is significantly improved.

[0036] During the recovery process, the system dynamically adjusts the heat recovery path of the heat pump in real time through the built-in autonomous temperature control unit and self-optimizing heat recovery path to avoid low heat transfer efficiency due to excessive temperature difference. In a certain period of time, the temperature of the exhaust gas is higher and the temperature of the cooling water is lower. The system automatically adjusts the heat exchange method between the two with a large temperature difference based on real-time data to maximize the efficiency of heat recovery. The system dynamically adjusts the temperature and flow distribution of the heat pump system according to actual temperature changes and load fluctuations to ensure that the heat transfer between the heat sources is as efficient and balanced as possible.

[0037] Through the adaptive feedback mechanism, the system automatically adjusts the heat source usage strategy according to real-time monitoring data, external load changes and system operating status, and optimizes the scheduling and coupling efficiency of the heat source in real time. For example, at a certain stage, the external load suddenly changes. The system can respond quickly and adjust the order and proportion of heat source input according to the new load conditions, so that each heat source works under the optimal conditions, thus avoiding unnecessary energy waste.

[0038] The implementation of this system has significantly improved the utilization rate of heat sources, reduced energy waste, and achieved efficient recovery of heat sources through intelligent adjustment and optimization. In actual applications, the system can optimize the scheduling strategy of heat sources in real time according to different production loads and environmental conditions, so that the heat recovery efficiency has been increased from the original 65% to more than 85%, with significant energy-saving effects. Through dynamic adjustment and real-time optimization, the system not only improves the utilization rate of heat sources, but also reduces the excessive operation and unnecessary energy consumption of the heat pump system, thereby significantly reducing production costs and operating expenses.

[0039] Table 1 Heat source recovery efficiency optimization data

[0040] From the data in the above table, it can be seen that in terms of the utilization efficiency of important heat sources such as exhaust gas heat source and circulating cooling water, before optimization, the recovery efficiency of exhaust gas heat source was 85%, which was increased to 92% after optimization, and the efficiency was increased by 7%; the recovery efficiency of circulating cooling water increased from 65% to 78%, an increase of 13%. In the optimization of the ambient air heat source, the recovery efficiency increased from 50% to 68%, and the efficiency increased by 18%. These results show that the heat source coupling and heat recovery path optimization technology of the present invention can significantly improve the recovery efficiency of various heat sources, thereby improving the energy utilization rate of the entire system. The data of temperature difference and flow change also prove that The role of the intelligent complementary regulation module in the optimization process is to avoid the low heat transfer efficiency caused by excessive temperature difference by adjusting the temperature difference and flow change between heat sources in real time, and further improve the heat recovery and transfer efficiency, especially the flow change of exhaust gas heat source and circulating cooling water, which shows the flexibility and adaptability of the system in dealing with different workloads. The improvement of recovery efficiency after optimization shows that the technology of the present invention can effectively improve the utilization efficiency of heat sources, reduce energy waste, and improve the overall performance of the system. It can also respond to external load changes in real time, realize efficient scheduling and coupling of heat sources, and achieve good energy-saving effects and operational stability.

[0041] In summary, by adopting the multi-heat source coupled heat pump control system of the present invention, the problems of heat source waste, low heat transfer efficiency, unreasonable heat source scheduling, etc. existing in the traditional heat source recovery system are successfully solved, the heat recovery efficiency and system stability are significantly improved, and an efficient and intelligent solution is provided for industrial waste heat recovery.

[0042] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A multi-heat source coupled heat pump control method for recovering waste heat from industrial circulating cooling water, characterized in that: The steps include: S1. Before the system is started, the key parameters of multiple heat sources such as cooling water waste heat, exhaust gas heat source, and ambient air are monitored in real time through the environmental sensing system. These data are preliminarily screened through the data processing unit. According to the current environmental conditions and load requirements, the parameters of each heat source are preprocessed to select the heat source set that best suits the current conditions. S2. Based on the obtained heat source set, the adaptive learning model is used to evaluate the recovery potential of each heat source. Based on historical data and real-time monitoring information, the utilization efficiency of each heat source under the current environment and load conditions is predicted. In combination with external load changes, the order, proportion and priority of each heat source are intelligently determined. S3. When multiple heat sources work in parallel, a heat source time window allocation mechanism is used to intelligently divide the use time window of each heat source according to the characteristics of different heat sources, external load fluctuations and the needs of each production link, and control the start and stop sequence and operation cycle of each heat source based on external load changes; S4. After the heat source is input into the heat pump system, the system automatically senses the temperature gradient, flow difference and heat matching between the heat sources through the intelligent complementary adjustment module, adjusts the fluid dynamics state of the heat pump system according to the real-time information, optimizes the heat transfer efficiency of each heat source, and realizes the coupling of heat source heat transfer and coordinated energy utilization in each working cycle by adjusting the operating parameters of the pump and the fluid path mode of the heat exchanger; S5. During the recovery process, the heat recovery path of the heat pump is dynamically adjusted in real time through the built-in autonomous temperature control unit and self-optimizing heat recovery path, and the fluid path and flow distribution are adjusted according to the temperature difference; S6. Introduce an adaptive feedback mechanism to automatically adjust the heat source usage strategy based on real-time monitored heat source operation data, external load changes and system working status, optimize the scheduling and coupling efficiency of the heat source in real time, generate improvement suggestions and make intelligent adjustments; S7. Through the built-in intelligent learning module, the heat source coupling mode and heat recovery path are optimized according to historical operation data and real-time feedback. The system can self-learn and predict future load fluctuations, heat source status and efficiency changes, and optimize the adaptability, energy-saving effect and stability of the overall system.

2. A multi-heat source coupled heat pump control method for recovering waste heat of industrial circulating cooling water according to claim 1, characterized in that: The S2 specifically includes: S21. Based on the obtained set of heat sources, the recovery potential of each heat source is evaluated through an adaptive learning model, wherein the evaluation process includes real-time monitoring and data analysis of key parameters of each heat source to determine its recovery potential under current environmental and load conditions; S22. Based on historical data and real-time monitoring information, combined with current environmental changes and external load requirements, the adaptive model is used to predict the efficiency of each heat source, and the real-time optimization of the efficiency of the heat source is achieved through dynamic adjustment. The optimal use of the heat source is predicted in combination with load changes, and the efficiency of each heat source is dynamically predicted: ； in, For the The recovery efficiency of each heat source is For the The recovered heat from each heat source, For the The temperature of the heat source, Input power to the heat source; S23. Based on the heat recovery potential and utilization efficiency obtained through evaluation and combined with the changes in external load, the order, proportion and priority of each heat source are intelligently determined, and the scheduling strategy of the heat source is dynamically adjusted according to real-time feedback; S24. During the prediction process, the system adjusts the heat source usage strategy in real time through adaptive learning and feedback mechanisms, so that the system can adjust the input sequence and proportion of each heat source according to load fluctuations and environmental changes, automatically generate improvement suggestions and adjust the scheduling of heat sources to optimize the overall recovery efficiency and system performance.

3. The multi-heat source coupled heat pump control method for recovering waste heat of industrial circulating cooling water according to claim 1 is characterized in that: The S3 specifically includes: S31. When multiple heat sources work in parallel, a heat source time window allocation mechanism is adopted according to the characteristics of each heat source and its relationship with external load fluctuations. The heat source time window allocation mechanism intelligently divides the use time window of the heat source based on the real-time performance data of each heat source, combined with the fluctuation of external load and the needs of the production link, so that each heat source works in an appropriate time period; S32. According to the external load fluctuations monitored in real time, combined with the working status of different heat sources and environmental changes, dynamically adjust the time window of the heat source, and adjust the activation order of the heat source according to the current demand by monitoring the real-time load and environmental parameters, so that the system can maximize the use of available heat sources and optimize energy waste; S33. Through real-time data feedback of multiple heat sources, the adaptive learning model is used to dynamically adjust the time window length and switching timing of the heat source, while optimizing the synergistic relationship between high-efficiency heat sources and low-efficiency heat sources. The impact of external load changes on heat source efficiency is predicted through historical load fluctuation data and real-time status, and the start-stop sequence and usage time of each heat source are intelligently predicted and optimized; S34. Through the mechanism of dynamically adjusting the heat source time window, the system can achieve optimized switching of different heat sources. The working cycle of each heat source is automatically switched based on real-time load changes. In the process of optimizing heat source switching and usage strategy, the optimal switching time point of the heat source is calculated: ； in, For the The switching time of each heat source, For the The flow rate of a heat source, For external load changes, , , is the adjustment coefficient, S35. Based on S31-S34, the system sets the order of input, usage ratio and switching timing of each heat source according to the characteristics, operating status and external load data of each heat source, divides the working time period of each heat source, and dynamically adjusts the usage ratio between each heat source.

4. A multi-heat source coupled heat pump control method for recovering waste heat of industrial circulating cooling water according to claim 1, characterized in that: The S4 specifically includes: S41. After the heat source is input into the heat pump system, the system monitors the temperature, flow and heat data of each heat source in real time through the intelligent complementary regulation module, obtains the real-time temperature, flow and heat key information from each heat source, and automatically identifies the matching degree between the heat sources according to the temperature gradient and flow difference between the heat sources; S42, using the acquired real-time monitoring data, the intelligent complementary regulation module analyzes the working status of each heat source, determines its energy transfer efficiency in the heat pump system, and adjusts the coupling mode between each heat source; S43. After optimizing the heat source coupling, the intelligent complementary regulation module optimizes the dynamic state of the fluid in the heat pump system based on the obtained heat source state data, specifically including adjusting the operating parameters of the pump and the flow path of the fluid, adjusting the heat transfer efficiency between the heat sources by feeding back the temperature and flow data of the heat source, maximizing the efficiency of each heat source, and transmitting the real-time state data to the optimization control system; S44. Based on the adjustment result, the heat transfer efficiency of each heat source in the heat pump system The following formula can be used for real-time optimization to maximize the heat utilization of efficient heat sources, while dynamically compensating for the heat loss of inefficient heat sources to achieve overall energy optimization: ； in, For the Heat source at time The heat transfer efficiency, For the Heat source at time The heat output, is the operating power of the pump, is the pressure difference of the pump, For the Heat source at time The temperature, is the ambient temperature; S45. Based on the adjustment results and real-time feedback data, the system continuously adjusts the coupling state, heat transfer path and dynamic parameters between the heat sources, so that the heat pump system can adapt to load changes and environmental fluctuations, optimize the operation of the heat source and maximize the heat recovery efficiency; S46. With multiple heat sources working together, the system can intelligently learn historical operation data and optimize the scheduling strategy between heat sources based on real-time feedback, so as to continuously optimize the heat recovery efficiency and ultimately minimize the overall energy consumption.

5. The multi-heat source coupled heat pump control method for recovering waste heat of industrial circulating cooling water according to claim 1 is characterized in that: The S5 specifically includes: S51. During the heat source recovery process, the temperature, flow rate and temperature gradient between heat sources are monitored in real time through the built-in autonomous temperature control unit, and real-time data is dynamically collected and analyzed to obtain the working status information of the heat source; S52. Based on the real-time monitoring data, the intelligent control module analyzes the energy transfer state of the heat source, and dynamically adjusts the fluid dynamics state of the heat recovery path in combination with the current load and system requirements, including adjusting the operating parameters of the pump and adjusting the flow path of the fluid to optimize the coupling of the heat source and the energy transfer efficiency; S53, through the role of the autonomous temperature control unit in the recovery path, according to the real-time temperature difference and flow rate changes, avoid low heat transfer efficiency caused by excessive temperature difference, and achieve maximum recovery and precise distribution of heat by adjusting the temperature and flow rate distribution of the heat pump system; S54, when adjusting the heat recovery path, the self-optimization module dynamically optimizes the heat distribution and path selection based on the temperature, flow rate and working status data of each heat source, so as to optimally match the working cycles of different heat sources and ensure the stability and efficiency of the heat transfer process; S55. Through the real-time feedback of the heat source status data, the intelligent control module calculates the optimal switching timing and usage ratio of each heat source in the heat recovery path, and dynamically adjusts the heat recovery efficiency of the heat source through the following innovative formula: ； in, For the Heat source at time Optimized heat recovery efficiency, For the Heat source at time The temperature difference, For the The flow rate of a heat source, For the Constants related to heat source characteristics is the target heat source temperature, is the target flow value, , For the The weight adjustment coefficient related to the temperature and flow changes of each heat source; S56, by optimizing the heat recovery efficiency , combined with real-time monitored load demand, dynamically adjust the working status of each heat source to keep the system heat transfer path in the optimal state during each working cycle, maximize heat recovery efficiency and optimize energy waste.

6. A multi-heat source coupled heat pump control method for recovering waste heat of industrial circulating cooling water according to claim 1, characterized in that: The S6 specifically includes: S61. Based on the real-time monitored heat source operation data, external load changes and system working status, the system collects and analyzes the temperature, flow, pressure, energy efficiency and environmental data from each heat source through an adaptive feedback mechanism, obtains the working status of each heat source in real time, and evaluates the adaptability of the current heat source to the system requirements; S62. After comprehensively analyzing the data collected in real time, the system uses an adaptive learning model to evaluate the impact of external load changes on the operation of the heat source, and dynamically adjusts the heat source usage strategy according to the current system workload and external demand forecast, including the priority of the heat source, the start-stop sequence, and the load distribution strategy, so that the heat source can be maximized during the working process; S63. After completing the optimization of the heat source use strategy, the system dynamically adjusts the heat source scheduling strategy based on the calculation results. The adjustment strategy is based on real-time load fluctuations, system requirements and heat source performance predictions to reasonably allocate the working time and usage ratio of each heat source. S64. After the scheduling strategy is adjusted, the system generates improvement suggestions based on the optimization results, transmits the feedback data to the intelligent learning module, and updates the adaptive model in real time, so that it can dynamically adjust the scheduling algorithm according to environmental fluctuations and load changes, and optimize the operation mode and load distribution of the heat source; S65. Based on real-time data feedback and external load fluctuations, the system continuously optimizes the scheduling and coupling efficiency of heat sources. By real-time monitoring of changes in external loads and the working status of internal heat sources, the system adjusts the operating sequence and load distribution of each heat source so that each heat source operates at the appropriate time and load conditions.

7. A multi-heat source coupled heat pump control method for recovering waste heat of industrial circulating cooling water according to claim 1, characterized in that: The S7 specifically includes: S71. Through the built-in intelligent learning module, the system collects historical operation data and real-time feedback information, monitors the operation status of each heat source in real time, including temperature, flow, pressure, and energy efficiency parameters, and analyzes the performance change trend of the heat source based on these data; S72. Based on historical data and real-time feedback, combined with external load changes and internal operating status, the system uses an adaptive learning model to predict future load fluctuations, evaluate the impact of load fluctuations on the performance of each heat source, and predict the efficiency and operating status of the heat source in the future; S73. Based on the predicted load fluctuations and heat source performance changes, the system dynamically optimizes the heat source coupling mode and heat recovery path. Combined with real-time data, the system adjusts the start and stop sequence, usage ratio and load distribution strategy of the heat sources to achieve collaborative work among the heat sources. The system also evaluates the overall optimization effect of the system in real time through the following formula: ； in, For the system at time The overall optimization efficiency, For the The workload of a heat source, is the total system load, For the Heat source at time Adjusted efficiency S74. In the process of optimizing the heat source coupling mode and the recovery path, the system feeds back the prediction results in real time, makes adjustments based on the following formula, and generates an optimization strategy for the heat source: ； in, For the Heat source at time The adjusted efficiency, , , For the The weighting coefficients associated with each heat source, For the Heat source at time The temperature, For the ideal working temperature, For the Heat source at time of traffic, is the average flow rate of all heat sources, is the external load at time The value of is the historical average value of external load, For the Heat source at time The power, is the optimal power value of the heat source, For the Power deviation adjustment factor for each heat source; S75. The system continuously makes adaptive adjustments through the intelligent learning module, optimizes the scheduling and coupling efficiency of the heat source in real time according to the working status of the heat source, changes in the external load and the energy efficiency requirements of the system, optimizes the adaptability, energy-saving effect and stability of the overall system, and enables the system to continue to operate in the optimal state under changing environments.

8. A multi-heat source coupled heat pump control system for recovering waste heat from industrial circulating cooling water, characterized in that: Includes the following modules: Environmental sensing module, used to monitor key parameters of multiple heat sources including cooling water waste heat, exhaust gas heat source, and ambient air in real time; The data processing unit screens and pre-processes the real-time monitoring data, and selects the most suitable heat source set for the current conditions according to the environmental conditions and load requirements; Adaptive learning model, used to evaluate the recovery potential of heat sources, combine historical data and real-time monitoring information to predict the efficiency of each heat source, and intelligently determine the order, proportion and priority of each heat source; The heat source time window allocation mechanism dynamically adjusts the heat source usage time window based on real-time monitoring of external load fluctuations and production needs, so that the heat source can be dynamically switched according to real-time load changes; Intelligent complementary regulation module, used to optimize the fluid dynamics state of the heat pump system, adjust the heat source coupling mode and operating parameters based on the heat source temperature gradient, flow difference and heat matching; Autonomous temperature control unit dynamically adjusts the heat recovery path in real time and optimizes the heat transfer efficiency of the heat source according to the temperature difference and flow changes; Adaptive feedback mechanism dynamically optimizes the heat source scheduling strategy and generates improvement suggestions based on heat source operation data, external load changes and system working status; The intelligent learning module predicts future load fluctuations based on historical operating data and real-time feedback, dynamically optimizes the heat source coupling mode and heat recovery path, and optimizes the energy-saving effect and stability of the system.

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