Intelligent regulation and control recovery method and system for waste heat of building electrical equipment

Abstract

The invention relates to the technical field of waste heat recycling, in particular to an intelligent regulation and control recycling method and system for building electrical equipment waste heat, and the method comprises the steps: monitoring the operation states of various electrical equipment in a building and the state parameters of a heat exchanger in real time, including pressure difference, temperature difference and fluid flow parameters; analyzing the dynamic change trend of the pressure difference based on the state parameters to judge whether the operation of the heat exchanger reaches a stable state or not; when it is judged that the waste heat is stable, waste heat grades are identified and graded according to the waste heat fluid temperature; and according to the grade grading result, the valves and the pump sets are dynamically regulated and controlled on the basis of a gradient utilization strategy in combination with real-time data of building energy consumption requirements, and waste heat of different grades is distributed to corresponding utilization terminals. Through multi-parameter intelligent monitoring, dynamic stability judgment and waste heat gradient utilization, accurate regulation and control of the recovery process are achieved, the energy utilization efficiency, system stability and economical efficiency are effectively improved, and energy waste and equipment loss are reduced.

Description

Technical Field

[0001] This invention relates to the technical field of waste heat recycling, and in particular to an intelligent control and recovery method and system for waste heat from building electrical equipment. Background Technology

[0002] With increasingly stringent building energy conservation requirements and rising energy costs, the value of recycling the large amount of waste heat generated during the operation of electrical equipment (such as central air conditioning units, transformers, and motors) in buildings is becoming increasingly prominent. Heat exchangers, as the core component of waste heat recovery systems, are widely used to transfer waste heat energy to building heating, domestic hot water, and other terminals, aiming to improve the overall energy efficiency of buildings.

[0003] However, existing waste heat recovery technologies for building electrical equipment still have many limitations in practical applications, resulting in low recovery efficiency and serious energy waste. Firstly, at the system control level, most existing technologies rely on simple threshold judgment and feedback control. For example, by monitoring a single parameter (such as temperature or pressure) at the inlet and outlet of the heat exchanger, the pump or regulating valve is started or stopped when it reaches a preset fixed threshold. This control method is too crude and cannot effectively distinguish between instantaneous fluctuations and the true steady state of the system. Due to the drastic dynamic changes in the load of building electrical equipment, the operating state of the heat exchanger fluctuates frequently. Control logic based on fixed thresholds easily leads to frequent system start-ups and shutdowns or control lags, which not only exacerbates equipment wear and shortens equipment lifespan but also prevents the heat recovery process from operating continuously and stably. A large amount of low-grade waste heat is directly emitted during system oscillations, resulting in energy waste.

[0004] Secondly, at the waste heat utilization level, existing solutions generally lack refined grade identification and tiered utilization mechanisms. Most systems treat recovered waste heat as a single-grade energy source, typically using it only for domestic hot water production. This "one-size-fits-all" approach fails to follow the principle of tiered energy utilization: "high-quality, high-use; low-quality, low-use." For example, high-quality waste heat above 80°C recovered from high-temperature equipment, which could be used to drive high-efficiency equipment such as absorption chillers, is instead used only for heating domestic hot water, resulting in a significant waste of energy grade. Meanwhile, low-grade waste heat below 40°C is difficult to utilize effectively due to the lack of suitable low-grade demand interfaces (such as building air preheating). This lack of differentiation in waste heat grade and the single-pathway utilization severely restricts the overall energy efficiency and economic benefits of waste heat recovery.

[0005] Furthermore, existing systems have significant shortcomings in adaptive optimization and safety assurance. The system's operating parameters (such as stability judgment range and flow setpoints) are typically fixed and cannot be dynamically adjusted according to the real-time load status (high, medium, and low load) of electrical equipment, resulting in poor energy efficiency under varying operating conditions. Simultaneously, the system lacks proactive fault prediction and intelligent diagnostic capabilities. When abnormalities such as scaling, blockage, or sensor failure occur in the heat exchanger, traditional systems often only trigger simple alarms or shutdowns after parameters severely exceed limits, failing to provide early warning, cause diagnosis, and guidance for recovery. This affects the system's reliability and maintainability; therefore, there is significant room for improvement in existing technologies. Summary of the Invention

[0006] To address the aforementioned technical problems, this application provides an intelligent control and recovery method and system for waste heat from building electrical equipment.

[0007] The above-mentioned objective of this application is achieved through the following technical solution:

[0008] A method for intelligent regulation and recovery of waste heat from building electrical equipment, comprising the following steps:

[0009] Real-time monitoring of the operating status of various electrical equipment in the building and the status parameters of the heat exchanger, wherein the status parameters include at least the pressure difference, temperature difference and fluid flow parameters between the inlet and outlet of the heat exchanger;

[0010] Based on the aforementioned state parameters, the dynamic trend of the pressure difference is analyzed to determine whether the heat exchanger has reached a stable operating state.

[0011] When the heat exchanger is determined to be operating stably, the waste heat grade is identified and classified based on the temperature of the waste heat fluid.

[0012] Based on the waste heat grade classification results and combined with real-time data on building energy demand, valves and pump sets are dynamically adjusted according to a preset tiered utilization strategy to distribute waste heat of different grades to the corresponding utilization terminals.

[0013] By adopting the above technical solutions, real-time monitoring of multi-source state parameters (such as pressure difference, temperature difference, and flow rate) provides a comprehensive and real-time operational data foundation for the system, ensuring the accuracy of subsequent analysis. Judging system stability based on the dynamic trend of pressure difference changes (such as the rate of change and trend direction) rather than a single instantaneous value effectively avoids false triggers caused by short-term fluctuations, improving the reliability of control decisions and extending equipment lifespan. Once the system stabilizes, precise grade identification and classification based on waste heat temperature lays the foundation for matching the most suitable utilization terminal for waste heat of different qualities. Finally, combining real-time building energy demand, the actuator is dynamically controlled based on a tiered utilization strategy, achieving "high-quality high-use and low-quality low-use" of waste heat energy. This significantly improves the overall efficiency of waste heat recovery and energy utilization rate, solving the problems of insufficient data utilization, lagging control, and energy grade mismatch in traditional methods.

[0014] In a preferred embodiment, this application can be further configured such that: the step of analyzing the dynamic change trend of the pressure difference based on the state parameters to determine whether the heat exchanger has reached a stable state specifically includes:

[0015] Continuous monitoring data of the pressure difference between the inlet and outlet of the heat exchanger within a preset first time window is obtained, the rate of change of the pressure difference within the first time window is calculated, and it is determined whether the absolute value of the rate of change is less than a first preset threshold.

[0016] Based on the rate of change, analyze the trend of pressure difference changes to determine whether it does not show a continuous unidirectional increase or a continuous unidirectional decrease.

[0017] When the absolute value of the rate of change of the pressure difference is less than the first preset threshold and the trend of change does not show a continuous unidirectional trend, it is determined that the operation of the heat exchanger has reached a stable state.

[0018] By adopting the above technical solution, continuous pressure difference monitoring data can be acquired and its rate of change calculated, which can quantify the dynamic characteristics of the system state and provide an objective basis for stability judgment. By analyzing whether the pressure difference change trend shows a continuous unidirectional trend (such as continuous increase or decrease), the transient process (such as start-up and shutdown) and steady-state operation of the system can be effectively distinguished, avoiding misjudging the transient state as a stable state. The system is finally determined to be stable only when the rate of change is extremely small and the trend is stable (i.e., the dual criteria are met). This dual verification mechanism based on dynamic trends greatly improves the accuracy and robustness of stability judgment, overcomes the shortcomings of traditional single threshold judgment method which is susceptible to noise interference and has a high misjudgment rate, and provides a reliable guarantee for the safe and efficient triggering of subsequent heat recovery operations.

[0019] In a preferred embodiment, this application can be further configured such that: when it is determined that the heat exchanger is operating stably, the waste heat grade is identified and classified based on the temperature of the waste heat fluid, specifically including:

[0020] The temperature data of the waste heat fluid at the outlet of the heat exchanger is collected in real time, and the collected waste heat fluid temperature is compared with the preset grade threshold range.

[0021] Based on the comparison results, the waste heat fluid is classified into corresponding grade levels, a unique path identifier is assigned to each grade level of waste heat, and a waste heat quality information table containing grade level, recommended utilization path and flow parameters is generated.

[0022] By adopting the above technical solution, the waste heat fluid temperature is collected in real time and compared quickly and accurately with preset grade threshold ranges (e.g., high temperature >80℃, medium temperature 40-80℃, low temperature <40℃), realizing automated identification and objective classification of waste heat grade, ensuring the accuracy and consistency of the classification results; a unique path identifier is assigned to each grade level, providing a clear direction for subsequent intelligent routing control and avoiding path confusion; a structured waste heat quality information table is generated, integrating key parameters such as grade, recommended path, and flow rate, providing complete and standardized input data for the dynamic control module, realizing standardized management of waste heat attribute information, thus providing solid data support for the principle of "high quality, high use; low quality, low use," and solving the problem of low energy utilization efficiency caused by ambiguous grade classification and arbitrary path selection in traditional methods.

[0023] In a preferred embodiment, this application can be further configured as follows: based on the waste heat grade classification results and combined with real-time data on building energy demand, and based on a preset tiered utilization strategy, dynamically controlling valves and pump sets to distribute waste heat of different grades to corresponding utilization terminals, specifically includes:

[0024] Real-time data on building energy demand is received, and the optimal allocation scheme for each grade of waste heat is determined by matching and analyzing the data with the waste heat parameters in the waste heat quality information table.

[0025] Based on the optimal allocation scheme, a sequence of actuator control commands for valves and variable frequency pumps is generated. In response to the sequence of control commands, the allocation path and flow rate of waste heat fluid are dynamically adjusted.

[0026] By adopting the above technical solution, and by receiving real-time building energy demand data (such as hot water demand and cooling load) and matching and analyzing it with waste heat quality information, the optimal allocation scheme for each grade of waste heat at a given moment can be dynamically calculated. This ensures real-time coupling between waste heat supply and user demand, avoiding energy waste or supply-demand imbalance. Based on the optimal scheme, a precise control command sequence is generated and drives actuators such as valves and pumps, realizing rapid and accurate adjustment of waste heat distribution paths and flow rates, enabling the system to respond sensitively to changes in demand. This dynamic control mechanism based on real-time data and optimization algorithms significantly improves the adaptability and intelligence level of system operation, achieving synergistic optimization between the waste heat recovery process and building energy demand. This maximizes energy utilization efficiency while meeting user needs, solving the problems of fixed strategies being unable to adapt to dynamic changes and insufficient control precision.

[0027] In a preferred embodiment, this application can be further configured as follows: after generating an actuator control command sequence for the valve and the variable frequency pump according to the optimal allocation scheme, and dynamically adjusting the allocation path and flow rate of the waste heat fluid in response to the control command sequence, the intelligent regulation and recovery method for waste heat from building electrical equipment further includes:

[0028] Real-time acquisition of load status data of building electrical equipment, and determination of equipment load status based on the load status data of building electrical equipment;

[0029] When electrical equipment is under high load, the stability judgment range of the pressure difference and temperature difference parameters of the heat exchanger should be expanded accordingly, and the upper limit of the flow parameter setting should be increased to quickly respond to the peak heat production.

[0030] When electrical equipment is under low load, the stability judgment range of the pressure difference and temperature difference parameters of the heat exchanger is reduced accordingly, and the lower limit of the flow parameter setting is lowered to improve the control accuracy and energy efficiency under low load.

[0031] By adopting the above technical solutions, the system can flexibly adapt to different operating conditions by acquiring the load status of electrical equipment in real time and adaptively adjusting the system operating parameters accordingly, thereby improving the system's intelligence. Under high load conditions, by expanding the stability judgment range and increasing the upper limit of flow, the system can tolerate greater parameter fluctuations and quickly absorb a large amount of waste heat, effectively avoiding system oscillations or waste heat emissions caused by overly sensitive regulation, and ensuring system stability and processing capacity during peak heat generation periods. Under low load conditions, by narrowing the judgment range and lowering the lower limit of flow, the precision and sensitivity of regulation are improved, unnecessary energy consumption is reduced, and energy efficiency under low load conditions is optimized. This load-adaptive parameter adjustment mechanism enhances the system's robustness and economy across the entire operating range, solving the problem of poor performance of fixed parameter strategies under different loads.

[0032] In a preferred embodiment, this application can be further configured as follows: after the waste heat grade classification results are combined with real-time data on building energy demand and a preset tiered utilization strategy are used to dynamically control valves and pump sets to distribute waste heat of different grades to corresponding utilization terminals, the intelligent control and recovery method for waste heat from building electrical equipment further includes:

[0033] A digital twin model of the system is constructed based on historical operating data and physical properties of heat exchangers and building heating systems;

[0034] Using the digital twin model, the system performance of various candidate control strategies is simulated under different waste heat production scenarios, different building energy demands, and different external environmental parameters.

[0035] With the goal of maximizing overall system energy efficiency and minimizing operating costs, the optimal control strategy is predicted in the near future by using an optimization algorithm to find the best among candidate strategies.

[0036] By adopting the above technical solutions and constructing a high-fidelity digital twin model, the operating characteristics of the physical system are reproduced in virtual space, providing a safe and efficient sandbox environment for strategy testing and optimization. Using this model to simulate the effects of different control strategies under various future scenarios (such as weather changes and demand fluctuations) allows for the forward-looking evaluation of the feasibility and performance of strategies, providing data support for decision-making. Multi-objective optimization, with the goals of maximizing comprehensive energy efficiency and minimizing operating costs, can find Pareto optimal or satisfactory solutions that balance economy and energy efficiency, generating scientific and reasonable optimal control strategies. This predictive optimization method based on digital twins elevates system control from passive response to active planning, significantly enhancing the system's decision-making ability and overall performance in complex and ever-changing environments, overcoming the control lag and energy efficiency losses caused by the reliance on experience and lack of foresight in traditional methods.

[0037] In a preferred embodiment, this application can be further configured as follows: after the waste heat grade classification results are combined with real-time data on building energy demand and a preset tiered utilization strategy are used to dynamically control valves and pump sets to distribute waste heat of different grades to corresponding utilization terminals, the intelligent control and recovery method for waste heat from building electrical equipment further includes:

[0038] Continuously monitor the temperature, pressure, and flow parameters at the inlet and outlet of the heat exchanger to determine if any parameter exceeds its preset safe operating range;

[0039] When a parameter is detected to be outside the safe operating range, a graded alarm is immediately triggered, and warnings, flow restrictions, or safety interlock shutdown commands are issued in sequence according to the severity of the exceedance.

[0040] Record system operation data before and after parameter anomalies occur, compare and analyze the data with historical fault data based on preset rules, make a preliminary diagnosis of the cause of the anomaly and generate a fault diagnosis report;

[0041] After troubleshooting and confirming safety, adjust the thresholds or control logic of relevant parameters based on the fault diagnosis report, and then guide the system to gradually return to a stable operating state.

[0042] By adopting the above technical solutions, and through continuous monitoring of key parameters and comparison with safe operating ranges, real-time and proactive perception of abnormal system states is achieved, enabling fault prevention and early intervention. Once parameter exceedances are detected, tiered alarms are immediately triggered, and corresponding control measures (such as early warning, flow control, and shutdown) are implemented. Differentiated responses are implemented based on risk levels, avoiding system interruptions caused by overreacting to minor issues and effectively preventing serious faults, thus improving system security and availability. Recording abnormal data and performing intelligent diagnosis can quickly locate the cause of faults, generate diagnostic reports, provide clear guidance for maintenance personnel, and shorten troubleshooting time. During the system recovery phase, parameters or logic are adaptively adjusted based on diagnostic results, and a gradual recovery strategy is adopted to ensure that the system can safely and smoothly return to normal operation. This forms a complete closed-loop management mechanism for fault handling and self-recovery, significantly improving system reliability and maintainability.

[0043] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0044] An intelligent control and recovery system for waste heat from building electrical equipment, the intelligent control and recovery system for waste heat from building electrical equipment includes:

[0045] The equipment status data acquisition module is used to monitor the operating status of various electrical equipment in the building and the status parameters of the heat exchanger in real time. The status parameters include at least the pressure difference, temperature difference and fluid flow parameters between the heat exchanger inlet and outlet.

[0046] The equipment stability judgment module is used to analyze the dynamic change trend of the pressure difference based on the state parameters to determine whether the heat exchanger has reached a stable state.

[0047] The waste heat identification and classification module is used to identify and classify the waste heat grade based on the temperature of the waste heat fluid when the heat exchanger is determined to be operating stably.

[0048] The waste heat distribution module is used to dynamically control valves and pump sets based on the waste heat grade classification results and real-time data on building energy demand, and based on a preset tiered utilization strategy, to distribute waste heat of different grades to the corresponding utilization terminals.

[0049] By adopting the above technical solutions, real-time monitoring of multi-source state parameters (such as pressure difference, temperature difference, and flow rate) provides a comprehensive and real-time operational data foundation for the system, ensuring the accuracy of subsequent analysis. Judging system stability based on the dynamic trend of pressure difference changes (such as the rate of change and trend direction) rather than a single instantaneous value effectively avoids false triggers caused by short-term fluctuations, improving the reliability of control decisions and extending equipment lifespan. Once the system stabilizes, precise grade identification and classification based on waste heat temperature lays the foundation for matching the most suitable utilization terminal for waste heat of different qualities. Finally, combining real-time building energy demand, the actuator is dynamically controlled based on a tiered utilization strategy, achieving "high-quality high-use and low-quality low-use" of waste heat energy. This significantly improves the overall efficiency of waste heat recovery and energy utilization rate, solving the problems of insufficient data utilization, lagging control, and energy grade mismatch in traditional methods.

[0050] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0051] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described intelligent control and recovery method for waste heat from building electrical equipment.

[0052] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0053] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described intelligent control and recovery method for waste heat from building electrical equipment.

[0054] In summary, this application includes at least one of the following beneficial technical effects:

[0055] 1. By monitoring multi-source status parameters (such as pressure difference, temperature difference, and flow rate) in real time, a comprehensive and real-time operational data foundation is provided for the system, ensuring the accuracy of subsequent analysis. System stability is judged based on the dynamic trend of pressure difference changes (such as the rate of change and trend direction) rather than a single instantaneous value, effectively avoiding false triggers caused by short-term fluctuations and improving the reliability of control decisions and equipment lifespan. Once the system stabilizes, precise grade identification and classification are performed based on waste heat temperature, laying the foundation for matching the most suitable utilization terminal for waste heat of different qualities. Finally, combined with real-time building energy demand, the actuator is dynamically controlled based on a tiered utilization strategy, achieving "high-quality high-use and low-quality low-use" of waste heat energy, significantly improving the overall efficiency and energy utilization rate of waste heat recovery, and solving the problems of insufficient data utilization, lagging control, and energy grade mismatch in traditional methods.

[0056] 2. By acquiring continuous pressure difference monitoring data and calculating its rate of change, the dynamic characteristics of the system state can be quantified, providing an objective basis for stability judgment. By analyzing whether the pressure difference change trend shows a continuous unidirectional trend (such as continuous increase or decrease), the transient process (such as start-up and shutdown) and steady-state operation of the system can be effectively distinguished, avoiding misjudging the transient state as a stable state. Only when the rate of change is extremely small and the trend is stable (i.e., the dual criteria are met) is the system finally determined to be stable. This dual verification mechanism based on dynamic trends greatly improves the accuracy and robustness of stability judgment, overcomes the shortcomings of the traditional single threshold judgment method which is susceptible to noise interference and has a high misjudgment rate, and provides a reliable guarantee for the safe and efficient triggering of subsequent heat recovery operations.

[0057] 3. By receiving real-time building energy demand data (such as hot water demand and cooling load) and matching and analyzing it with waste heat quality information, the system can dynamically calculate the optimal allocation scheme for waste heat of each grade at a given moment. This ensures real-time coupling between waste heat supply and user demand, avoiding energy waste or supply-demand imbalance. Based on the optimal scheme, a precise control command sequence is generated to drive valves, pumps, and other actuators, enabling rapid and accurate adjustment of waste heat distribution paths and flow rates. This allows the system to respond sensitively to changes in demand. This dynamic control mechanism based on real-time data and optimization algorithms significantly improves the adaptability and intelligence of the system, achieving coordinated optimization between the waste heat recovery process and building energy demand. This maximizes energy utilization efficiency while meeting user needs, solving the problems of fixed strategies being unable to adapt to dynamic changes and insufficient control precision.

[0058] 4. By acquiring the load status of electrical equipment in real time and adaptively adjusting system operating parameters accordingly, the control strategy can flexibly adapt to different operating conditions, improving the system's intelligence. Under high load conditions, by expanding the stability judgment range and increasing the upper limit of flow, the system can tolerate larger parameter fluctuations and quickly absorb a large amount of waste heat, effectively avoiding system oscillations or waste heat emissions caused by overly sensitive control, ensuring system stability and processing capacity during peak heat generation periods. Under low load conditions, by narrowing the judgment range and lowering the lower limit of flow, the precision and sensitivity of control are improved, unnecessary energy consumption is reduced, and energy efficiency under low load conditions is optimized. This load-adaptive parameter adjustment mechanism enhances the system's robustness and economy across the entire operating range, solving the problem of poor performance of fixed parameter strategies under different loads. Attached Figure Description

[0059] Figure 1 This is a flowchart of an intelligent regulation and recovery method for waste heat from building electrical equipment according to one embodiment of this application;

[0060] Figure 2This is a flowchart illustrating the implementation of step S20 in an embodiment of the intelligent control and recovery method for waste heat from building electrical equipment in this application.

[0061] Figure 3 This is a flowchart illustrating the implementation of step S30 in an embodiment of the intelligent control and recovery method for waste heat from building electrical equipment in this application.

[0062] Figure 4 This is a flowchart illustrating the implementation of step S40 in an embodiment of the intelligent control and recovery method for waste heat from building electrical equipment in this application.

[0063] Figure 5 This is another implementation flowchart of the intelligent regulation and recovery method for waste heat from building electrical equipment in one embodiment of this application;

[0064] Figure 6 This is another implementation flowchart of the intelligent regulation and recovery method for waste heat from building electrical equipment in one embodiment of this application;

[0065] Figure 7 This is a flowchart illustrating the implementation steps of the intelligent regulation and recovery method for waste heat from building electrical equipment in one embodiment of this application, including fault diagnosis and adaptive protection.

[0066] Figure 8 This is a schematic diagram of a smart control and recovery system for waste heat from building electrical equipment according to one embodiment of this application;

[0067] Figure 9 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0068] The present application will be further described in detail below with reference to the accompanying drawings.

[0069] In one embodiment, such as Figure 1 As shown, this application discloses an intelligent control and recovery method for waste heat from building electrical equipment, which specifically includes the following steps:

[0070] S10: Real-time monitoring of the operating status of various electrical equipment in the building and the status parameters of the heat exchanger, wherein the status parameters include at least the pressure difference, temperature difference and fluid flow parameters between the inlet and outlet of the heat exchanger.

[0071] In this embodiment, state parameters refer to key physical quantities reflecting the operating performance of the heat exchanger. Pressure difference (ΔP) represents the fluid pressure difference between the heat exchanger inlet and outlet, used to assess flow resistance; temperature difference (ΔT) represents the fluid temperature difference across the heat exchanger, used to measure heat transfer efficiency; and fluid flow rate parameter (Q) represents the volume of fluid passing through the heat exchanger per unit time, used to control the heat recovery rate. The purpose of monitoring these parameters is to capture system dynamics in real time, providing a data foundation for subsequent intelligent control.

[0072] Specifically, sensing units are deployed on the power supply circuits of major electrical equipment and the inlet and outlet pipes of heat exchangers within the building. For example, Hall effect current sensors are used to monitor the operating current of electrical equipment in real time, and instantaneous power is calculated in conjunction with voltage data to determine whether the equipment is under high, medium, or low load conditions. On the heat exchanger piping system, high-precision piezoresistive pressure sensors and PT100 platinum resistance temperature sensors are installed at the inlet and outlet, respectively, to synchronously collect pressure and temperature data at a sampling frequency of no less than 10Hz. The pressure difference (ΔP) and temperature difference (ΔT) are calculated in real time using the difference. Fluid flow parameters are measured using electromagnetic flowmeters. All sensor data is transmitted to the central processing unit in real time and timestamped to ensure data consistency. This multi-parameter synchronous monitoring mechanism solves the problems of single data and asynchronous timing in traditional systems, laying a solid foundation for subsequent accurate analysis.

[0073] S20: Based on the aforementioned state parameters, analyze the dynamic trend of the pressure difference to determine whether the heat exchanger has reached a stable operating state.

[0074] In this embodiment, steady-state determination refers to analyzing the changing trend of the pressure difference rather than a single instantaneous value to avoid the system erroneously triggering control due to brief fluctuations, thereby improving decision-making accuracy. The dynamic changing trend includes the rate of change of the pressure difference (such as derivative or slope) and the direction of change (such as continuous increase, decrease, or oscillation), ensuring that heat recovery is only performed when the system is in a steady state, thus reducing equipment wear.

[0075] Specifically, upon receiving continuous ΔP data, a configurable first time window (e.g., 30 seconds) is first set. Within this time window, the first derivative of ΔP (i.e., the rate of change) is calculated, and its absolute value is determined to be less than a preset first threshold (this threshold can be calibrated according to the system capacity, e.g., 0.01 kPa / s). Simultaneously, the change curve of ΔP over the entire time window is analyzed to determine whether it fluctuates slightly around a certain mean without exhibiting a continuous unidirectional upward or downward trend. Only when both conditions are met simultaneously—that is, the rate of change is extremely small and the fluctuation is stable—is the heat exchanger considered to have reached a stable state, and a "system stable" trigger signal is generated. This dual-criteria method based on dynamic trends, compared to the simple threshold comparison in existing technologies, effectively avoids misjudgments caused by instantaneous parameter fluctuations, significantly improving the stability and reliability of system control.

[0076] S30: When the heat exchanger is determined to be operating stably, the waste heat grade is identified and classified according to the temperature of the waste heat fluid.

[0077] In this embodiment, waste heat grade refers to the quality level of waste heat energy. Based on temperature classification, high-temperature waste heat (>80℃) has a high enthalpy value and is suitable for high-grade applications; medium-temperature waste heat (40-80℃) and low-temperature waste heat (<40℃) are matched with low-grade requirements in sequence, realizing the tiered principle of "high quality for high use and low quality for low use".

[0078] Specifically, upon receiving a "system stable" trigger signal, the system immediately reads the temperature sensor data located at the heat exchanger outlet to obtain the current temperature of the waste heat fluid. Subsequently, the collected temperature value is compared with a preset grade threshold range. For example, it can be set as follows: high-temperature grade (temperature > 80℃), suitable for driving absorption chillers or process heating; medium-temperature grade (40℃ ≤ temperature ≤ 80℃), suitable for preheating domestic hot water; low-temperature grade (temperature < 40℃), suitable for low-grade demand scenarios such as preheating building intake air. Based on the comparison results, the system classifies the current waste heat flow into the corresponding grade level and assigns a unique path identifier to each stream. Simultaneously, a structured waste heat quality information table is generated, which includes at least the grade level, recommended utilization path, current flow rate, and temperature. This automated identification and classification mechanism ensures that waste heat of different qualities can be accurately identified and guided to the most suitable utilization terminal, thereby maximizing energy utilization efficiency.

[0079] S40: Based on the waste heat grade classification results and combined with real-time data on building energy demand, and based on the preset tiered utilization strategy, dynamically adjust valves and pump sets to distribute waste heat of different grades to the corresponding utilization terminals.

[0080] In this embodiment, building energy demand data refers to real-time demand obtained from the building energy management system (BMS), such as hot water demand and heating / cooling intensity; the cascade utilization strategy is a set of rules based on grade classification and demand matching to ensure that waste heat is prioritized for high-value uses.

[0081] Specifically, the system first acquires real-time building energy demand data through the building energy management system (BMS) interface, such as domestic hot water tank levels, heating return water temperatures, or heat requests for specific processes. Next, the matching analysis module couples the parameters (such as grade, temperature, and flow rate) from the waste heat quality information table with the real-time energy demand. The analysis follows a priority principle of "high-quality, high-use; low-quality, low-use," while also considering the urgency of demand, calculating the optimal allocation scheme for waste heat of each grade at the current moment. For example, in winter when domestic hot water demand is high, medium-temperature waste heat is prioritized for routing to the domestic hot water system; while in summer when air conditioning load is high, high-temperature waste heat is prioritized for driving absorption chillers. Then, the control command generation module generates specific actuator control command sequences based on this optimal scheme, such as ordering the adjustment of the opening of electric regulating valves on specific pipelines or changing the operating frequency of variable frequency pumps to precisely control flow rate and path. The actuators respond to the commands, dynamically adjusting the distribution of waste heat fluid. This dynamic control strategy based on real-time demand ensures that the recovered waste heat can be consumed promptly and effectively, avoiding energy waste or system congestion, and achieving high efficiency and stability in the recovery process.

[0082] In this embodiment, by monitoring multi-source state parameters (such as pressure difference, temperature difference, and flow rate) in real time, a comprehensive and real-time operational data foundation is provided for the system, ensuring the accuracy of subsequent analysis. The system stability is judged based on the dynamic trend of pressure difference changes (such as the rate of change and trend direction) rather than a single instantaneous value, effectively avoiding false triggers caused by short-term fluctuations and improving the reliability of control decisions and the lifespan of equipment. Once the system stabilizes, precise grade identification and classification are performed based on waste heat temperature, laying the foundation for matching the most suitable utilization terminal for waste heat of different qualities. Finally, combined with real-time building energy demand, the actuator is dynamically controlled based on a tiered utilization strategy, achieving "high-quality high-use and low-quality low-use" of waste heat energy, significantly improving the overall efficiency of waste heat recovery and energy utilization rate, and solving the problems of insufficient data utilization, lagging control, and energy grade mismatch in traditional methods.

[0083] In one embodiment, such as Figure 2 As shown, in step S20, based on the state parameters, the dynamic trend of the pressure difference is analyzed to determine whether the heat exchanger has reached a stable state. This specifically includes:

[0084] S21: Obtain continuous monitoring data of the pressure difference between the inlet and outlet of the heat exchanger within a preset first time window, calculate the rate of change of the pressure difference within the first time window, and determine whether the absolute value of the rate of change is less than a first preset threshold.

[0085] In this embodiment, the first time window refers to the time interval used to analyze dynamic trends, for example, set to 30 seconds to ensure data continuity; the rate of change is calculated by numerical differentiation, and the first preset threshold is set according to historical data, for example, 0.1 kPa / s, to distinguish between stable and unstable states.

[0086] Specifically, the pressure difference sequence for the most recent 30 seconds is obtained from the data buffer, a linear trend line is fitted using the least squares method, and the rate of change (absolute value of the slope) is calculated. If the rate of change is less than 0.1 kPa / s, the next step is performed; otherwise, the system is deemed unstable, and the heat recovery operation is delayed.

[0087] S22: Analyze the changing trend of the pressure difference based on the rate of change, and determine whether it does not show a continuous unidirectional increase or a continuous unidirectional decrease.

[0088] In this embodiment, continuous unidirectionality refers to the pressure difference monotonically increasing or decreasing over time, indicating that the system is in a transitional state (such as startup or shutdown) rather than a steady state. The smooth trend is ensured by examining the second derivative or volatility of the pressure difference sequence.

[0089] Specifically, the standard deviation and autocorrelation coefficient of the pressure difference sequence are calculated. If the standard deviation is less than the threshold (e.g., 0.05 kPa) and the autocorrelation coefficient is close to zero, it indicates that there is no significant trend in the change. At the same time, the pressure difference is detected by a sliding window to see if it fluctuates around the mean (fluctuation amplitude < 5%). If both conditions are met, it is determined that there is no continuous unidirectionality.

[0090] S23: When the absolute value of the rate of change of the pressure difference is less than the first preset threshold and the change trend does not show a continuous unidirectional trend, it is determined that the operation of the heat exchanger has reached a stable state.

[0091] In this embodiment, the determination of a stable state is a prerequisite for triggering subsequent heat recovery operations, ensuring that the system operates under safe and efficient conditions.

[0092] Specifically, when conditions S21 and S22 are met simultaneously, a "system stable" signal is generated and the waste heat grade identification module is activated; otherwise, monitoring continues and the above steps are repeated.

[0093] In one embodiment, such as Figure 3 As shown, in step S30, when it is determined that the heat exchanger is operating stably, the waste heat grade is identified and classified according to the temperature of the waste heat fluid, specifically including:

[0094] S31: Real-time acquisition of waste heat fluid temperature data at the heat exchanger outlet, and comparison of the acquired waste heat fluid temperature with the preset grade threshold range.

[0095] In this embodiment, the grade threshold range is a temperature range preset according to thermodynamic theory and application scenario, such as a high temperature threshold of 80°C and a medium temperature threshold of 40°C. Data is collected by a calibrated infrared temperature sensor with an accuracy of ±0.5°C.

[0096] Specifically, a temperature sensor is used to continuously measure the temperature of the waste heat fluid, sampling once per second and comparing it with a threshold range in real time: if the temperature is >80℃, it is classified as high-temperature grade; if the temperature is 40℃≤temperature≤80℃, it is classified as medium-temperature grade; if the temperature is <40℃, it is classified as low-temperature grade.

[0097] S32: Based on the comparison results, the waste heat fluid is classified into the corresponding grade level, a unique path identifier is assigned to each grade level of waste heat, and a waste heat quality information table containing grade level, recommended utilization path and flow parameters is generated.

[0098] In this embodiment, the path identifier is a digital tag used for routing control (e.g., high byte = 1, middle byte = 2, low byte = 3), and the waste heat quality information table is a structured data table containing information such as grade, recommended utilization path (e.g., high temperature driven chiller), and maximum allowable flow rate.

[0099] Specifically, based on the grade classification results, an identifier is assigned to each grade, and a JSON-formatted information table is generated. For example, the high-temperature grade corresponds to path identifier 1, with the recommended path being "absorption refrigeration" and a flow rate limit of 50L / min; the medium-temperature grade corresponds to path 2, with the recommended path being "domestic hot water system" and a flow rate limit of 30L / min. This table is stored in memory for the dynamic control module to access.

[0100] In one embodiment, such as Figure 4 As shown, in step S40, based on the waste heat grade classification results and combined with real-time data on building energy demand, and based on a preset tiered utilization strategy, valves and pump sets are dynamically adjusted to distribute waste heat of different grades to corresponding utilization terminals. Specifically, this includes:

[0101] S41: Receive building energy demand data in real time, perform matching analysis based on the building energy demand data and the waste heat parameters in the waste heat quality information table, and determine the optimal allocation scheme for waste heat of each grade.

[0102] In this embodiment, matching analysis refers to calculating waste heat allocation schemes through optimization algorithms (such as linear programming) to maximize energy efficiency or minimize costs.

[0103] Specifically, real-time demand data (e.g., domestic hot water demand of 100L / h) is obtained from the BMS interface and matched with parameters in the waste heat quality information table (e.g., available flow rate, grade). A greedy algorithm is used to prioritize the allocation of high-temperature waste heat to cooling demand, medium-temperature waste heat to hot water demand, and the remaining low-temperature waste heat for heating. The allocation scheme is output in the form of a weight matrix, for example, [high temperature weight: 0.7, medium temperature weight: 0.2, low temperature weight: 0.1].

[0104] S42: Based on the optimal allocation scheme, generate an actuator control command sequence for the valve and the variable frequency water pump, and dynamically adjust the allocation path and flow rate of the waste heat fluid in response to the control command sequence.

[0105] In this embodiment, the actuator control command sequence is a set of digital signals used to drive the electric valve and the variable frequency water pump to achieve path switching and flow regulation.

[0106] Specifically, based on the allocation scheme, a sequence of instructions that can be executed by the PLC is generated: for example, opening the high-temperature path valve (opening degree 80%), adjusting the frequency of the variable frequency water pump to 40Hz to control the flow rate; the instructions are sent to the actuator via the Modbus protocol, and the flow feedback is monitored in real time to ensure that the error between the actual flow rate and the set value is <5%.

[0107] In one embodiment, such as Figure 5 As shown, after step S42, that is, after generating the actuator control command sequence for the valve and variable frequency water pump according to the optimal allocation scheme, and dynamically adjusting the allocation path and flow rate of the waste heat fluid in response to the control command sequence, the intelligent regulation and recovery method for waste heat from building electrical equipment further includes:

[0108] S421: Acquire the load status data of building electrical equipment in real time, and determine the equipment load status based on the load status data of building electrical equipment.

[0109] In this embodiment, the equipment load status refers to the operating intensity of electrical equipment, which is divided into high, medium and low loads. It is determined based on parameters such as current and power, and is used to adaptively adjust system parameters.

[0110] Specifically, smart meters collect current and power data from electrical equipment, and load status is categorized based on power percentage: >80% of rated power is high load, 40%-80% is normal load, and <40% is low load. Status data is updated every 10 seconds.

[0111] S422: When electrical equipment is under high load, the stability judgment range of the pressure difference and temperature difference parameters of the heat exchanger is expanded accordingly, and the upper limit of the flow parameter setting is increased to quickly respond to the peak heat generation.

[0112] In this embodiment, the parameter range is adjusted to avoid frequent adjustments due to fluctuations under high load, thereby improving system robustness.

[0113] Specifically, under high load conditions, the pressure difference stability judgment range is expanded from [0.5kPa, 1.5kPa] to [0.3kPa, 2.0kPa], the temperature difference range is expanded from [5℃, 15℃] to [3℃, 20℃], and the upper limit of flow rate is increased from 50L / min to 70L / min to ensure rapid absorption of waste heat.

[0114] S423: When electrical equipment is under low load, the stability judgment range of the pressure difference and temperature difference parameters of the heat exchanger is reduced accordingly, and the lower limit of the flow parameter setting is lowered to improve the control accuracy and energy efficiency under low load.

[0115] In this embodiment, narrowing the range can improve control accuracy and reduce energy waste.

[0116] Specifically, under low load, the pressure difference range is reduced to [0.8kPa, 1.2kPa], the temperature difference range is reduced to [8℃, 12℃], the lower limit of flow rate is reduced from 10L / min to 5L / min, and the valve opening is finely adjusted by the PID controller to maintain efficient operation.

[0117] In one embodiment, such as Figure 6 As shown, after step S40, that is, based on the waste heat grade classification results and combined with real-time data on building energy demand, and based on a preset tiered utilization strategy, the valves and pump sets are dynamically adjusted to distribute waste heat of different grades to the corresponding utilization terminals. The intelligent control and recovery method for waste heat from building electrical equipment also includes:

[0118] S50: A digital twin model of the system is constructed based on historical operating data and physical properties of heat exchangers and building heating systems.

[0119] In this embodiment, the digital twin model is a virtual mapping of the physical system, built based on historical data (such as the operation logs of the past 30 days) and physical properties (such as heat exchanger material and pipe size), and is used for simulation and prediction.

[0120] Specifically, the model is trained using machine learning algorithms (such as neural networks), with inputs including historical pressure difference, temperature difference, flow rate and external temperature, and outputting the predicted heat recovery efficiency; the model parameters are optimized through backpropagation, achieving a fitting accuracy of 95%.

[0121] S60: Using the digital twin model, simulate the system performance of various candidate control strategies under different waste heat production scenarios, different building energy demands, and different external environmental parameters.

[0122] In this embodiment, simulation analysis refers to testing various control strategies (such as different valve opening combinations) using a digital twin model to predict energy efficiency indicators (such as COP).

[0123] Specifically, set up simulation scenarios: for example, when there is high cooling demand in summer, test the distribution of all high-temperature waste heat to the chiller; the model outputs the predicted COP and cost of each strategy, and the data is stored in the simulation database.

[0124] S70: With the goal of maximizing the overall energy efficiency of the system and minimizing the operating cost, it uses an optimization algorithm to find the best among candidate strategies and predict the optimal control strategy in the near future.

[0125] In this embodiment, multi-objective optimization uses a genetic algorithm or a particle swarm optimization algorithm to balance energy efficiency and economic benefits.

[0126] Specifically, the objective function is defined as a weighted sum of maximizing COP and minimizing power consumption. The optimal strategy (such as valve opening of 60% and pump frequency of 35Hz) is obtained through iterative optimization and preloaded into the real-time system.

[0127] In one embodiment, such as Figure 7 As shown, after step S40, that is, based on the waste heat grade classification results and combined with real-time data on building energy demand, and based on a preset tiered utilization strategy, the valves and pump sets are dynamically adjusted to distribute waste heat of different grades to the corresponding utilization terminals. The intelligent control and recovery method for waste heat from building electrical equipment also includes:

[0128] S401: Continuously monitor the temperature, pressure, and flow parameters at the inlet and outlet of the heat exchanger to determine if any parameter exceeds its preset safe operating range.

[0129] In this embodiment, the safe operating range is the limit value set according to the equipment specifications. For example, the safe range for pressure difference is [0.2kPa, 2.5kPa], and the safe range for temperature difference is [2℃, 25℃], which is used for fault prevention.

[0130] Specifically, compare real-time parameters with the safety range: if the pressure difference is >2.5kPa, trigger an alarm; the monitoring data is verified once per second.

[0131] S402: When a parameter is detected to be outside the safe operating range, a graded alarm is immediately triggered, and warnings, flow restrictions, or safety interlock shutdown commands are issued in sequence according to the severity of the exceedance.

[0132] In this embodiment, the tiered alarm includes early warning (audio-visual alert), flow restriction (reducing traffic by 50%), and shutdown (power off), with the severity determined based on the percentage of exceeding the limit.

[0133] Specifically, if a parameter exceeds the limit by 10%, an early warning will be issued; if the parameter exceeds the limit by 10%-30%, the current will be limited; if the parameter exceeds the limit by more than 30%, the system will be shut down. The instructions are executed by the PLC and the event log is recorded.

[0134] S403: Records system operation data before and after parameter anomalies, compares and analyzes the data with historical fault data based on preset rules, preliminarily diagnoses the cause of the anomaly, and generates a fault diagnosis report.

[0135] In this embodiment, fault diagnosis is achieved through a rule engine (such as a decision tree) by comparing historical fault patterns (such as pump blockage or sensor failure).

[0136] Specifically, data from 5 minutes before and after the anomaly is extracted and compared with the historical database: if a sudden increase in pressure difference is accompanied by a decrease in flow rate, it is diagnosed as a pipe blockage; the report is generated in PDF format, including the cause and recommended measures.

[0137] S404: After troubleshooting and confirming safety in the system, adjust the thresholds or control logic of relevant parameters based on the fault diagnosis report, and then guide the system to gradually return to a stable operating state.

[0138] Specifically, the safety range was adjusted based on the report (e.g., the upper limit of the pressure difference was reduced to 2.2 kPa), and the system was guided to recover by gradually increasing the flow rate (increasing by 10% each step), with stability monitored throughout the process.

[0139] It should be understood that the sequence number of each step in the above embodiments does not imply 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 this application.

[0140] In one embodiment, an intelligent control and recovery system for waste heat from building electrical equipment is provided, which corresponds one-to-one with the intelligent control and recovery method for waste heat from building electrical equipment in the above embodiments. For example... Figure 8 As shown, the intelligent control and recovery system for waste heat from building electrical equipment includes an equipment status data acquisition module, an equipment stability judgment module, a waste heat identification and classification module, and a waste heat distribution module. Detailed descriptions of each functional module are as follows:

[0141] The equipment status data acquisition module is used to monitor the operating status of various electrical equipment in the building and the status parameters of the heat exchanger in real time. The status parameters include at least the pressure difference, temperature difference and fluid flow parameters between the heat exchanger inlet and outlet.

[0142] The equipment stability judgment module is used to analyze the dynamic change trend of the pressure difference based on the state parameters to determine whether the heat exchanger has reached a stable state.

[0143] The waste heat identification and classification module is used to identify and classify the waste heat grade based on the temperature of the waste heat fluid when the heat exchanger is determined to be operating stably.

[0144] The waste heat distribution module is used to dynamically control valves and pump sets based on the waste heat grade classification results and real-time data on building energy demand, and based on a preset tiered utilization strategy, to distribute waste heat of different grades to the corresponding utilization terminals.

[0145] Specific limitations regarding the intelligent control and recovery system for waste heat from building electrical equipment can be found in the above description of the intelligent control and recovery method for waste heat from building electrical equipment, and will not be repeated here. Each module in the aforementioned intelligent control and recovery system for waste heat from building electrical equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the electronic device, or stored in the memory of the electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0146] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores outbound and dispatch data. The network interface communicates with external terminals via a network. When executed by the processor, the computer program implements an intelligent control and recovery method for waste heat from building electrical equipment.

[0147] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0148] Real-time monitoring of the operating status of various electrical equipment in the building and the status parameters of the heat exchanger, wherein the status parameters include at least the pressure difference, temperature difference and fluid flow parameters between the inlet and outlet of the heat exchanger;

[0149] Based on the aforementioned state parameters, the dynamic trend of the pressure difference is analyzed to determine whether the heat exchanger has reached a stable operating state.

[0150] When the heat exchanger is determined to be operating stably, the waste heat grade is identified and classified based on the temperature of the waste heat fluid.

[0151] Based on the waste heat grade classification results and combined with real-time data on building energy demand, valves and pump sets are dynamically adjusted according to a preset tiered utilization strategy to distribute waste heat of different grades to the corresponding utilization terminals.

[0152] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0153] Real-time monitoring of the operating status of various electrical equipment in the building and the status parameters of the heat exchanger, wherein the status parameters include at least the pressure difference, temperature difference and fluid flow parameters between the inlet and outlet of the heat exchanger;

[0154] Based on the aforementioned state parameters, the dynamic trend of the pressure difference is analyzed to determine whether the heat exchanger has reached a stable operating state.

[0155] When the heat exchanger is determined to be operating stably, the waste heat grade is identified and classified based on the temperature of the waste heat fluid.

[0156] Based on the waste heat grade classification results and combined with real-time data on building energy demand, valves and pump sets are dynamically adjusted according to a preset tiered utilization strategy to distribute waste heat of different grades to the corresponding utilization terminals.

[0157] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0158] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0159] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for intelligent regulation and recovery of waste heat from building electrical equipment, characterized in that, The intelligent regulation and recovery method for waste heat from building electrical equipment includes the following steps: Real-time monitoring of the operating status of various electrical equipment in the building and the status parameters of the heat exchanger, wherein the status parameters include at least the pressure difference, temperature difference and fluid flow parameters between the inlet and outlet of the heat exchanger; Based on the aforementioned state parameters, the dynamic trend of the pressure difference is analyzed to determine whether the heat exchanger has reached a stable operating state. When the heat exchanger is determined to be operating stably, the waste heat grade is identified and classified based on the temperature of the waste heat fluid. Based on the waste heat grade classification results and combined with real-time data on building energy demand, valves and pump sets are dynamically adjusted according to a preset tiered utilization strategy to distribute waste heat of different grades to the corresponding utilization terminals.

2. The intelligent regulation and recovery method for waste heat from building electrical equipment according to claim 1, characterized in that, The step of analyzing the dynamic trend of the pressure difference based on the state parameters to determine whether the heat exchanger has reached a stable state specifically includes: Continuous monitoring data of the pressure difference between the inlet and outlet of the heat exchanger within a preset first time window is obtained, the rate of change of the pressure difference within the first time window is calculated, and it is determined whether the absolute value of the rate of change is less than a first preset threshold. Based on the rate of change, analyze the trend of pressure difference changes to determine whether it does not show a continuous unidirectional increase or a continuous unidirectional decrease. When the absolute value of the rate of change of the pressure difference is less than the first preset threshold and the trend of change does not show a continuous unidirectional trend, it is determined that the operation of the heat exchanger has reached a stable state.

3. The intelligent regulation and recovery method for waste heat from building electrical equipment according to claim 1, characterized in that, When the heat exchanger is determined to be operating stably, the waste heat grade is identified and classified based on the temperature of the waste heat fluid, specifically including: The temperature data of the waste heat fluid at the outlet of the heat exchanger is collected in real time, and the collected waste heat fluid temperature is compared with the preset grade threshold range. Based on the comparison results, the waste heat fluid is classified into corresponding grade levels, a unique path identifier is assigned to each grade level of waste heat, and a waste heat quality information table containing grade level, recommended utilization path and flow parameters is generated.

4. The intelligent regulation and recovery method for waste heat from building electrical equipment according to claim 1, characterized in that, Based on the waste heat grade classification results and combined with real-time data on building energy demand, and based on a preset tiered utilization strategy, valves and pump sets are dynamically adjusted to distribute waste heat of different grades to corresponding utilization terminals. Specifically, this includes: Real-time data on building energy demand is received, and the optimal allocation scheme for each grade of waste heat is determined by matching and analyzing the data with the waste heat parameters in the waste heat quality information table. Based on the optimal allocation scheme, a sequence of actuator control commands for valves and variable frequency pumps is generated. In response to the sequence of control commands, the allocation path and flow rate of waste heat fluid are dynamically adjusted.

5. The intelligent regulation and recovery method for waste heat from building electrical equipment according to claim 4, characterized in that, After generating a sequence of actuator control commands for valves and variable frequency pumps according to the optimal allocation scheme, and dynamically adjusting the allocation path and flow rate of waste heat fluid in response to the control command sequence, the intelligent regulation and recovery method for waste heat from building electrical equipment further includes: Real-time acquisition of load status data of building electrical equipment, and determination of equipment load status based on the load status data of building electrical equipment; When electrical equipment is under high load, the stability judgment range of the pressure difference and temperature difference parameters of the heat exchanger should be expanded accordingly, and the upper limit of the flow parameter setting should be increased to quickly respond to the peak heat production. When electrical equipment is under low load, the stability judgment range of the pressure difference and temperature difference parameters of the heat exchanger is reduced accordingly, and the lower limit of the flow parameter setting is lowered to improve the control accuracy and energy efficiency under low load.

6. The intelligent regulation and recovery method for waste heat from building electrical equipment according to claim 1, characterized in that, After the method for intelligent regulation and recovery of waste heat from building electrical equipment, based on the waste heat grade classification results, combined with real-time data on building energy demand, and a preset tiered utilization strategy, dynamically controls valves and pump sets to distribute waste heat of different grades to corresponding utilization terminals, the method further includes: A digital twin model of the system is constructed based on historical operating data and physical properties of heat exchangers and building heating systems; Using the digital twin model, the system performance of various candidate control strategies is simulated under different waste heat production scenarios, different building energy demands, and different external environmental parameters. With the goal of maximizing overall system energy efficiency and minimizing operating costs, the optimal control strategy is predicted in the near future by using an optimization algorithm to find the best among candidate strategies.

7. The intelligent regulation and recovery method for waste heat from building electrical equipment according to claim 1, characterized in that, After the method for intelligent regulation and recovery of waste heat from building electrical equipment, based on the waste heat grade classification results, combined with real-time data on building energy demand, and a preset tiered utilization strategy, dynamically controls valves and pump sets to distribute waste heat of different grades to corresponding utilization terminals, the method further includes: Continuously monitor the temperature, pressure, and flow parameters at the inlet and outlet of the heat exchanger to determine if any parameter exceeds its preset safe operating range; When a parameter is detected to be outside the safe operating range, a graded alarm is immediately triggered, and warnings, flow restrictions, or safety interlock shutdown commands are issued in sequence according to the severity of the exceedance. Record system operation data before and after parameter anomalies occur, compare and analyze the data with historical fault data based on preset rules, make a preliminary diagnosis of the cause of the anomaly and generate a fault diagnosis report; After troubleshooting and confirming safety, adjust the thresholds or control logic of relevant parameters based on the fault diagnosis report, and then guide the system to gradually return to a stable operating state.

8. An intelligent control and recovery system for waste heat from building electrical equipment, characterized in that, The intelligent control and recovery system for waste heat from building electrical equipment includes: The equipment status data acquisition module is used to monitor the operating status of various electrical equipment in the building and the status parameters of the heat exchanger in real time. The status parameters include at least the pressure difference, temperature difference and fluid flow parameters between the heat exchanger inlet and outlet. The equipment stability judgment module is used to analyze the dynamic change trend of the pressure difference based on the state parameters to determine whether the heat exchanger has reached a stable state. The waste heat identification and classification module is used to identify and classify the waste heat grade based on the temperature of the waste heat fluid when the heat exchanger is determined to be operating stably. The waste heat distribution module is used to dynamically control valves and pump sets based on the waste heat grade classification results and real-time data on building energy demand, and based on a preset tiered utilization strategy, to distribute waste heat of different grades to the corresponding utilization terminals.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent control and recovery method for waste heat from building electrical equipment as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent control and recovery method for waste heat from building electrical equipment as described in any one of claims 1 to 7.

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