Method and system for switching state of boiler heat recovery heat pump in conjunction with environmental data

Abstract

The application discloses a state switching method and system of a boiler heat recovery heat pump combined with environmental data, and relates to the technical field of intelligent control. The method comprises the following steps: collecting environmental data in a boiler system and external environmental data in real time; activating a multi-channel research and judgment component in parallel to perform state switching research and judgment according to the collected environmental data, and obtaining a state instruction set, wherein the state instruction set comprises at least one of a degraded state instruction, a dry burning descaling state instruction and a defrosting state instruction; performing state competition arbitration on the state instruction set in combination with a preset priority control constraint to determine a state switching timing instruction; and applying the state switching timing instruction to an actuator of the heat pump to switch the working state of the heat pump. The technical effect of improving the accuracy and timeliness of state control and improving the stability and economy of the system is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, and particularly relates to a state switching method and system of a boiler heat recovery heat pump combined with environmental data. BACKGROUND

[0002] In the boiler heat recovery scene, the traditional heat pump control system has obvious limitations. The existing control mechanism is mostly based on fixed threshold discrimination post-processing, lacks early prediction function, and lacks consideration of external influencing factors, resulting in system response lag and low running efficiency. The working state of the heat pump cannot be accurately and timely switched, affecting the stability of the heat pump operation. SUMMARY

[0003] The present application provides a state switching method and system of a boiler heat recovery heat pump combined with environmental data to solve the technical problem of poor flexibility and predictability in the prior art, affecting the accuracy of state switching, and to achieve the technical effect of improving the accuracy and timeliness of state control, and improving the stability and economy of the system.

[0004] In a first aspect, the present application provides a state switching method of a boiler heat recovery heat pump combined with environmental data, wherein the state switching method of the boiler heat recovery heat pump combined with environmental data comprises:

[0005] Real-time collection of environmental data and external environmental data in the boiler system.

[0006] According to the collected environmental data, a plurality of channel research and judgment components are activated in parallel to perform state switching research and judgment to obtain a state instruction set, wherein the state instruction set includes at least one of a degraded state instruction, a dry burning descaling state instruction, and a defrosting state instruction, and the plurality of channel research and judgment components at least include a first research and judgment channel for degraded switching research and judgment, a second research and judgment channel for dry burning descaling switching research and judgment, and a third research and judgment channel for defrosting switching research and judgment.

[0007] In combination with a preset priority control constraint, state competition arbitration is performed on the state instruction set to determine the execution order of a plurality of state instructions in the state instruction set, and a state switching timing instruction is correspondingly generated, wherein the priority control constraint is used to define the priority of a plurality of state instructions.

[0008] The state switching timing instruction is applied to the actuator of the heat pump to switch the working state of the heat pump.

[0009] In a feasible implementation manner, real-time collection of environmental data in the boiler system comprises:

[0010] Real-time reading of boiler flue gas component concentration through a sensor array installed on the boiler flue, wherein the boiler flue gas component concentration at least includes the concentration value of sulfur oxide in the flue gas, the concentration value of nitrogen oxide, the humidity value and the flue gas temperature.

[0011] Real-time reading of heat pump fluid data through a sensor installed in the heat pump system, wherein the heat pump fluid data at least includes the inlet and outlet air path static pressure differential value of the flow through the evaporator, the water inlet temperature and the water outlet temperature of the flow through the evaporator, and the water volume flow through the evaporator.

[0012] Real-time hardness value of circulating water is read through an online water quality sensor.

[0013] In a feasible implementation, the external environment data at least includes the cumulative running time of the heat pump read from the system clock and the real-time electricity price signal obtained from the power grid interface, wherein the real-time electricity price signal includes peak, flat and valley.

[0014] In a feasible implementation, the multi-channel research and judgment component includes a first research and judgment channel, which is used for:

[0015] Real-time calculation of acid dew point temperature based on boiler flue gas component concentration.

[0016] Obtain the running parameter sequence of the heat pump system, and determine the predicted minimum wall surface temperature of the evaporator and the corresponding most unfavorable temperature position based on the running parameter sequence and the pre-constructed twin simulation model.

[0017] In combination with the dynamic safety margin, it is calculated and judged whether the minimum wall surface temperature is less than the sum of the acid dew point temperature and the dynamic safety margin, wherein the dynamic safety margin is determined according to historical running data and environmental fluctuation characteristics.

[0018] If the determination result is yes, a degradation state instruction is generated. If the determination result is no, no instruction is output.

[0019] In a feasible implementation, the multi-channel research and judgment component further includes a second research and judgment channel, which is used for:

[0020] Based on the heat pump fluid data, the real-time heat exchange amount and the real-time heat exchange coefficient are calculated.

[0021] Based on the historical running data of the heat pump system and the preset confidence threshold, the reference heat exchange coefficient is determined, and the real-time relative heat exchange efficiency is calculated correspondingly, and the performance attenuation degree is output.

[0022] In combination with the self-learning optimization weight, the real-time hardness value, the performance attenuation degree and the cumulative running time of the heat pump are weighted to obtain the scale removal demand index.

[0023] According to the performance degradation degree configuration confidence constraint, a dynamic index threshold of the descaling demand index in a preset time window is determined by combining a statistical analysis method.

[0024] If the descaling demand index is greater than the dynamic index threshold and the real-time electricity price signal is valley, a dry burning descaling state instruction is generated, otherwise no instruction is output.

[0025] In a feasible implementation manner, the multi-channel judgment component further comprises a third judgment channel, configured to:

[0026] The air path static pressure differential value is differentiated to obtain a differential pressure change rate.

[0027] It is determined whether the differential pressure change rate is greater than a preset positive threshold for a continuous preset number of times, and if the determination result is yes, a defrosting state instruction is generated.

[0028] If the determination result is no, no instruction is output.

[0029] In a feasible implementation manner, according to the performance degradation degree configuration confidence constraint, a dynamic index threshold of the descaling demand index in a preset time window is determined by combining a statistical analysis method, comprising:

[0030] Based on the preset time window, a historical descaling demand index set is obtained, and a historical index mean and a historical index standard deviation of the historical descaling demand index set are statistically analyzed.

[0031] By combining a preset mapping rule, a confidence level corresponding to the performance degradation degree is matched and obtained.

[0032] A Z value corresponding to the confidence level is used as a correction coefficient of the historical index standard deviation, and the historical index mean and the historical index standard deviation are weighted to obtain the dynamic index threshold.

[0033] In a feasible implementation manner, the priority control constraint comprises an execution sequence constraint of the instruction and an execution interval constraint when sequentially executed.

[0034] In a second aspect, the present application further provides a state switching system of a boiler heat recovery heat pump combined with environmental data, wherein the state switching system of the boiler heat recovery heat pump combined with environmental data comprises:

[0035] An environmental data acquisition module is configured to acquire environmental data in a boiler system and external environmental data in real time.

[0036] The state switching research and judgment module is configured to activate the multi-channel research and judgment assembly in parallel to research and judge state switching according to the collected environmental data, and obtain a state instruction set, wherein the state instruction set comprises at least one of a degraded state instruction, a dry burning and descaling state instruction, and a defrosting state instruction, and the multi-channel research and judgment assembly comprises at least a first research and judgment channel for degraded state switching, a second research and judgment channel for dry burning and descaling state switching, and a third research and judgment channel for defrosting state switching which are arranged in parallel.

[0037] The state competition arbitration module is configured to perform state competition arbitration on the state instruction set in combination with a preset priority control constraint, determine the execution sequence of the plurality of state instructions in the state instruction set, and correspondingly generate a state switching timing instruction, wherein the priority control constraint is used to define the priority of the plurality of state instructions.

[0038] The heat pump working state switching module is configured to apply the state switching timing instruction to the actuator of the heat pump to switch the working state of the heat pump.

[0039] The application discloses a state switching method and system of a boiler heat recovery heat pump combined with environmental data, which comprises the following steps: collecting environmental data in and outside a boiler system in real time; activating a multi-channel research and judgment assembly in parallel to generate a state instruction set, which comprises degraded state, dry burning and descaling, or defrosting instructions; performing arbitration in combination with a priority constraint to determine a state switching timing instruction; and applying the timing instruction to a heat pump actuator to realize working state switching. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The application discloses a state switching method and system of a boiler heat recovery heat pump combined with environmental data, which comprises the following steps: collecting environmental data in and outside a boiler system in real time; activating a multi-channel research and judgment assembly in parallel to generate a state instruction set, which comprises degraded state, dry burning and descaling, or defrosting instructions; performing arbitration in combination with a priority constraint to determine a state switching timing instruction; and applying the timing instruction to a heat pump actuator to realize working state switching.

[0041] Figure 2 The application discloses a state switching method and system of a boiler heat recovery heat pump combined with environmental data, which comprises the following steps: collecting environmental data in and outside a boiler system in real time; activating a multi-channel research and judgment assembly in parallel to generate a state instruction set, which comprises degraded state, dry burning and descaling, or defrosting instructions; performing arbitration in combination with a priority constraint to determine a state switching timing instruction; and applying the timing instruction to a heat pump actuator to realize working state switching.

[0042] In the drawings, the components represented by the numbers are described as follows: an environmental data collection module 11, a state switching research and judgment module 12, a state competition arbitration module 13, and a heat pump working state switching module 14. DETAILED DESCRIPTION

[0043] The above technical solutions will be described in detail below in combination with the accompanying drawings and specific embodiments, so that the above technical solutions can be better understood. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments for explaining the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings, not all.

[0044] In one embodiment, the state switching method of the boiler heat recovery heat pump combined with environmental data comprises: Figure 1 The flowchart of the state switching method of the boiler heat recovery heat pump combined with environmental data of the present application, wherein the state switching method of the boiler heat recovery heat pump combined with environmental data comprises:

[0045] S100: Real-time collection of internal and external environmental data of the boiler system.

[0046] Specifically, the key environmental parameters inside and outside the boiler during operation are continuously obtained through a sensor network or a data interface, aiming to obtain the dynamic running state of the boiler and heat pump system and external economic and time constraint information, so as to provide basic data for subsequent state research and switching decision.

[0047] Specifically, the internal environmental data of the boiler system can include flue gas component concentration, fluid data and real-time hardness value, which are respectively used to predict corrosion, frosting and descaling demand. The external environmental data is based on external operating conditions, combined with economy and equipment aging characteristics, to enhance the flexibility and predictability of state switching. Real-time collection ensures the timeliness and accuracy of data, avoids response lag, and improves system operation efficiency and stability.

[0048] In some embodiments, the real-time collection of internal environmental data of the boiler system comprises:

[0049] The flue gas component concentration of the boiler is read in real time by a sensor array installed on the boiler flue, wherein the flue gas component concentration of the boiler at least includes the concentration value of sulfur oxide in flue gas, the concentration value of nitrogen oxide in flue gas, the humidity value and the flue gas temperature; the heat pump fluid data is read in real time by a sensor installed in the heat pump system, wherein the heat pump fluid data at least includes the inlet and outlet air path static pressure difference value of the evaporator, the inlet and outlet water path temperature of the evaporator, and the water path volume flow of the evaporator; the real-time hardness value of circulating water is read by an online water quality sensor.

[0050] Specifically, the boiler system internal environment data refers to various physical and chemical parameters directly reflecting the operating conditions of the boiler and its associated heat pump system. In the boiler heat recovery heat pump scenario, the key indicators include the boiler flue gas component concentration, heat pump fluid data, and circulating water hardness value, which are used to evaluate the combustion efficiency, heat exchange performance, and scaling risk, respectively. Among them, the concentration values of sulfur oxides and nitrogen oxides in the flue gas can reflect the completeness of fuel combustion and the level of pollution emissions, and the flue gas humidity and temperature are closely related to the boiler thermal efficiency; the inlet and outlet static pressure difference of the heat pump evaporator, water temperature and flow can be used to judge whether there is blockage or frosting phenomenon in the heat exchanger; the circulating water hardness is a direct indicator of the concentration of calcium and magnesium ions in the water, which is positively correlated with the scaling rate.

[0051] Specifically, an infrared gas analyzer or an array of electrochemical sensors is installed on the boiler flue to monitor the concentration (in ppm) of , in the flue gas in real time, and simultaneously collect the humidity (%RH) and temperature (°C) of the flue gas. For example, at a certain operating time, the sensor reads 120 ppm, 80 ppm, humidity 18%RH, and temperature 160°C, indicating that the current combustion emissions are at a moderate level.

[0052] Specifically, the static pressure sensors installed at the inlet and outlet of the evaporator obtain the pressure difference of the gas path, combined with the inlet water temperature, outlet water temperature, and volume flow, to calculate the heat exchange efficiency and determine whether there is an increase in thermal resistance or flow abnormality. At the same time, online water quality sensors such as inductively coupled conductivity meters or calcium and magnesium ion selective electrodes can read the circulating water hardness value in real time, for example, in , which can be used to dynamically evaluate the scaling risk.

[0053] Through the above process, multi-dimensional and real-time monitoring of the internal operating state of the boiler heat recovery heat pump can be achieved, significantly improving the perception ability of combustion efficiency, heat exchange performance, and scaling trend, helping to capture operation abnormalities and potential failures in real time, supporting the accurate calculation of subsequent descaling demand index and the state switching decision of the heat pump system, thereby improving the intelligent level of system operation, energy saving effect, and timeliness of maintenance response, prolonging the service life of equipment and reducing operation and maintenance costs.

[0054] In some embodiments, the external environment data at least includes the cumulative running time of the heat pump read from the system clock and the real-time electricity price signal obtained from the power grid interface, wherein the real-time electricity price signal includes peak, flat, and valley.

[0055] Specifically, the cumulative operating time of the heat pump in the external environmental data reflects the equipment's usage level and potential aging status. Longer operating times may indicate a higher risk of scaling, creating a potential need for descaling. Real-time electricity price signals represent the economic efficiency of the external power grid, such as different prices during peak, off-peak, and valley periods. This guides the system to perform energy-intensive operations like dry-burning descaling during off-peak hours, reducing operating costs and minimizing the impact on the external power grid. Considering external environmental data helps improve the flexibility and economy of the heat pump system at different operating stages, ensuring its efficient and stable operation.

[0056] S200: Based on the collected environmental data, activate the multi-channel analysis component in parallel to perform state switching analysis and obtain a state instruction set, wherein the state instruction set includes at least one of the following: downgrade state instruction, dry burning and descaling state instruction, and defrosting state instruction. The multi-channel analysis component includes at least a first analysis channel for downgrade switching analysis, a second analysis channel for dry burning and descaling switching analysis, and a third analysis channel for defrosting switching analysis, all set in parallel.

[0057] Specifically, the multi-channel analysis component is a state-switching decision-making function component composed of multiple independent or collaborative analysis units. It may include algorithm models, rule modules, or expert systems, and is used to process the collected environmental data in parallel and make state identification judgments. In particular, the multi-channel analysis component is used to classify and identify the operating status of boiler heat recovery heat pump systems.

[0058] Specifically, the status instruction set is a set of control commands output by the analysis components, used to guide the system to enter different operating modes, including degraded state (indicating that the system performance has degraded and the load needs to be reduced or the system needs to be shut down), dry burning descaling state (indicating that dry burning heating needs to be started to achieve rapid descaling), and defrosting state (indicating that the heat pump needs to be started to reverse defrost in order to maintain heat exchange efficiency), etc.

[0059] Specifically, real-time collected environmental data such as boiler flue gas composition, heat pump fluid parameters, and water hardness are input into multiple analysis channels. For example, channel 1 is a rule-based diagnostic model, which sets if... If the concentration exceeds 150 ppm and the water hardness is greater than 200 mg / L, a dry-burning descaling command is triggered. Channel 2 is a machine learning-based classification model that outputs system status classification labels, such as "normal," "scaling," "frost," and "performance degradation," after inputting multiple sensor parameters. Channel 3 is an energy efficiency analysis module based on a thermodynamic model, which calculates the performance coefficient change trend and determines whether a degradation state has been entered. These channels run in parallel on edge computing nodes or in the cloud, and ultimately merge their respective judgment results into a unified set of status commands.

[0060] Through the above process, multi-angle and multi-model parallel analysis of the running state can be realized, the risk of misoperation caused by single index misjudgment can be avoided, and the accuracy and robustness of state switching judgment can be improved. The multi-channel research and judgment mechanism enables the heat pump system to have stronger self-adaptability and fault tolerance, can quickly identify whether it needs to enter the defrosting, descaling or degraded running state under complex working conditions, thereby ensuring the continuity and safety of system operation, and improving the overall thermal efficiency and intelligent level.

[0061] In some embodiments, the multi-channel research and judgment component includes a first research and judgment channel, which is configured to:

[0062] The acid dew point temperature is calculated in real time based on the concentration of the boiler flue gas components. The running parameter sequence of the heat pump system is obtained, and the predicted minimum wall temperature of the evaporator and the corresponding most unfavorable temperature position are determined based on the running parameter sequence and the pre-constructed twin simulation model. The minimum wall temperature is calculated and judged whether it is less than the sum of the acid dew point temperature and the dynamic safety margin based on the dynamic safety margin, wherein the dynamic safety margin is determined according to historical operation data and environmental fluctuation characteristics. If the determination result is yes, a degraded state instruction is generated. If the determination result is no, no instruction is output.

[0063] Specifically, the acid dew point temperature refers to the lowest temperature at which acid droplets begin to condense in the cooling process of the acid gas in the boiler flue gas, which is usually around 100℃, and depends on the sulfur content and humidity in the flue gas. If the surface temperature of a component in the boiler or heat pump system is lower than the acid dew point temperature, acid condensation corrosion is likely to occur, affecting the service life of the equipment. Specifically, the minimum wall temperature refers to the point with the lowest temperature on the heat exchange wall inside the heat pump evaporator, which usually appears in the area with the lowest fluid heat exchange efficiency or the highest risk of frosting, and can be obtained by predicting the temperature distribution and heat exchange state inside through the twin simulation model and the current running parameter sequence.

[0064] Specifically, the dynamic safety margin is a temperature safety boundary value set to avoid measurement errors, environmental fluctuations or model uncertainties, and has dynamic variation characteristics, which is usually obtained by fitting or statistical analysis of historical data.

[0065] Specifically, the first research and judgment channel first uses the concentration and flue gas humidity value collected by the boiler flue sensor to calculate the current acid dew point temperature based on an empirical formula or a lookup table method. For example, when the concentration of SO2 is 1000 mg / m3 and the flue gas humidity is 10%, the acid dew point temperature is calculated to be 100℃.

[0066] Specifically, the first research and judgment channel first uses the concentration and flue gas humidity value collected by the boiler flue sensor to calculate the current acid dew point temperature based on an empirical formula or a lookup table method. For example, when the concentration of SO2 is 1000 mg / m3 and the flue gas humidity is 10%, the acid dew point temperature is calculated to be 100℃. Specifically, the first research and judgment channel first uses the concentration and flue gas humidity value collected by the boiler flue sensor to calculate the current acid dew point temperature based on an empirical formula or a lookup table method. For example, when the concentration of SO2 is 1000 mg / m3 and the flue gas humidity is 10%, the acid dew point temperature is calculated to be 100℃. ​​With a concentration of 150 ppm and a humidity of 20%, the acid dew point temperature can be calculated to be 112℃. Subsequently, the heat pump operating parameter sequence (such as evaporator inlet and outlet water temperatures, gas pressure difference, flow rate, etc.) is acquired and input into a thermodynamically pre-built twin simulation model to simulate the temperature distribution on the evaporator's inner wall and determine the lowest wall temperature and its corresponding location. For example, the predicted lowest wall temperature is 108℃, located at the downstream end of the evaporator. Further, based on the fluctuation range of historical operating data, a safety margin of 6℃ is dynamically set, resulting in a sum of the acid dew point temperature and the safety margin of 118℃. Since 108℃ < 118℃, acid condensation corrosion is suspected, and a degraded status command is generated, prompting a reduction in load or adjustment of operating parameters to avoid equipment damage. If the predicted lowest wall temperature is 122℃, which is higher than the threshold, no command is output.

[0067] For example, determining the dynamic safety margin based on historical operating data and environmental fluctuation characteristics includes: first, collecting system operating data over a past period, including acid dew point temperature, wall temperature, boiler load, flue gas humidity, etc. Concentration, heat pump flow rate, etc., are analyzed, and outlier removal and smoothing are performed. Then, using analytical methods such as sliding window statistics or Fourier transform, the fluctuation range of the above operating data at different time scales is extracted. For example, if the past 24 hours... A concentration standard deviation of 15 ppm and an air temperature variation range of 12℃ can be considered as significant environmental fluctuations. Next, based on the historical distribution of the difference between wall temperature and acid dew point temperature, combined with alarm or corrosion records of the equipment under different operating conditions, a margin adjustment model based on linear regression, support vector regression, or fuzzy logic control is constructed. This enables the analysis of environmental fluctuation characteristics based on real-time internal and external environmental data, and the input of this data into the margin adjustment model to obtain a dynamic safety margin. For example, during a certain operation, historical system data shows that... When concentration fluctuations exceed 20 ppm and flue gas humidity fluctuations exceed 10%, a difference of at least 7°C must be maintained between the minimum wall temperature and the acid dew point temperature to prevent corrosion. Therefore, under the current detected fluctuation conditions, the dynamic safety margin can be set to 7°C instead of the default 5°C.

[0068] Through the above process, the setting of dynamic safety margins can better align with current operating conditions and historical experience, avoiding misjudgments of corrosion risks due to excessively small margins or unnecessary degraded operation due to excessively large margins. This helps improve the system's adaptability and judgment accuracy, achieving a balance between safety and operational efficiency, and enhancing reliability.

[0069] Through the above process of dynamically predicting potential corrosion risks based on real-time data and simulation models, precise safety control can be achieved. Compared with the traditional method of setting fixed temperature limits or relying on human experience, this method has higher precision and adaptability. It can identify and avoid acid dew point corrosion risks in a timely manner without affecting the overall efficiency of the system, thereby improving predictive maintenance capabilities and operational safety.

[0070] In some embodiments, the multi-channel judgment component further comprises a second judgment channel for:

[0071] Based on the heat pump fluid data, the real-time heat exchange amount and the real-time heat exchange coefficient are calculated; based on the historical operation data of the heat pump system and the preset confidence threshold, the baseline heat exchange coefficient is determined, and the real-time relative heat exchange efficiency is calculated correspondingly, and the performance attenuation degree is output; combined with the self-learning optimization weight, the real-time hardness value, the performance attenuation degree and the heat pump cumulative running time are weighted to obtain the descaling demand index; according to the performance attenuation degree, the confidence constraint is configured, and the dynamic index threshold of the descaling demand index in the preset time window is determined by combining the statistical analysis method; if the descaling demand index is greater than the dynamic index threshold and the real-time electricity price signal is valley, a dry burning descaling state instruction is generated, otherwise no instruction is output.

[0072] Specifically, the heat exchange coefficient is a key indicator for measuring the heat conduction efficiency between the fluid and the heat exchange wall in the heat pump system, with a unit of W / (m²·K), which is used to reflect the heat transfer rate per unit area per unit temperature difference. The real-time heat exchange amount is the heat transferred per unit time in the evaporator or condenser of the heat pump system at the current time, which can be calculated by the flow meter and the temperature difference between the inlet and outlet water.

[0073] Specifically, the performance attenuation degree refers to the relative decline ratio of the current heat exchange efficiency relative to the historical baseline state, reflecting the degree of performance degradation of the heat pump system due to factors such as fouling and aging. The descaling demand index is a comprehensive index for quantifying whether the system needs to be descaled, which is calculated by weighting the heat pump running time, performance attenuation degree and water hardness.

[0074] Specifically, dry burning descaling is a descaling method that uses the heat pump's own operation to heat the fouling area and decompose the fouling in a no-water or low-flow state. It is usually executed at a lower electricity price (valley electricity price) to save costs.

[0075] Specifically, the second judgment channel first obtains fluid data from the heat pump system, including inlet and outlet water temperature, flow, pressure, etc., and calculates the current heat exchange amount = ρ (density) × c pSpecifically, the heat exchange coefficient is calculated by the formula: (specific heat capacity) x ΔT (temperature difference) x V (volume flow rate) and the heat exchange coefficient (obtained by the NTU-ε method or an empirical formula); then, based on historical operation data, such as the heat exchange coefficient during the first operation or efficient operation after cleaning, and in combination with a set confidence level (95%), a reference heat exchange coefficient is extracted, for example, the historical average is 680 W / (m²·K). The real-time heat exchange coefficient with a 95% confidence level is 590 W / (m²·K), and the performance degradation degree is (680 590) / 680=13.2%.

[0076] Finally, according to the obtained real-time hardness value of the water quality and the cumulative running time of the heat pump, a descaling demand index is formed according to the self-learning weight, for example: descaling demand index=w1x hardness value+w2x performance degradation degree+w3x running time, wherein w1, w2 and w3 are weight coefficients of the hardness value, the performance degradation degree and the running time, respectively. If w1=0.4, w2=0.4, w3=0.2, the hardness is 300 ppm, the degradation degree is 13.2%, and the running time is 1200 hours, in combination with the maximum hardness value of 500 ppm and the designed running time of 2000 hours, the descaling demand index can be calculated as:

[0077] 0.4x300 / 500+0.4x0.132+0.2x1200 / 2000=0.24+0.0528+0.12=0.4128.

[0078] Further, through statistical analysis in the sliding time window, the current dynamic index threshold is determined to be 0.405, and the current descaling demand index is higher than the threshold. If the electricity price signal is also in the valley, such as the night-time electricity price being lower than 0.2 yuan / kWh, a dry burning descaling state instruction is generated, otherwise no instruction is output and the system continues to run. The calculation method of the index enables the system to intelligently determine whether descaling is needed according to different working conditions, avoiding the resource waste or lag maintenance problem caused by fixed-period descaling.

[0079] Through the above process, the second judgment channel can realize intelligent identification and active maintenance of the scaling risk of the heat pump system. Compared with the traditional descaling method based on fixed time interval or manual judgment, through the fusion of multi-source data, dynamic threshold and self-learning mechanism, personalized and accurate descaling decisions are realized. Not only the system operation efficiency is improved, the energy consumption is reduced, the operation cost is further optimized, but also the economic efficiency and intelligent level of the system are improved.

[0080] In some embodiments, the multi-channel judgment component further includes a third judgment channel for:

[0081] Differential calculation is performed on the static pressure differential value of the gas circuit to obtain a pressure differential change rate; it is determined whether the pressure differential change rate exists for a continuous preset number of times greater than a preset positive threshold value, and if the determination result is yes, a defrosting state instruction is generated; and if the determination result is no, no instruction is output.

[0082] Specifically, the static pressure differential value of the gas circuit refers to the static pressure differential between the inlet and outlet of the condenser air side in the heat pump system, reflecting the change of the flow resistance of the air flow in the heat exchanger. The pressure differential change rate refers to the change speed of the pressure differential per unit time, i.e., the first derivative (calculated by differentiation) of the pressure differential, which is used to determine whether a rapid airflow blockage or frosting phenomenon occurs.

[0083] Specifically, the continuous preset number of times greater than the preset positive threshold value means that the pressure differential change rate continuously exceeds a certain set positive threshold value, for example, greater than 5 Pa / min, for a plurality of continuous sampling periods, and it can be considered that frosting may continue. The defrosting state instruction is a control signal generated by the heat pump system after detecting the frosting trend, which is used to trigger the defrosting mode (such as reverse cycle, hot gas bypass, etc.) to restore the heat exchange efficiency.

[0084] Specifically, the third judgment channel first collects the static pressure values of the inlet and outlet of the gas circuit in real time through the sensor, and calculates the static pressure differential value. For example, the inlet side is 1020 Pa, and the outlet side is 980 Pa, and the differential is 40 Pa. Then, at a fixed time interval, such as every minute, the differential algorithm is used to calculate the pressure differential change rate. For example, if the pressure differential one minute ago is 30 Pa, and now it is 40 Pa, then the change rate is (40 30) / 1=10 Pa / min. The system sets the threshold value to be 5 Pa / min, and the continuous number of times is 3, i.e., if the pressure differential change rate is greater than 5 Pa / min for 3 consecutive periods, it is determined that there is a continuous frosting trend. At this time, the defrosting state instruction is generated, and the defrosting process is started; if not, if the condition is not met, no instruction is output, and the current running state is maintained.

[0085] Through the above process, the third judgment channel uses the pressure differential change rate as a sensitive index to quickly identify the frosting trend of the air side heat exchanger in the heat pump system. Compared with the traditional method based on temperature difference or timed defrosting, the response is faster, the misjudgment rate is lower, which helps to improve the accuracy and energy efficiency of the defrosting control, reduces the adverse effects of the number of defrosting on the system energy consumption and component life, and thus improves the stability and economy of the heat pump system.

[0086] In some implementations, the confidence constraint is configured according to the performance degradation degree, and a dynamic index threshold of the scale-up demand index in the preset time window is determined by combining a statistical analysis method, including:

[0087] Based on the preset time window, a set of historical descaling demand indexes is obtained, and a statistical analysis is performed on the historical index mean and the historical index standard deviation of the set of historical descaling demand indexes; a confidence level corresponding to the performance degradation degree is matched by combining a preset mapping rule; a Z value corresponding to the confidence level is taken as a correction coefficient of the historical index standard deviation, and the historical index mean and the historical index standard deviation are weighted to obtain the dynamic index threshold.

[0088] Specifically, the confidence constraint is a limit condition set based on the confidence level in statistics, and is used to control the reliability of the judgment result. The preset time window is a historical data time range for statistical analysis, such as the past 30 days or 500 hours. The dynamic index threshold is a judgment threshold dynamically adjusted according to the historical data distribution, which is used to replace the fixed threshold to improve the accuracy and adaptability of the judgment.

[0089] Specifically, the Z value is a statistical value corresponding to the confidence level under the standard normal distribution, for example, the Z value corresponding to the 95% confidence level is about 1.96. Based on the Z value, the historical data volatility is adjusted (the historical mean and the standard deviation are weighted), and a dynamic threshold with statistical significance can be formed.

[0090] Specifically, first, in a set time window, such as the past 30 days, the descaling demand index is collected every hour or every day during the system operation to form a set of historical descaling demand indexes. For example, the historical index mean is 0.42, and the historical standard deviation is 0.06. Then, according to the current performance degradation degree, the preset mapping rule is matched, for example, the performance degradation degree between 15% and 20% corresponds to a confidence level of 90%, and the Z value corresponding to the current 18% RH is 1.645. Further, the Z value is taken as a correction coefficient multiplied by the historical standard deviation, and then added to the historical mean to obtain a dynamic threshold, that is, the dynamic index threshold = 0.42 + 1.645 x 0.06 = 0.5187, which will be used as the basis for judging whether to trigger the descaling instruction in the current period. If the real-time calculated descaling demand index 0.4128 is less than the threshold, it is judged that descaling is not needed.

[0091] Through the above process, the fixed threshold is no longer relied on, but the judgment standard is dynamically adjusted according to the current performance state of the equipment and the historical operation data. The false positive rate and the false negative rate can be effectively reduced, and the descaling decision is more scientific and reasonable.

[0092] S300: In combination with a preset priority control constraint, state competition arbitration is performed on the state instruction set to determine the execution order of multiple state instructions in the state instruction set, and a state switching timing instruction is correspondingly generated, wherein the priority control constraint is used to define the priority of multiple state instructions.

[0093] Specifically, the state instruction set is a set of instructions generated by multiple monitoring and judgment channels for controlling the running state of the heat pump system, and the state competition arbitration is a process of determining which instruction to execute first according to a preset rule when multiple instructions meet the trigger condition at the same time, causing execution conflict or resource competition.

[0094] Specifically, the state switching timing instruction is a scheduling command output after arbitration for controlling state switching, ensuring that the system runs in order under reasonable timing.

[0095] In some embodiments, the priority control constraint includes an execution order constraint of the instruction and an execution interval constraint when executed in sequence.

[0096] Specifically, the priority control constraint is a control strategy set to avoid instruction conflict or frequent switching, including an execution order constraint (i.e., the priority relationship between instructions, such as defrosting being prior to descaling) and an execution interval constraint (i.e., a certain time interval must be maintained between two instructions before execution, ensuring that each type of instruction is executed when the device is in a relatively stable initial state. Preventing frequent switching from causing damage to the device).

[0097] Specifically, first, the state instruction set from multiple judgment channels is received, for example, the trigger conditions for defrosting and descaling are met at the same time, and both instructions are activated. At this time, state competition arbitration is performed according to the preset priority control constraint. For example, the priority of the defrosting instruction is set to 1 and the priority of the descaling instruction is set to 2 (the smaller the number, the higher the priority), and the system executes the defrosting instruction first. At the same time, the execution interval constraint is checked, for example, the defrosting instruction is executed at least 30 minutes before the descaling instruction is executed to avoid frequent switching that causes the heat pump to frequently start and stop or the system to fluctuate in heat, affecting stability. Finally, a state switching timing instruction can be output, for example: execute defrosting immediately, plan to execute descaling in 30 minutes, and synchronize the instruction to the controller execution queue.

[0098] Through the above process, it is ensured that in the case of multiple state instructions being executed at the same time, each operation can be sequentially and reasonably scheduled and executed, avoiding the decline in system efficiency or the exacerbation of device wear caused by instruction conflict or frequent switching, which helps to enhance the running stability, reliability and life management capability of the heat pump system.

[0099] S400: Apply the state switching timing instruction to the actuator of the heat pump to switch the working state of the heat pump.

[0100] Specifically, the state switching timing instruction is converted into a machine language instruction to guide the actuator to perform accurate actions, such as adjusting a valve, controlling the speed of a motor, etc., so as to realize the conversion of the heat pump between different working states. For example, when receiving a degraded state instruction, the actuator can reduce the power of the heat pump or shut down part of the function through a servo motor or a proportional control valve; when receiving a defrosting instruction, the heat pump may be triggered to reverse the cycle to remove frost.

[0101] In summary, the state switching method of the boiler heat recovery heat pump combined with environmental data provided by the present application has the following technical effects:

[0102] By collecting the internal and external environmental data of the boiler system in real time, a plurality of channel research and judgment components are activated in parallel to generate a state instruction set, including a degraded state instruction, a dry burning descaling state instruction or a defrosting state instruction; arbitration is performed in combination with a priority constraint to determine a state switching timing instruction; and the timing instruction is applied to the heat pump actuator to realize the switching of the working state, thereby achieving the technical effects of improving the accuracy and timeliness of state control, and improving the stability and economy of the system.

[0103] In the second embodiment, the state switching method of the boiler heat recovery heat pump combined with environmental data is as shown in the following table: Figure 2 is a structural schematic diagram of the state switching system of the boiler heat recovery heat pump combined with environmental data of the present application. For example, Figure 1 The flowchart of the state switching method of the boiler heat recovery heat pump combined with environmental data of the present application can be implemented by the structure as shown in Figure 2 .

[0104] Based on the same concept as the state switching method of the boiler heat recovery heat pump combined with environmental data in the embodiments, the state switching system of the boiler heat recovery heat pump combined with environmental data provided by the present application comprises:

[0105] An environmental data acquisition module 11 is configured to collect internal and external environmental data of a boiler system in real time.

[0106] A state switching research and judgment module 12 is configured to activate a plurality of channel research and judgment components in parallel to perform state switching research and judgment according to the collected environmental data, and obtain a state instruction set, wherein the state instruction set comprises at least one of a degraded state instruction, a dry burning descaling state instruction and a defrosting state instruction, and the plurality of channel research and judgment components at least comprise a first research and judgment channel for degraded state switching research and judgment, a second research and judgment channel for dry burning descaling state switching research and judgment, and a third research and judgment channel for defrosting state switching research and judgment.

[0107] A state competition arbitration module 13 is configured to perform state competition arbitration on the state instruction set in combination with a preset priority control constraint, determine the execution sequence of a plurality of state instructions in the state instruction set, and correspondingly generate a state switching timing instruction, wherein the priority control constraint is used to define the priority of the plurality of state instructions.

[0108] The heat pump working state switching module 14 is configured to apply the state switching timing instruction to the actuator of the heat pump to switch the working state of the heat pump.

[0109] In some embodiments, the execution steps of the environment data acquisition module 11 include:

[0110] The concentration of the flue gas components of the boiler is read in real time by a sensor array installed on the flue of the boiler, wherein the concentration of the flue gas components of the boiler at least includes the concentration value of sulfur oxides, the concentration value of nitrogen oxides, the humidity value, and the flue gas temperature in the flue gas.

[0111] The heat pump fluid data is read in real time by a sensor installed in the heat pump system, wherein the heat pump fluid data at least includes the pressure difference value of the inlet and outlet air path of the evaporator, the inlet temperature and outlet temperature of the water path of the evaporator, and the volume flow of the water path of the evaporator.

[0112] The real-time hardness value of the circulating water is read by an online water quality sensor.

[0113] In some embodiments, the external environment data at least includes the cumulative running time of the heat pump read from the system clock and the real-time electricity price signal obtained from the power grid interface, wherein the real-time electricity price signal includes peak, flat, and valley.

[0114] In some embodiments, the multi-channel judgment component in the state switching judgment module 12 includes a first judgment channel, and the execution steps of the first judgment channel include:

[0115] The acid dew point temperature is calculated in real time based on the concentration of the flue gas components of the boiler.

[0116] The running parameter sequence of the heat pump system is obtained, and the predicted minimum wall surface temperature of the evaporator and the corresponding most unfavorable temperature position are determined based on the running parameter sequence and the pre-constructed twin simulation model.

[0117] In combination with the dynamic safety margin, it is calculated and judged whether the minimum wall surface temperature is less than the sum of the acid dew point temperature and the dynamic safety margin, wherein the dynamic safety margin is determined according to historical running data and environmental fluctuation characteristics.

[0118] If the determination result is yes, a degraded state instruction is generated. If the determination result is no, no instruction is output.

[0119] In some embodiments, the multi-channel judgment component in the state switching judgment module 12 further includes a second judgment channel, and the execution steps of the second judgment channel include:

[0120] Based on the heat pump fluid data, the real-time heat exchange amount and the real-time heat exchange coefficient are calculated.

[0121] Based on the historical operation data of the heat pump system and a preset confidence threshold, a benchmark heat exchange coefficient is determined, and a real-time relative heat exchange efficiency is calculated correspondingly, and a performance attenuation degree is output.

[0122] In combination with self-learning optimization weights, the real-time hardness value, the performance attenuation degree and the cumulative running time of the heat pump are weighted to obtain a descaling demand index.

[0123] According to the performance attenuation degree, a confidence constraint is configured, and a dynamic index threshold of the descaling demand index in a preset time window is determined in combination with a statistical analysis method.

[0124] If the descaling demand index is greater than the dynamic index threshold and the real-time electricity price signal is a valley, a dry burning descaling state instruction is generated, and if not, no instruction is output.

[0125] In some embodiments, the execution step of the second judgment channel further includes:

[0126] Based on the preset time window, a historical descaling demand index set is obtained, and a historical index mean and a historical index standard deviation of the historical descaling demand index set are statistically analyzed.

[0127] In combination with a preset mapping rule, a confidence level corresponding to the performance attenuation degree is matched and obtained.

[0128] The Z value corresponding to the confidence level is taken as a correction coefficient of the historical index standard deviation, and the historical index mean and the historical index standard deviation are weighted to obtain the dynamic index threshold.

[0129] In some embodiments, the priority control constraint includes an execution order constraint of the instruction and an execution interval constraint when sequentially executed.

[0130] It should be understood that the embodiments mentioned in the specification focus on their differences from other embodiments, and the specific embodiments in the first embodiment are also applicable to the state switching system of the boiler heat recovery heat pump combined with environmental data described in the second embodiment. For the sake of brevity of the specification, no further expansion is made here.

[0131] It should be understood that the embodiments disclosed in the present application and the above description can enable those skilled in the art to implement the present application. At the same time, the present application is not limited to the part of the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments or make equivalent replacement of some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of switching the state of a boiler heat recovery heat pump in combination with environmental data, characterized by, The method comprises the following steps: Real-time acquisition of environmental data in the boiler system and external environmental data; According to the collected environmental data, a multi-channel research and judgment component is activated in parallel to conduct state switching research and judgment to obtain a state instruction set, wherein the state instruction set includes at least one of a degraded state instruction, a dry burning and descaling state instruction, and a defrosting state instruction, and the multi-channel research and judgment component includes at least a first research and judgment channel, a second research and judgment channel, and a third research and judgment channel for dry burning and descaling switching research and judgment. Combined with a preset priority control constraint, the state competition arbitration is performed on the state instruction set to determine the execution order of multiple state instructions in the state instruction set and correspondingly generate a state switching timing instruction, wherein the priority control constraint is used to define the priority of multiple state instructions. The state switching timing instruction is applied to the actuator of the heat pump to switch the working state of the heat pump. Real-time acquisition of environmental data in the boiler system includes: Real-time reading of boiler flue gas component concentration through a sensor array installed on the boiler flue, wherein the boiler flue gas component concentration includes at least the concentration value of sulfur oxides, the concentration value of nitrogen oxides, the humidity value, and the flue gas temperature in the flue gas. Real-time reading of heat pump fluid data through a sensor installed in the heat pump system, wherein the heat pump fluid data includes at least the inlet and outlet air path static pressure difference value of the evaporator, the water inlet and outlet temperature of the evaporator, and the water volume flow of the evaporator. Reading the real-time hardness value of the circulating water through an online water quality sensor. The multi-channel research and judgment component includes a first research and judgment channel, which is used to: Real-time calculation of acid dew point temperature based on boiler flue gas component concentration; Obtaining a sequence of operating parameters of the heat pump system and determining the predicted minimum wall temperature of the evaporator and the corresponding most unfavorable temperature position based on the sequence of operating parameters and a pre-constructed twin simulation model; Combined with a dynamic safety margin, it is calculated and judged whether the minimum wall temperature is less than the sum of the acid dew point temperature and the dynamic safety margin, wherein the dynamic safety margin is determined according to historical operating data and environmental fluctuation characteristics; If the determination result is yes, a degraded state instruction is generated; if the determination result is no, no instruction is output.

2. The method of switching the state of a boiler heat recovery heat pump in conjunction with environmental data according to claim 1, characterized by, The external environmental data includes at least the cumulative running time of the heat pump read from the system clock and the real-time electricity price signal obtained from the power grid interface, wherein the real-time electricity price signal includes peak, flat, and valley.

3. The method of switching the state of a boiler heat recovery heat pump in conjunction with environmental data according to claim 2, characterized by, The multi-channel research and judgment component also includes a second research and judgment channel, which is used to: Based on the heat pump fluid data, the real-time heat exchange amount and the real-time heat exchange coefficient are calculated; Based on the historical operating data of the heat pump system and the preset confidence threshold, the baseline heat exchange coefficient is determined, and the real-time relative heat exchange efficiency is correspondingly calculated, and the performance attenuation degree is output; Combined with the self-learning optimization weight, the real-time hardness value, the performance attenuation degree, and the cumulative running time of the heat pump are weighted to obtain a descaling demand index; According to the performance attenuation degree, a confidence constraint is configured, and a dynamic index threshold of the descaling demand index in a preset time window is determined by a statistical analysis method. If the scale removal demand index is greater than the dynamic index threshold value and the real-time electricity price signal is valley, a dry burning scale removal state instruction is generated, otherwise no instruction is output.

4. The method of switching the state of a boiler heat recovery heat pump in conjunction with environmental data according to claim 2, characterized by, The multi-channel judgment component further includes a third judgment channel for: Differential calculation is performed on the air path static pressure differential value to obtain a differential rate of pressure differential; It is determined whether the differential rate of pressure differential has been greater than a preset positive threshold value for a continuous preset number of times, and if the determination result is yes, a defrosting state instruction is generated; If the determination result is no, no instruction is output.

5. The method of switching the state of a boiler heat recovery heat pump in conjunction with environmental data according to claim 3, characterized by, The performance degradation degree is configured with a confidence constraint, and a dynamic index threshold value of the scale removal demand index in a preset time window is determined by combining a statistical analysis method, including: Based on the preset time window, a historical scale removal demand index set is obtained, and a historical index mean value and a historical index standard deviation of the historical scale removal demand index set are statistically analyzed; A confidence level corresponding to the performance degradation degree is matched based on a preset mapping rule; The dynamic index threshold value is obtained by weighting the historical index mean value and the historical index standard deviation with a Z value corresponding to the confidence level as a correction coefficient of the historical index standard deviation.

6. The method of switching the state of a boiler heat recovery heat pump in conjunction with environmental data according to claim 4, characterized by, The priority control constraint includes an execution sequence constraint of the instructions and an execution interval constraint when sequentially executed.

7. A state switching system of a boiler heat recovery heat pump combined with environmental data, characterized by, A state switching method of a boiler heat recovery heat pump combined with environmental data according to any one of claims 1-6, comprising: an environmental data acquisition module for acquiring real-time environmental data and external environmental data in a boiler system; a state switching judgment module for activating a multi-channel judgment component in parallel to perform state switching judgment based on the collected environmental data, and obtaining a state instruction set, wherein the state instruction set includes at least one of a degradation state instruction, a dry burning scale removal state instruction, and a defrosting state instruction, and the multi-channel judgment component includes at least a first judgment channel for degradation switching judgment, a second judgment channel for dry burning scale removal switching judgment, and a third judgment channel for defrosting switching judgment, which are arranged in parallel; a state competition arbitration module for performing state competition arbitration on the state instruction set by combining a preset priority control constraint to determine the execution sequence of multiple state instructions in the state instruction set and correspondingly generate a state switching timing instruction, wherein the priority control constraint is used to define the priority of multiple state instructions; a heat pump working state switching module for applying the state switching timing instruction to the actuators of the heat pump to switch the working state of the heat pump.

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