Boiler heat recovery heat pump state switching method and system combined with environmental data

Abstract

The invention discloses a state switching method and system for a boiler heat recovery heat pump in combination with environmental data, and relates to the technical field of intelligent control. The method comprises the steps that internal environmental data and external environmental data of a boiler system are collected in real time; according to the collected environment data, a multi-channel studying and judging assembly is activated in parallel to conduct state switching studying and judging, a state instruction set is obtained, and the state instruction set comprises at least one of a degradation state instruction, a dry burning descaling state instruction and a defrosting state instruction; in combination with a preset priority control constraint, performing state competition arbitration on the state instruction set, and determining a state switching time sequence instruction; and applying the state switching time sequence instruction to an actuator of the heat pump so as to switch the working state of the heat pump. Therefore, the technical effects of improving the accuracy and timeliness of state control and improving the stability and economy of the system are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, in particular 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 judgment for 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: Real-time collection of environmental data and external environmental data in the boiler system.

[0005] 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, and a state instruction set is obtained, 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.

[0006] 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.

[0007] The state switching timing instruction is applied to the actuator of the heat pump to switch the working state of the heat pump.

[0008] In a feasible implementation manner, real-time collection of environmental data in the boiler system comprises: Real-time reading of the concentration of boiler flue gas components by a sensor array installed on the boiler flue, wherein the concentration of boiler flue gas components 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.

[0009] Through the sensor installed in the heat pump system, real-time heat pump fluid data is read, wherein the heat pump fluid data at least includes the inlet and outlet air path static pressure differential value flowing through the evaporator, the water inlet temperature and water outlet temperature flowing through the evaporator, and the water volume flow rate flowing through the evaporator.

[0010] Through the online water quality sensor, the real-time hardness value of the circulating water is read.

[0011] 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.

[0012] In a feasible implementation, the multi-channel research and judgment component includes a first research and judgment channel, and the first research and judgment channel is used for: The acid dew point temperature is calculated in real time based on the concentration of the flue gas components of the boiler.

[0013] 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.

[0014] 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.

[0015] If the determination result is yes, a degradation state instruction is generated. If the determination result is no, no instruction is output.

[0016] In a feasible implementation, the multi-channel research and judgment component further includes a second research and judgment channel, which is used for: Based on the heat pump fluid data, the real-time heat exchange amount and the real-time heat exchange coefficient are calculated.

[0017] 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.

[0018] 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 a descaling demand index.

[0019] 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.

[0020] 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.

[0021] In a feasible implementation, the multi-channel judgment component further comprises a third judgment channel, configured to: The air path static pressure differential value is differentiated to obtain a differential pressure change rate.

[0022] It is determined whether the differential pressure change rate 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.

[0023] If the determination result is no, no instruction is output.

[0024] In a feasible implementation, the confidence constraint is configured according to the performance attenuation degree, and the dynamic index threshold value of the scale removal demand index in the preset time window is determined by combining a statistical analysis method, comprising: 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.

[0025] The confidence level corresponding to the performance attenuation degree is matched according to a preset mapping rule.

[0026] 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 value and the historical index standard deviation are weighted to obtain the dynamic index threshold value.

[0027] In a feasible implementation, the priority control constraint comprises an execution sequence constraint of the instructions and an execution interval constraint when sequentially executed.

[0028] In a second aspect, the 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: An environmental data acquisition module is configured to acquire environmental data in a boiler system and external environmental data in real time.

[0029] A state switching judgment module is configured to activate a multi-channel judgment component in parallel to perform state switching judgment according to the acquired environmental data, and obtain a state instruction set, wherein the state instruction set comprises 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 comprises at least a first judgment channel, a second judgment channel and a third judgment channel which are arranged in parallel and used for degradation switching judgment, dry burning scale removal switching judgment and defrosting switching judgment, respectively.

[0030] A 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 configured to define the priority of the plurality of state instructions.

[0031] A 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.

[0032] 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 inside and outside a boiler system in real time; activating a multi-channel research and judgment component in parallel to generate a state instruction set, including degradation, dry burning 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

[0033] Figure 1 The figure is a flowchart of the state switching method of the boiler heat recovery heat pump combined with environmental data.

[0034] Figure 2 The figure is a structural diagram of the state switching system of the boiler heat recovery heat pump combined with environmental data.

[0035] In the drawings, the components represented by the numbers are described as follows: an environmental data acquisition 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

[0036] The above technical solutions will be described in detail below in combination with the drawings of the specification and specific embodiments, so as to better understand the above technical solutions. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments of the application, and it should be understood that the application is not limited to the example embodiments for explaining the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application. In addition, it should be noted that, for convenience of description, only the parts related to the application are shown in the drawings, not all.

[0037] Embodiment one, as Figure 1A flowchart of 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: S100: Real-time collection of internal and external environmental data of the boiler system.

[0038] Specifically, 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 operating state of the boiler and heat pump system and external economic and time constraint information, thereby providing basic data for subsequent state research and switching decision.

[0039] 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 requirements. The external environmental data is based on external operating conditions, combined with economic efficiency 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.

[0040] In some embodiments, the real-time collection of internal environmental data of the boiler system comprises: 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 values of sulfur oxides and nitrogen oxides in the flue gas, humidity value and 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 static pressure differential value of the gas path flowing through the evaporator, the inlet and outlet temperature of the water path flowing through the evaporator, and the volume flow of the water path flowing through the evaporator; the real-time hardness value of the circulating water is read by an online water quality sensor.

[0041] Specifically, the internal environmental data of the boiler system refers to various physical and chemical parameters that directly reflect the operating conditions of the boiler and its attached heat pump system. In the boiler heat recovery heat pump scenario, the key indicators include the flue gas component concentration of the boiler, the heat pump fluid data and the hardness value of the circulating water, which are respectively used to evaluate the combustion efficiency, the heat exchange performance and the scaling risk. 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 emission, the humidity and temperature of the flue gas are closely related to the thermal efficiency of the boiler; the inlet and outlet static pressure differential of the heat pump evaporator, the water path temperature and the flow can be used to judge whether there is blockage or frosting phenomenon in the heat exchanger; the hardness of circulating water is a direct indicator of the concentration of calcium and magnesium ions in water, which is positively correlated with the scaling rate.

[0042] Specifically, an infrared gas analyzer or an electrochemical sensor array is installed on the boiler flue to monitor the concentration of sulfur oxides and nitrogen oxides in the flue gas in real time. 、 the concentration (unit: ppm) of the flue gas, and synchronously collect the humidity (%RH) and temperature (℃) of the flue gas. For example, at a certain operating moment, the sensor reads 120 ppm, 80 ppm, humidity 18%RH, and temperature 160℃, indicating that the current combustion emission is at a medium level.

[0043] Specifically, the pressure difference of the gas circuit is obtained through the static pressure sensors installed at the inlet and outlet of the evaporator, and combined with the inlet water temperature, outlet water temperature, and volume flow of the water circuit, the heat exchange efficiency can be calculated to determine whether there is an increase in thermal resistance or flow abnormality. At the same time, through online water quality sensors such as inductively coupled conductivity meters or calcium and magnesium ion selective electrodes, the hardness value of the circulating water can be read in real time, for example, in mg / L, which can be used to dynamically evaluate the scaling risk.

[0044] 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, which significantly improves the perception ability of the combustion efficiency, heat exchange performance, and scaling trend, helps to capture operation abnormalities and potential faults in real time, supports the accurate calculation of the 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 the equipment and reducing the operation and maintenance cost.

[0045] 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.

[0046] Specifically, the cumulative running time of the heat pump in the external environment data reflects the degree of use and potential aging state of the equipment. When the running time is long, there may be a higher scaling risk, and there is a potential need to enter the descaling state. The real-time electricity price signal is an economic representation of the external power grid, such as different electricity prices during peak, flat, and valley periods, which is used to guide the system to perform high-energy-consuming operations such as dry burning descaling during the valley electricity price period to reduce operating costs while reducing the impact on the external power grid. By considering the external environment data, the flexibility and economy of the heat pump system in different operating stages can be improved, ensuring efficient and stable operation of the heat pump system.

[0047] S200: According to the collected environment data, activate the multi-channel research and judgment component in parallel to conduct state switching research and judgment, and obtain a state instruction set, wherein the state instruction set includes at least one of a degradation state instruction, a dry burning descaling state instruction, and a defrosting state instruction, and the multi-channel research and judgment component at least includes a first research and judgment channel for degradation 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] Through the above process, multi-angle and multi-model parallel analysis of the operating status can be achieved, avoiding the risk of misoperation caused by misjudgment of a single indicator, and improving the accuracy and robustness of state switching judgment. The multi-channel judgment mechanism enables the heat pump system to have stronger adaptability and fault tolerance, and can quickly identify whether it needs to enter defrosting, descaling or degrading operation under complex operating conditions, thereby ensuring the continuity and safety of system operation and improving the overall thermal efficiency and intelligence level.

[0052] In some embodiments, the multi-channel analysis component includes a first analysis channel, the first analysis channel being used for: The acid dew point temperature is calculated in real time based on the boiler flue gas component concentration; the operating 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 operating parameter sequence and the pre-constructed twin simulation model; combined with the dynamic safety margin, it is calculated and determined 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 based on 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.

[0053] Specifically, acid dew point temperature refers to the temperature of acidic gases in boiler flue gas ( , The lowest temperature at which acidic droplets begin to condense during the cooling process is usually around 100°C, depending on the sulfur content and humidity in the flue gas. If the surface temperature of a component in a boiler or heat pump system is lower than the acid dew point temperature, acid condensation corrosion is likely to occur, affecting the equipment's lifespan.

[0054] Specifically, the lowest wall temperature refers to the point with the lowest temperature on the heat exchange wall inside the heat pump evaporator. It usually occurs in the area with the lowest fluid heat exchange efficiency or the greatest risk of frosting. The internal temperature distribution and heat exchange status can be obtained by predicting the internal temperature distribution and heat exchange status through a twin simulation model and the current operating parameter sequence.

[0055] Specifically, dynamic safety margin is a temperature safety boundary value set to avoid measurement errors, environmental fluctuations, or model uncertainties. It has dynamic changing characteristics and is usually derived from historical data fitting or statistical analysis.

[0056] Specifically, the first analysis channel first utilizes data collected by boiler flue gas sensors. , The concentration and flue gas humidity values ​​are used to calculate the current acid dew point temperature based on empirical formulas or lookup tables. For example, when... 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.

[0057] 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... The concentration standard deviation is 15 ppm, and the air temperature variation range is 12°C, which can be considered as a relatively severe environmental fluctuation. Then, based on the difference distribution between the historical wall temperature and the acid dew point temperature, combined with the alarm or corrosion record of the equipment under different working conditions, a margin adjustment model based on linear regression, support vector regression or fuzzy logic control is constructed, so as to realize the process of analyzing the environmental fluctuation characteristics according to the real-time internal and external environmental data, and inputting to the dynamic safety margin adjustment model to obtain the dynamic safety margin. For example, in a certain operation, the system historical data shows that the acid dew point temperature is 60°C, and the wall surface temperature is 55°C, which is 5°C lower than the acid dew point temperature. At this time, the concentration fluctuation is greater than 20 ppm, and the flue gas humidity fluctuation is more than 10%. In order to avoid corrosion, at least a difference of 7°C between the minimum wall surface temperature and the acid dew point temperature is required. Therefore, under the current detection of similar fluctuation conditions, the dynamic safety margin can be set to 7°C instead of the default 5°C. When the concentration fluctuation is greater than 20 ppm and the flue gas humidity fluctuation is more than 10%, at least a difference of 7°C between the minimum wall surface temperature and the acid dew point temperature is required to avoid corrosion. Therefore, under the current detection of similar fluctuation conditions, the dynamic safety margin can be set to 7°C instead of the default 5°C.

[0058] Through the above process, the setting of the dynamic safety margin can be more consistent with the current working condition and historical experience, avoiding false corrosion risk caused by too small margin or unnecessary degradation operation caused by too large margin. It helps to improve the adaptive ability and judgment accuracy of the system, balances between safety and running efficiency, and enhances the reliability.

[0059] Through the above process of dynamically predicting potential corrosion risk based on real-time data and simulation model, accurate safety control can be realized. Compared with the traditional fixed temperature setting or manual experience judgment, it has higher precision and adaptability, and can timely identify and avoid acid dew point corrosion risk without affecting the overall efficiency of the system, improving the predictive maintenance ability and operation safety.

[0060] In some embodiments, the multi-channel judgment component further includes a second judgment channel for: 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 reference heat exchange coefficient is determined, and the real-time relative heat exchange efficiency is calculated correspondingly. The output is the performance attenuation degree. 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, the dry burning descaling state instruction is generated, otherwise no instruction is output.

[0061] 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), 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.

[0062] Specifically, the performance attenuation degree refers to the relative decline of the current heat exchange efficiency relative to the historical benchmark 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 based on the running time of the heat pump, the performance attenuation degree, and the water hardness, etc. The confidence threshold is used to determine whether the current heat exchange performance is reliable, and to filter effective benchmarks from historical data.

[0063] Specifically, dry burning descaling is a descaling method that uses the heat pump itself to heat the fouling area and decompose the fouling in a no-water or low-flow state, which usually needs to be performed at a low electricity price (electricity price valley) to save costs.

[0064] 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 p (pressure) × V (volume flow) and the heat exchange coefficient (retrieved by NTU-ε method or empirical formula) in real time; then, based on historical operation data, such as the heat exchange coefficient when running for the first time or running efficiently after cleaning, combined with the set confidence (95%), the benchmark heat exchange coefficient is extracted, for example, the historical average is 680 W / (m²·K). The real-time heat exchange coefficient of 95% confidence calculated is 590 W / (m²·K), and the performance attenuation degree is (680 590) / 680=13.2%.

[0065] Finally, according to the obtained real-time hardness value of water quality and the cumulative running time of the heat pump, the descaling demand index is formed by weighting according to the self-learning weight, for example: descaling demand index = w1×hardness value + w2×performance attenuation degree + w3×running time, where w1, w2, w3 are the weight coefficients of hardness value, performance attenuation degree and running time, respectively. If w1=0.4, w2=0.4, w3=0.2, the hardness is 300 ppm, the attenuation degree is 13.2%, and the running time is 1200 hours, combined with the maximum hardness value of 500 ppm and the designed running time of 2000 hours, the descaling demand index can be calculated as: 0.4×300 / 500+0.4×0.132+0.2×1200 / 2000=0.24+0.0528+0.12=0.4128.

[0066] Further, through statistical analysis within 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 problems caused by fixed-period descaling.

[0067] 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 running efficiency is improved, the energy consumption is reduced, the running cost is further optimized, but also the economic efficiency and intelligent level of the system are improved.

[0068] In some embodiments, the multi-channel judgment component further includes a third judgment channel for: The static pressure differential value of the gas circuit is differentiated 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. If the determination result is yes, a defrosting state instruction is generated. If the determination result is no, no instruction is output.

[0069] Specifically, the static pressure differential value of the gas circuit refers to the static pressure difference 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 of the pressure differential (calculated by differentiation), which is used to determine whether rapid airflow obstruction or frosting occurs.

[0070] Specifically, the continuous preset number of times greater than the preset positive threshold means that the pressure differential change rate continuously exceeds a certain set positive threshold, for example, greater than 5 Pa / min, which can be considered as possible continuous frosting. 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.

[0071] Specifically, the third judgment channel first acquires the static pressure values of the inlet and outlet of the gas circuit through the sensor in real time, and calculates the static pressure differential value. For example, the inlet side is 1020 Pa and the outlet side is 980 Pa, so the differential is 40 Pa. Then, the differential value is recorded at a fixed time interval, such as every minute, and the differential algorithm is used to calculate the differential change rate. If the differential was 30 Pa a minute ago and is now 40 Pa, the change rate is (40-30) / 1=10 Pa / min. If the change rate exceeds a certain set positive threshold, for example, greater than 5 Pa / min, it can be considered that the air flow is continuously blocked or frosting. 30) / 1=10 Pa / min. The system sets a threshold of 5 Pa / min, and the continuous number of times is 3, that is, if the pressure difference change rate is greater than 5 Pa / min in 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.

[0072] Through the above process, the third judgment channel uses the pressure difference 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 defrosting control, reduce the adverse effects of defrosting frequency on system energy consumption and component life, and thus improve the stability and economy of the heat pump system.

[0073] In some implementations, the confidence constraint is configured according to the performance degradation degree, and a dynamic index threshold of the descaling demand index in the preset time window is determined by combining a statistical analysis method, including: Based on the preset time window, a set of historical descaling demand indexes is obtained, and the historical index mean and the historical index standard deviation of the set of historical descaling demand indexes are statistically analyzed. Combined with a preset mapping rule, the confidence level corresponding to the performance degradation degree is matched. The 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.

[0074] Specifically, the confidence constraint is a limiting condition set based on the confidence level in statistics, which 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.

[0075] 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 (weighting the historical mean and standard deviation), which can form a dynamic threshold with statistical significance.

[0076] Specifically, first, collect the scale removal demand index of the system operation every hour or every day in a set time window, such as the past 30 days, to form a historical scale removal demand index set. 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, match the preset mapping rule, for example, the performance degradation degree is between 15% and 20%, and the confidence level is 90%, then the Z value corresponding to the current 18% RH is 1.645. Further, multiply the Z value by the historical standard deviation as a correction coefficient, and add it to the historical mean to obtain the 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 determining whether to trigger the scale removal instruction in the current period. If the real-time calculated scale removal demand index 0.4128 is less than the threshold, it is determined that scale removal is not needed.

[0077] 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 device and the historical operation data. The false positive rate and the false negative rate can be effectively reduced, and the scale removal decision is more scientific and reasonable.

[0078] 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.

[0079] 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 when multiple instructions meet the trigger condition at the same time, there is an execution conflict or resource competition, according to the preset rule.

[0080] Specifically, the state switching timing instruction is a scheduling command finally output after arbitration for controlling state switching, which ensures the orderly operation of the system under a reasonable timing.

[0081] In some embodiments, the priority control constraint includes an execution order constraint of the instructions and an execution interval constraint when sequentially executed.

[0082] Specifically, the priority control constraint is a control strategy set to avoid instruction conflict or frequent switching, including execution order constraint (i.e., the priority relationship between instructions, such as defrosting before scale removal) and execution interval constraint (i.e., two instructions must be separated by a certain time before execution to ensure that each type of instruction is executed in a relatively stable initial state. Preventing frequent switching from causing damage to the device).

[0083] Specifically, first, a set of state instructions from multiple research channels is received, for example, the current trigger conditions for defrosting and descaling are met at the same time, both instructions are activated. At this time, state competition arbitration is carried out 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), then the system executes the defrosting instruction first. At the same time, the execution interval constraint is also checked, for example, after the defrosting is completed, the descaling instruction needs to wait at least 30 minutes to be executed, so as to avoid frequent switching leading to frequent start-stop of the heat pump or system heat fluctuation 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.

[0084] Through the above process, it is ensured that in the case of concurrent multiple state instructions, each operation can be sequentially and reasonably scheduled and executed, avoiding the decline of system efficiency or the intensification of equipment wear caused by instruction conflict or frequent switching, which helps to enhance the operation stability, reliability and life management ability of the heat pump system.

[0085] S400: apply the state switching timing instruction to the actuator of the heat pump to switch the working state of the heat pump.

[0086] Specifically, the state switching timing instruction is converted into a machine language instruction to guide the actuator to perform precise actions, such as adjusting the valve, controlling the motor speed, etc., so as to realize the conversion between different working states of the heat pump. 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.

[0087] 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: By collecting real-time environmental data inside and outside the boiler system, activating multiple research and judgment components in parallel to generate a set of state instructions, including degraded, dry burning, descaling or defrosting instructions, combining priority constraints for arbitration to determine the state switching timing instruction, and applying the timing instruction to the heat pump actuator to realize the switching of the working state, the technical effects of improving the accuracy and timeliness of state control, and improving the stability and economy of the system are achieved.

[0088] Embodiment two, as 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 realized by the structure as Figure 2 shown.

[0089] Based on the same idea as the state switching method of the boiler heat recovery heat pump combined with environmental data in the embodiments, the application also provides a state switching system of a boiler heat recovery heat pump combined with environmental data, which comprises: An environmental data acquisition module 11 is configured to acquire environmental data in a boiler system and external environmental data in real time.

[0090] A state switching research and judgment module 12 is configured to activate a multi-channel research and judgment component in parallel to perform state switching research and judgment according to the acquired environmental data, and obtain a state instruction set, wherein the state instruction set comprises at least one of a degraded state instruction, a dry combustion descaling state instruction, and a defrosting state instruction, and the multi-channel research and judgment component comprises at least a first research and judgment channel, a second research and judgment channel, and a third research and judgment channel which are arranged in parallel and used for degraded state switching research and judgment, dry combustion descaling state switching research and judgment, and defrosting state switching research and judgment, respectively.

[0091] 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 the 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 state instructions.

[0092] A heat pump working state switching module 14 is configured to apply the state switching timing instruction to an actuator of the heat pump to switch the working state of the heat pump.

[0093] In some embodiments, the execution steps of the environmental data acquisition module 11 comprise: Real-time reading of the concentration of boiler flue gas components through a sensor array installed on the boiler flue, wherein the concentration of boiler flue gas components comprises 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.

[0094] Real-time reading of heat pump fluid data through a sensor installed in the heat pump system, wherein the heat pump fluid data comprises at least 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.

[0095] Reading of the real-time hardness value of the circulating water through an online water quality sensor.

[0096] In some embodiments, the external environmental data comprises at least the cumulative running time of the heat pump read from a system clock and a real-time electricity price signal obtained from a power grid interface, wherein the real-time electricity price signal comprises peak, flat, and valley.

[0097] In some embodiments, the multi-channel research and judgment component in the state switching research and judgment module 12 comprises a first research and judgment channel, and the execution steps of the first research and judgment channel comprise: The acid dew point temperature is calculated in real time based on the component concentration of the boiler flue gas.

[0098] An operation parameter sequence of the heat pump system is acquired, and a predicted minimum wall surface temperature of the evaporator and a corresponding most unfavorable temperature position are determined based on the operation parameter sequence and a pre-constructed twin simulation model.

[0099] In combination with a dynamic safety margin, it is calculated and determined 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 operation data and environmental fluctuation characteristics.

[0100] If the determination result is yes, a degradation state instruction is generated. If the determination result is no, no instruction is output.

[0101] 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: Based on the heat pump fluid data, a real-time heat exchange amount and a real-time heat exchange coefficient are calculated.

[0102] Based on the historical operation data of the heat pump system and a preset confidence threshold, a reference heat exchange coefficient is determined, and a real-time relative heat exchange efficiency is correspondingly calculated, and the performance attenuation degree is output.

[0103] In combination with a 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.

[0104] 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.

[0105] 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, and if not, no instruction is output.

[0106] In some embodiments, the execution steps of the second judgment channel further include: Based on the preset time window, a historical descaling demand index set is acquired, and a historical index mean and a historical index standard deviation of the historical descaling demand index set are statistically analyzed.

[0107] In combination with a preset mapping rule, a confidence level corresponding to the performance attenuation degree is matched and acquired.

[0108] 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.

[0109] In some embodiments, the priority control constraints include execution order constraints of the instructions and execution interval constraints when sequentially executed.

[0110] It should be understood that the embodiments mentioned in the specification focus on the 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 in the second embodiment. For the sake of brevity of the specification, no further expansion is made here.

[0111] 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. Meanwhile, the present application is not limited to the above-mentioned part of the embodiments, and it should be understood that those skilled in the art can still modify the technical solutions recorded in the above embodiments or make equivalent replacement for part of the 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 degraded switching research and judgment, dry burning and descaling switching research and judgment, and defrosting switching research and judgment, which are arranged in parallel; Combined 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 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.

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, Real-time acquisition of environmental data in the boiler system, including: 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 temperature and water 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.

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 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.

4. 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 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.

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 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 a preset confidence threshold, a reference heat exchange coefficient is determined, and a real-time relative heat exchange efficiency is correspondingly calculated, which is output as a performance attenuation degree; Combined with a 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.

6. 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 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 pressure change rate; It is determined whether the differential pressure change rate 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.

7. The method of switching the state of a boiler heat recovery heat pump in conjunction with environmental data according to claim 5, characterized by, According to the performance degradation degree configuration confidence constraint, the dynamic index threshold value of the scale removal demand index in the 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 according to a preset mapping rule; The Z value corresponding to the confidence level is used as a correction coefficient of the historical index standard deviation, and the historical index mean value and the historical index standard deviation are weighted to obtain the dynamic index threshold value.

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

9. 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 to 8, 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 according to 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 state competition arbitration of 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 actuator of the heat pump to switch the working state of the heat pump.

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