Heat recovery method and system based on rotary dehumidifier

Abstract

The invention relates to the technical field of heat recovery systems, in particular to a heat recovery method and system based on a rotary dehumidifier, and the method comprises the steps: collecting and processing a low-grade heat source discharged by the rotary dehumidifier to obtain real-time temperature data and flow data of low-grade hot water; the temperature data, the flow data and the specific heat capacity of water are combined to calculate the heat energy release amount of the low-grade hot water in unit time; calculating a heating demand value of a rotating wheel in the rotating wheel dehumidifier according to the current environment humidity; fitting the relevance between the heating demand value and the heat energy release amount to obtain a dynamic adjustment coefficient; and low-grade hot water is controlled to enter the high-temperature heat pump, working parameters of the high-temperature heat pump are adjusted based on the dynamic adjustment coefficient, and adjustment is conducted according to the temperature difference. Optimization of the heat recovery process is achieved through intelligent adjustment, the recovery efficiency of low-grade heat energy is improved, energy waste is reduced, and the overall energy efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heat recovery systems, and in particular to a heat recovery method and system based on a rotary dehumidifier. Background Art

[0002] With the global energy crisis and increasing environmental protection needs, how to efficiently utilize and recycle low-grade heat energy generated in industrial processes has become one of the current research hotspots. As an important air conditioning equipment, rotary dehumidifiers are widely used in refrigeration, air conditioning, ventilation and other fields. Rotary dehumidifiers can effectively regulate air humidity, but they also generate a large amount of low-grade heat energy during their operation. This heat energy is usually directly discharged into the environment, resulting in a waste of resources. Therefore, how to effectively recycle and utilize these low-grade heat sources has become the key to improving energy efficiency and reducing energy consumption.

[0003] At present, most recovery technologies for low-grade heat sources focus on directly using heat exchangers to transfer heat to other processes or heat storage media. However, due to the low exhaust temperature of the rotary dehumidifier and the fact that its working conditions fluctuate with changes in ambient humidity and temperature, existing heat recovery technologies are difficult to effectively adjust and match the heat requirements under different working conditions in real time. Especially during the dehumidification process of the rotary dehumidifier, the change in the amount of heat energy released is not stable, resulting in the inability of existing heat recovery methods to accurately control the supply of heat energy, resulting in low system efficiency or energy waste. Therefore, how to dynamically adjust the operating parameters of the heat recovery system according to changes in ambient humidity has become a technical problem that needs to be solved urgently.

[0004] In addition, existing heat recovery methods often ignore the intelligent control of heat recovery systems. Although some studies have tried to optimize the heat recovery process by adding control devices, most methods lack real-time feedback mechanisms and adaptive adjustment functions. Although the control strategy of high-temperature heat pumps can improve the heat recovery efficiency to a certain extent, the adjustment of its working parameters often relies on manual settings or simple control algorithms, which makes it difficult to achieve precise matching with ambient humidity and rotor heating requirements. Therefore, developing a heat recovery method based on real-time data feedback and intelligent adjustment, which can dynamically optimize the heat recovery process according to the actual operating status and environmental conditions of the rotary dehumidifier, is of great significance for improving overall energy efficiency. Summary of the invention

[0005] The main purpose of the present invention is to provide a heat recovery method and system based on a rotary dehumidifier, aiming to overcome the technical problem that the prior art cannot dynamically adjust the low-grade heat recovery system to match changing needs.

[0006] In order to achieve the above-mentioned invention problem, the present invention proposes a heat recovery method based on a rotary dehumidifier, the method comprising: The low-grade heat source discharged from the rotary dehumidifier is collected and processed, and the temperature and flow rate of the low-grade hot water are monitored and measured to obtain real-time temperature data and flow rate data of the low-grade hot water; The temperature data is multiplied by the flow data, and the calculated result is combined with the specific heat capacity of water to calculate the heat energy release per unit time of the low-grade hot water; Acquiring the current ambient humidity based on the rotary dehumidifier, and calculating the heating demand value of the rotary wheel in the rotary dehumidifier according to the current ambient humidity; Fitting the correlation between the heating demand value and the heat energy release amount, and calculating the dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount; Controlling the low-grade hot water to enter the high-temperature heat pump, adjusting the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient, and determining whether the temperature value of the hot water output by the high-temperature heat pump matches the heating demand value of the rotor; If they match, the hot water is delivered to the dehumidifier heating coil through the water pump; if they do not match, the working parameters of the high-temperature heat pump are adjusted until the output hot water temperature value matches the heating demand value of the wheel.

[0007] Furthermore, the step of collecting and processing the low-grade heat source discharged from the rotary dehumidifier, monitoring the temperature and measuring the flow rate of the low-grade hot water, and obtaining the real-time temperature data and flow rate data of the low-grade hot water includes: Based on the sensor, the flow velocity and temperature in the low-grade hot water pipeline are sampled and processed in real time to obtain the original flow velocity data and original temperature data; The original flow velocity data and the original temperature data are subjected to denoising to obtain denoised flow velocity data and temperature data: Acquire the inner diameter data of the pipeline, and correct the flow rate data based on the inner diameter data to obtain the real-time flow data of the low-grade hot water; Performing time series filtering on the temperature data, dividing the temperature data into a plurality of sub-data according to time points; A temperature trend analysis is performed on the plurality of sub-data to obtain a temperature change rate, and the temperature data is corrected according to the temperature change rate to obtain real-time temperature data.

[0008] Furthermore, the step of calculating the product of the temperature data and the flow data, and combining the calculation result with the specific heat capacity of water to calculate the heat energy release per unit time of the low-grade hot water includes: Performing time synchronization processing on the temperature data and the flow data to obtain a time matching sequence of the temperature data and the flow data, wherein each pair of temperature data and flow data corresponds to the same time period; Perform product calculation on each pair of temperature and flow data in the time matching sequence, and perform product calculation on the calculation result and the specific heat capacity of water to obtain instantaneous thermal energy corresponding to the flow and temperature in multiple time periods; The instantaneous heat energy corresponding to each time period is subjected to time integration processing to obtain the total heat energy release per unit time of the low-grade hot water, wherein the unit time is a preset minute or hour.

[0009] Furthermore, the step of obtaining the current ambient humidity based on the rotary dehumidifier and calculating the heating demand value of the rotary wheel in the rotary dehumidifier according to the current ambient humidity includes: Based on the humidity sensor of the rotary dehumidifier, current environmental humidity data is acquired, and the current environmental humidity data is preprocessed to obtain a humidity data sequence; Marking the timestamp of the humidity data sequence, and performing time-weighted average processing on the humidity data sequence based on the timestamp to obtain a weighted humidity value; Calculating the dehumidification load required by the rotating wheel under the current ambient humidity according to the difference between the weighted humidity value and the preset humidity threshold; Acquire the working parameters of the wheel, match and analyze the dehumidification load with the working state of the rotary dehumidifier, and evaluate the deviation between the demand value and the actual working state, wherein the working state at least includes the current surface temperature of the wheel; The deviation is used as a correction factor and input into a linear regression model to adjust the dehumidification load. When the linear regression model converges, a heating demand value of the wheel is output.

[0010] Furthermore, the step of fitting the correlation between the heating demand value and the heat energy release amount to calculate the dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount includes: Converting the heating demand value and the heat energy release amount into time series data to obtain a set of continuous time series data pairs of heating demand value and heat energy release amount; A sliding window with a preset number of bits is set, and the time series data is processed in a local time interval based on the sliding window to obtain local statistical characteristics of the heating demand value and the heat energy release in each sliding window; Performing curve fitting on the relationship between the heating demand value and the heat energy release amount in each of the sliding windows to obtain a fitting coefficient, and generating a fitting relationship formula for each sliding window based on the fitting coefficient; Calculating the fitting error between the heating demand and the heat energy release amount of each sliding window based on the fitting relationship to obtain fitting error data for each window; According to the fitting coefficient and the fitting error data, weighted processing is performed on the relationship between the heating demand and the heat energy release amount to obtain a weighted fitting relationship; Based on the weighted fitting relationship and the current ambient humidity value, a dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount is calculated.

[0011] Furthermore, the step of adjusting the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient and judging whether the temperature value of the hot water output by the high-temperature heat pump matches the heating demand value of the rotor includes: Collecting and processing the current working parameters of the high-temperature heat pump to obtain a numerical value set of the current working parameters; Normalizing the dynamic adjustment coefficient within a preset range to obtain a normalized adjustment coefficient; Comparing the normalized adjustment coefficient with the control range of each working parameter, obtaining the adjustment amplitude of the working parameter of the high-temperature heat pump according to the adjustment coefficient, and correcting and adjusting the working parameter according to the adjustment amplitude; The high-temperature heat pump is controlled to start based on the corrected and adjusted working parameters, and whether the hot water temperature value output by the high-temperature heat pump matches the heating demand value of the rotor is obtained based on a sensor.

[0012] Furthermore, if there is a mismatch, the step of adjusting the operating parameters of the high-temperature heat pump until the output hot water temperature value matches the heating demand value of the runner includes: Obtaining a difference between a temperature value of hot water output by the high-temperature heat pump and a heating demand value of the rotor, and obtaining an adjustment range of the high-temperature heat pump based on the difference; Performing preliminary adjustment processing on the working parameters of the high-temperature heat pump according to the adjustment range, wherein the preliminary adjustment processing at least includes adjusting the speed of the compressor to obtain a preliminary adjustment value of the speed of the compressor; Based on the preliminary adjustment value of the compressor speed, adjusting the condensing pressure of the high-temperature heat pump to obtain an adjustment value of the condensing pressure; According to the adjustment value of the condensing pressure, dynamically adjusting the refrigerant flow rate of the high-temperature heat pump to obtain an adjusted refrigerant flow rate value; According to the adjusted refrigerant flow value, the heat exchange efficiency is optimized to obtain the optimized heat exchange efficiency, and the heat exchange efficiency is associated with the compressor speed, the condensing pressure, and the refrigerant flow to obtain the final heat pump adjustment parameters; The operating parameters of the high-temperature heat pump are continuously adjusted based on the final heat pump adjustment parameters until the output hot water temperature value matches the heating demand value of the rotor.

[0013] The present invention also proposes a heat recovery system based on a rotary dehumidifier, comprising: A collection module is used to collect and process the low-grade heat source discharged from the rotary dehumidifier, monitor the temperature and measure the flow rate of the low-grade hot water, and obtain real-time temperature data and flow rate data of the low-grade hot water; A first calculation module is used to calculate the product of the temperature data and the flow data, and combine the calculation result with the specific heat capacity of water to calculate the heat energy release per unit time of the low-grade hot water; A second calculation module, configured to obtain the current ambient humidity based on the rotary dehumidifier, and calculate the heating demand value of the rotary dehumidifier according to the current ambient humidity; A correlation module, used for fitting the correlation between the heating demand value and the heat energy release amount, and calculating a dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount; a control module, used for controlling the low-grade hot water to enter the high-temperature heat pump, adjusting the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient, and determining whether the temperature value of the hot water output by the high-temperature heat pump matches the heating demand value of the runner; The adjustment module is used to control the hot water to be delivered to the dehumidifier heating coil through the water pump if there is a match; if there is no match, the working parameters of the high-temperature heat pump are adjusted until the output hot water temperature value matches the heating demand value of the wheel.

[0014] The present invention further provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented.

[0016] Beneficial effects: The heat recovery method and system based on the rotary dehumidifier proposed in this application solves the problems of low recovery efficiency and instability of low-grade heat sources in traditional heat recovery technology through real-time monitoring, intelligent adjustment and feedback mechanism. First, by monitoring the temperature and measuring the flow rate of low-grade hot water discharged by the rotary dehumidifier, the heat energy release amount is accurately calculated to provide accurate data for heat recovery. Furthermore, based on the relationship between the current ambient humidity and the rotary heating demand value, the working parameters of the high-temperature heat pump are dynamically adjusted to ensure that the hot water temperature output by the heat pump matches the heating demand value. Unlike the traditional method that relies on manual intervention or fixed setting parameters, this method can respond to environmental changes in real time, optimize the heat recovery process through intelligent adjustment, avoid energy waste caused by untimely or inaccurate adjustment, improve the recovery efficiency of low-grade heat energy, reduce energy waste, and improve system stability and accuracy through intelligent control, significantly improving overall energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the steps of a heat recovery method based on a rotary dehumidifier in one embodiment of the present invention; Figure 2 It is a schematic block diagram of the structure of a heat recovery system based on a rotary dehumidifier according to an embodiment of the present invention; Figure 3 is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "above", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when an element is said to be "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any module and all combinations of one or more associated listed items.

[0020] Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0021] Reference Figure 1 The embodiment of the present invention provides a heat recovery method based on a rotary dehumidifier, comprising the following steps: S1: collecting and processing the low-grade heat source discharged from the rotary dehumidifier, monitoring the temperature and measuring the flow rate of the low-grade hot water, and obtaining real-time temperature data and flow rate data of the low-grade hot water; In step S1, the water chiller of the rotary dehumidifier has a heat recovery function. When providing the necessary cold water for the dehumidifier, it also produces hot water at 50-55°C, that is, the air passes through the dehumidifier's rotor, and the hygroscopic material in the rotor absorbs moisture in the air and releases heat. As the rotor rotates, this heat is discharged as a low-grade heat source, which belongs to a low-temperature heat source. In order to achieve heat recovery, a hot water collection device is designed in the exhaust port or drainage system of the dehumidifier; temperature monitoring is achieved by installing a temperature sensor in the drainage pipe. The temperature sensor can monitor the temperature of the hot water flowing through the pipe in real time. Flow measurement is achieved by installing a flow meter in the pipe. These flow meters can measure the flow of hot water in cubic meters per hour or liters per minute. The flow meter detects the speed of the water flow in the pipe, combines the cross-sectional area of ​​the pipe to calculate the water flow, and transmits its data to the system.

[0022] S2: Calculate the product of the temperature data and the flow data, and combine the calculated result with the specific heat capacity of water to calculate the heat energy release per unit time of the low-grade hot water; In step S2, the flow data reflects the mass of water flowing through the system per unit time, and the temperature data reflects the actual thermal energy level of the water. The higher the temperature of the water, the greater the thermal energy it carries. By multiplying the temperature data with the flow data, a preliminary value representing the thermal energy in the water flow can be obtained. Specific heat capacity refers to the amount of heat required to increase the temperature of a unit mass of water by 1°C. The specific heat capacity of water is a constant, which is 4.18 kJ / kg·°C. Combining this specific heat capacity value with the product of the temperature and flow rate calculated previously, the thermal energy released by low-grade hot water per unit time can be accurately obtained. Specifically, the calculation formula for the amount of heat energy released is: Heat energy release = flow rate × specific heat capacity × temperature change, where the flow rate is expressed in terms of the amount of water flowing per unit time (kg / s), the specific heat capacity is a constant, and the temperature change refers to the degree of change in the temperature of the water flow as it flows through the system. Through this formula, the amount of heat energy released by low-grade hot water can be obtained.

[0023] S3: acquiring the current ambient humidity based on the rotary dehumidifier, and calculating the heating demand value of the rotary dehumidifier according to the current ambient humidity; In step S3, the ambient humidity refers to the current water vapor content in the air, which affects the dehumidification process of the rotary dehumidifier. Generally, when the humidity is high, the dehumidifier needs a stronger heating amount to increase the temperature of the rotor in order to effectively extract moisture from the air. On the contrary, when the humidity is low, the heating demand of the dehumidifier will be reduced, because the water vapor content in the air is low, the dehumidification effect of the rotor is sufficient, and additional heating is no longer required. Therefore, the change of the current ambient humidity directly affects the heating demand value of the rotor.

[0024] In step S3, the humidity data of the current environment is monitored in real time by a humidity sensor or other humidity measuring equipment. The humidity data reflects the concentration of water vapor in the air. Based on this real-time humidity data, a mathematical relationship model between humidity and heating power is established. This model takes into account multiple factors, including the heat exchange efficiency of the rotor, the air flow rate, and the temperature change of the environment. These factors jointly determine the amount of heating required by the rotor at different humidities. That is, the higher the humidity, the greater the heating demand value, and vice versa. In practical applications, a relationship curve can be obtained through experimental data to represent the relationship between humidity and heating demand. For example, when the humidity is high, in order to achieve the best dehumidification effect of the rotor, the rotor needs to be heated to a higher temperature to ensure that the rotor can absorb more moisture. If the humidity is low, the temperature demand of the rotor will be reduced accordingly, because the content of water vapor is low, and too much heat is not required to promote the adsorption of moisture. Specifically, the experiment sets different ambient humidity values ​​and selects multiple humidity levels for the experiment, such as 30%, 50%, 70% and 90%. These values ​​can represent the heating requirements under different humidity conditions. In each experiment, it is necessary to record data such as ambient humidity, air flow, and wheel temperature, and measure the heat energy input required by the wheel (i.e., heating demand value) to generate a series of experimental data, including heating requirements under different humidity and flow conditions. Through the accumulation of experimental data, a relationship curve between ambient humidity and heating demand can be established through a preset algorithm, and each set of experimental data can be organized into a table to list the heating demand value under each humidity condition. The expression of the preset algorithm can be: , where Q is the heating demand value, H is humidity, and a, b and c are regression coefficients. Through the above regression analysis, the optimal regression coefficients are obtained, thereby establishing a relationship curve between humidity and heating demand. It should be noted that the heating demand value is not a fixed value, but is adjusted with the real-time changes in ambient humidity to ensure that appropriate heat energy input can be provided under different environmental conditions, thereby optimizing the dehumidification effect of the rotor and avoiding overheating or underheating.

[0025] S4: fitting the correlation between the heating demand value and the heat energy release amount, and calculating the dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount; In step S4, the role of the dynamic adjustment coefficient is to quantify the influence of ambient humidity on the amount of heat energy released, which can be used to dynamically adjust the working parameters of the high-temperature heat pump. Since the change law of heat energy demand under different humidity levels is different, a mathematical model between ambient humidity and heat energy released by low-grade hot water is established based on experimental data or empirical models. The mathematical model can use a curve fitting method to calculate a function or coefficient to express the dynamic changes between ambient humidity, heating demand value and heat energy released extracted from historical data or real-time data. The model is mainly used to calculate an adjustment coefficient corresponding to the amount of heat energy released through real-time ambient humidity data. The calculation process of this coefficient usually uses data fitting algorithms, such as least squares method, weighted regression and other methods to further improve the accuracy of fitting. Specifically, when it comes to implementation, the calculation method of the dynamic adjustment coefficient can be the interaction of multiple variables, including but not limited to the operating status of the rotary dehumidifier, the current ambient humidity, temperature changes and flow fluctuations. Through real-time monitoring and processing of these factors, the dynamic adjustment coefficient will automatically adjust with the changes in environmental conditions to ensure that a high energy efficiency ratio is always maintained under different humidity conditions to avoid excessive heating or energy waste caused by humidity fluctuations.

[0026] S5: controlling the low-grade hot water to enter the high-temperature heat pump, adjusting the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient, and determining whether the temperature value of the hot water output by the high-temperature heat pump matches the heating demand value of the rotor; S6: If they match, the hot water is transported to the dehumidifier heating coil through the water pump; if they do not match, the working parameters of the high-temperature heat pump are adjusted until the output hot water temperature value matches the heating demand value of the rotor.

[0027] In steps S5 and S6, the low-grade hot water is controlled to enter the high-temperature heat pump. The function of the high-temperature heat pump is to heat the low-grade hot water through the compressor and the heat exchange system to increase its temperature so that the hot water can meet the heating demand of the rotary dehumidifier. The dynamic adjustment coefficient reflects the influence of the current ambient humidity on the amount of heat energy released. This coefficient is used to adjust the working parameters of the high-temperature heat pump. For example, when the ambient humidity is high, the heating demand of the rotary dehumidifier may be large, so the high-temperature heat pump is required to provide more heat; conversely, if the ambient humidity is low, less heat may be required. Therefore, the dynamic adjustment coefficient adjusts the working mode of the heat pump according to the real-time humidity conditions to ensure that the high-temperature heat pump can output the appropriate hot water temperature at any ambient humidity. After the adjustment of the high-temperature heat pump is completed, it is determined whether the hot water temperature output by the high-temperature heat pump matches the heating demand value of the rotary wheel. The matching here means that the hot water temperature output by the high-temperature heat pump should just meet the heating temperature required by the rotary heating coil. If the output hot water temperature is exactly the same as the demand value, it means that the heat recovery process has reached the optimal state, and the rotary dehumidifier can be effectively heated. The hot water is sent to the heating coil of the rotary dehumidifier through the water pump, and the heat energy is transferred to the rotor through heat exchange, thereby heating the rotor and maintaining the efficient operation of the rotary dehumidifier. If the hot water temperature output by the high-temperature heat pump fails to fully match the heating demand value of the rotor, that is, the temperature is too high or too low, it enters the adjustment state. In this case, the working parameters of the high-temperature heat pump are adjusted according to the temperature deviation until the output hot water temperature matches the heating demand value. The adjustment method can include adjusting the compressor speed of the heat pump, adjusting the circulation flow of the heat pump, optimizing the heat exchange efficiency, etc. Through these adjustments, it is ensured that the hot water temperature output by the heat pump accurately matches the heating demand of the rotary dehumidifier, and there will be no overheating or insufficient conditions, thereby avoiding the waste of energy or insufficient heating.

[0028] It is worth noting that in this embodiment, hot water at 50-55°C is heat exchanged through a high-temperature heat pump evaporator. After the high-pressure refrigerant in the evaporator absorbs the low-grade heat source of the hot water, it is compressed by the compressor and then exchanged with the hot water on the condenser side to release heat to produce 120°C hot water, which is then transported to the dehumidifier heating coil through a water pump to heat the dehumidifier. The dehumidifier does not require electric heating during operation.

[0029] In summary, this application solves the problems of inefficiency and instability of traditional heat recovery technology in the process of low-grade heat source recovery through real-time monitoring and intelligent control, thereby greatly improving energy utilization efficiency.

[0030] In one embodiment, the step of collecting and processing the low-grade heat source discharged from the rotary dehumidifier, monitoring the temperature and measuring the flow rate of the low-grade hot water, and obtaining the real-time temperature data and flow rate data of the low-grade hot water includes: Based on the sensor, the flow velocity and temperature in the low-grade hot water pipeline are sampled and processed in real time to obtain the original flow velocity data and original temperature data; The original flow velocity data and the original temperature data are subjected to denoising to obtain denoised flow velocity data and temperature data: Acquire the inner diameter data of the pipeline, and correct the flow rate data based on the inner diameter data to obtain the real-time flow data of the low-grade hot water; Performing time series filtering on the temperature data, dividing the temperature data into a plurality of sub-data according to time points; A temperature trend analysis is performed on the plurality of sub-data to obtain a temperature change rate, and the temperature data is corrected according to the temperature change rate to obtain real-time temperature data.

[0031] In the above embodiment, the real-time flow data and temperature data of low-grade hot water are obtained to realize the effective collection and processing of the low-grade heat source discharged by the rotary dehumidifier. Low-grade hot water refers to hot water with a relatively low temperature. A hot water collection device can be directly designed in the exhaust port or drainage system of the dehumidifier, and the flow rate and temperature in the low-grade hot water pipeline can be sampled in real time through the sensor. Specifically, the sensor is installed in the pipeline and can monitor its flow rate and temperature in real time in the flowing hot water. The flow rate sensor obtains the original flow rate data by detecting the speed of the water flow, and the temperature sensor measures the instantaneous temperature of the hot water to obtain the original temperature data. The original flow rate data and the original temperature data are denoised to remove the interference signal and retain only the effective information representing the real flow rate and temperature state. The denoising method can include mean filtering, Kalman filtering or median filtering, etc., through which the original data can be effectively smoothed and the influence of external noise can be reduced. The flow rate data and temperature data after denoising are more in line with the actual situation than the original data. After obtaining the denoised data, the inner diameter data of the pipe is obtained. Combined with the denoised flow rate data, the flow rate data can be converted into actual flow data using fluid mechanics formulas. For example, if the inner diameter of the pipe is large, the water flow rate may be slower, while a smaller pipe may result in a higher flow rate. By correcting the flow rate data for the inner diameter, the real hot water flow data can be obtained. Since the temperature data itself may fluctuate over time, a single temperature reading often cannot reflect the overall temperature change trend of the hot water. Therefore, the temperature data is subjected to time series filtering, and short-term fluctuations are eliminated by smoothing, thereby highlighting the long-term temperature change trend. This processing can use a bandpass filter or a low-pass filter to remove high-frequency noise according to the characteristics of the time series, making the temperature data more stable and convenient for subsequent analysis. The filtered temperature data is divided into multiple sub-data, which are divided based on different time points. By independently analyzing the data of different time periods, the law of temperature change can be understood more carefully. Each sub-data represents the temperature change information over a period of time, which can reflect the trend of temperature increase or decrease. Based on multiple sub-data, trend analysis is performed on the temperature data, and the temperature change rate is calculated to determine the speed of temperature change over time. The calculation of the rate of change can be done by numerical derivation, or by using regression analysis and other methods to fit the changing trend of the temperature data. The analysis of the temperature change rate can reveal the dynamic changes of the hot water system, such as whether the system is heating or whether the hot water temperature is stable. A large temperature change rate may mean that the hot water system is in a more intense heat exchange process, while a small change rate may indicate that the system is operating stably, and the temperature data is corrected based on the analysis results of the temperature change rate.

[0032] In one embodiment, the step of calculating the product of the temperature data and the flow data, and combining the calculation result with the specific heat capacity of water to calculate the heat energy release per unit time of the low-grade hot water includes: Performing time synchronization processing on the temperature data and the flow data to obtain a time matching sequence of the temperature data and the flow data, wherein each pair of temperature data and flow data corresponds to the same time period; Perform product calculation on each pair of temperature and flow data in the time matching sequence, and perform product calculation on the calculation result and the specific heat capacity of water to obtain instantaneous thermal energy corresponding to the flow and temperature in multiple time periods; The instantaneous heat energy corresponding to each time period is subjected to time integration processing to obtain the total heat energy release per unit time of the low-grade hot water, wherein the unit time is a preset minute or hour.

[0033] In the above embodiment, the temperature data and flow data are processed in time synchronization, and each pair of matching temperature data and flow data represents the temperature state and flow rate of low-grade hot water in a specific time period, which together determine the heat energy release capacity of hot water in the time period. For each temperature and flow data in the time matching sequence, a product calculation is performed. The temperature data and the flow data are multiplied to obtain the instantaneous heat energy under the unit flow rate. Since the heat energy release of hot water is proportional to the product of temperature and flow rate, the higher the temperature and the greater the flow rate, the greater the heat energy released. For example, if the temperature at a certain moment is 40°C and the flow rate is 10 cubic meters per hour, then the heat energy at that moment is equal to 40°C multiplied by 10 cubic meters per hour, that is, 400 units of heat energy, and then the calculated result is multiplied by the specific heat capacity of water to obtain the actual heat energy at each moment. After obtaining the heat energy at each moment, the instantaneous heat energy corresponding to each time period is accumulated through time integration processing, so as to obtain the total heat energy release of low-grade hot water in the preset time unit. For example, if the temperature and flow data are collected as one data point per hour, then within an hour, the total heat energy released within the hour is obtained by summing up the instantaneous heat energy at each moment. For example, assuming that the instantaneous heat energy in each time period within a certain hour is 400 kJ, 500 kJ, and 600 kJ, respectively, after time integration, the total heat energy released is 1500 kJ. According to the preset time unit (such as minutes or hours).

[0034] In one embodiment, the step of obtaining the current ambient humidity based on the rotary dehumidifier and calculating the heating demand value of the rotary wheel in the rotary dehumidifier according to the current ambient humidity includes: Based on the humidity sensor of the rotary dehumidifier, current environmental humidity data is acquired, and the current environmental humidity data is preprocessed to obtain a humidity data sequence; Marking the timestamp of the humidity data sequence, and performing time-weighted average processing on the humidity data sequence based on the timestamp to obtain a weighted humidity value; Calculating the dehumidification load required by the rotating wheel under the current ambient humidity according to the difference between the weighted humidity value and the preset humidity threshold; Acquire the working parameters of the wheel, match and analyze the dehumidification load with the working state of the rotary dehumidifier, and evaluate the deviation between the demand value and the actual working state, wherein the working state at least includes the current surface temperature of the wheel; The deviation is used as a correction factor and input into a linear regression model to adjust the dehumidification load. When the linear regression model converges, a heating demand value of the wheel is output.

[0035] In the above embodiment, the humidity sensor of the rotary dehumidifier obtains the humidity data of the current environment, and the humidity change trend of the environment is evaluated through these data to form a humidity data sequence, and the timestamp of each data point is marked. The function of the timestamp is to bind the humidity data to time and reflect the humidity change over time. Based on these timestamps, the humidity data sequence is subjected to time-weighted averaging. Through weighted averaging, the system can pay more attention to the recent humidity changes, while also taking into account the influence of historical data, thereby obtaining a weighted humidity value. The dehumidification load required by the rotary dehumidifier under the current environmental humidity is calculated by using the difference between the weighted humidity value and the preset humidity threshold. The humidity threshold is preset based on the design parameters and operating conditions of the rotary dehumidifier, indicating the humidity level that the rotary dehumidifier should reach under a specific environment. When the difference between the weighted humidity value and the preset humidity threshold is large, a larger dehumidification load needs to be provided, otherwise, a smaller load may be required. The working parameters of the rotary dehumidifier are obtained, including information such as the surface temperature of the rotary dehumidifier. When the surface temperature of the rotary dehumidifier is low, its dehumidification efficiency is high and less heating energy is required; when the surface temperature of the rotary dehumidifier is high, the dehumidification efficiency is low and more heating is required to maintain a suitable working state. By matching the dehumidification load with the working state of the rotary dehumidifier, the deviation between the required dehumidification load and the actual working state is evaluated, and a correction factor is obtained through the evaluation of the deviation. This correction factor reflects the difference between the current working state of the rotary dehumidifier and the actual demand, and is input into the linear regression model. The linear regression model is used to adjust the dehumidification load according to the actual deviation to ensure the accuracy of the heating demand. After a certain number of iterations and adjustments, the model will eventually converge and output a heating demand value.

[0036] Furthermore, the calculation expression of the above embodiment is: ,in, is the rotor heating demand value, which indicates the heating power value (unit: watt) required by the rotor under the current ambient humidity, i.e. the final calculated heating demand; is the time weighting coefficient, which is a weighting factor used to control the influence of the weighted humidity value in the sum; N is the number of humidity data points, which is the number of humidity data points collected in a certain period of time (unit: pieces). For example, if the humidity sensor records data once per second, there may be 3600 data points in one hour; is the time weighting factor, which is the time weighting factor for each humidity data point The associated time weighting factor. It will decrease over time in the form of , where λ is the decay constant, controlling the weighted decay speed, t is the current time, is the data point time; is a humidity data point, representing the time point The collected humidity value is expressed as a percentage (%) or relative humidity; is the surface temperature of the rotor in degrees Celsius (°C); is the reference temperature, i.e. the surface temperature of the rotor under standard or ideal conditions, which is determined by the design parameters of the equipment or the target temperature set by the system, and the unit is Celsius (°C); β is the temperature adjustment coefficient. The higher the surface temperature of the rotor, the lower the dehumidification efficiency, and more heating is required to maintain normal operation. It is a constant; is the humidity difference adjustment, which represents the difference between the weighted humidity value and the preset humidity threshold, and the adjusted amount after correction; γ is the humidity difference adjustment coefficient, which is used to adjust the effect of the humidity difference on the heating demand. According to the sensitivity of the system to humidity changes, adjust Contribution to final heating demand.

[0037] In one embodiment, the step of fitting the correlation between the heating demand value and the heat energy release amount to calculate the dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount includes: Converting the heating demand value and the heat energy release amount into time series data to obtain a set of continuous time series data pairs of heating demand value and heat energy release amount; A sliding window with a preset number of bits is set, and the time series data is processed in a local time interval based on the sliding window to obtain local statistical characteristics of the heating demand value and the heat energy release in each sliding window; Performing curve fitting on the relationship between the heating demand value and the heat energy release amount in each of the sliding windows to obtain a fitting coefficient, and generating a fitting relationship formula for each sliding window based on the fitting coefficient; Calculating the fitting error between the heating demand and the heat energy release amount of each sliding window based on the fitting relationship to obtain fitting error data for each window; According to the fitting coefficient and the fitting error data, weighted processing is performed on the relationship between the heating demand and the heat energy release amount to obtain a weighted fitting relationship; Based on the weighted fitting relationship and the current ambient humidity value, a dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount is calculated.

[0038] In the above embodiment, the heating demand value and the heat energy release amount are converted into time series data, which are arranged in chronological order to form a set of continuous data pairs. A sliding window with a preset number of bits is set, and the time series data is processed in a local time interval based on the sliding window. The sliding window technology is used to process time series data. The sliding window will slide in the time series data, taking a fixed number of data points each time to calculate the local statistical features in the time interval. These statistical features may include the mean, variance, minimum, maximum, etc. of the data, reflecting the changing trend between the heating demand and the heat energy release in the local time period. For example, if the window is set to 10 time points, each sliding window will calculate the statistical features of the heating demand value and the heat energy release amount in these 10 time points, thereby capturing the local correlation between the heating demand and the heat energy release amount. After obtaining the local statistical features, the relationship between the heating demand value and the heat energy release amount in each sliding window is curve fitted, and the best fitting relationship between the heating demand and the heat energy release amount is found through a mathematical model, which can be a linear regression, nonlinear regression or other forms of mathematical functions. In this embodiment, a fitting coefficient is obtained by curve fitting, thereby generating a fitting relationship for each sliding window. This fitting relationship reflects the quantitative relationship between the heating demand and the amount of heat energy released within a specific time interval. After the fitting is completed, the fitting error between the heating demand and the amount of heat energy released in each sliding window is calculated based on the fitting relationship. The fitting error refers to the difference between the actual data and the fitting model. Based on the weighted fitting relationship and the current ambient humidity value, the dynamic adjustment coefficient of the current ambient humidity to the amount of heat energy released is calculated. The adjustment coefficient reflects the impact of humidity changes on the amount of heat energy released. According to the different ambient humidity, the ratio between the heating demand and the amount of heat energy released is dynamically adjusted. For example, in the case of high humidity, more heat energy release may be required to achieve the same heating effect, while in the case of low humidity, the amount of heat energy released may need to be reduced.

[0039] Furthermore, the calculation expression of the above embodiment is: ,in, It represents the amount of heat energy released after dynamic adjustment at time point t; represents the summation operation on the sliding window w, and the weighted results of multiple sliding windows are summed up; Represents the weighted coefficient of the sliding window w. Each time window has a different contribution to the final heat energy release, and each window is assigned a weight coefficient; It represents the function value obtained by fitting the heating demand and heat energy release within the time window w. The fitting function obtained by the model (such as linear regression, nonlinear regression, etc.) reflects the mathematical relationship between these data points. represents the heating demand value at time point t−w+1; represents the amount of heat energy released at time point t−w+1; Represents the fitting error weighting coefficient of the sliding window w; represents the fitting error of the sliding window w; Represents the ambient humidity (relative humidity) at time point t; It represents the dynamic adjustment coefficient of the ambient humidity to the amount of heat energy released. For example, when the humidity is high, it may be necessary to increase the heat energy to compensate for the heat loss caused by the humidity. The value of ββ represents the intensity of this adjustment. In an example, assuming that the current time t is a certain moment, the ambient humidity =70%, and 5 sliding windows are set. The weighting coefficient and fitting error of each window are as follows: =0.2, =0.3, =0.25, =0.15, =0.1, =0.1, =0.2, =0.15, =0.3, =0.25, the above parameters are calculated through the expression, and the fitting result is: =0.8, =0.7, =0.75, =0.6, =0.65. Finally, assuming that the dynamic adjustment coefficient β=0.2 (for example, for every 10% increase in humidity, the heat release is adjusted by 20%), the final heat release can be calculated using the above formula. .

[0040] In one embodiment, the step of adjusting the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient and judging whether the hot water temperature value output by the high-temperature heat pump matches the heating demand value of the rotor includes: Collecting and processing the current working parameters of the high-temperature heat pump to obtain a numerical value set of the current working parameters; Normalizing the dynamic adjustment coefficient within a preset range to obtain a normalized adjustment coefficient; Comparing the normalized adjustment coefficient with the control range of each working parameter, obtaining the adjustment amplitude of the working parameter of the high-temperature heat pump according to the adjustment coefficient, and correcting and adjusting the working parameter according to the adjustment amplitude; The high-temperature heat pump is controlled to start based on the corrected and adjusted working parameters, and whether the hot water temperature value output by the high-temperature heat pump matches the heating demand value of the rotor is obtained based on a sensor.

[0041] In the above embodiment, by collecting the current working parameters of the high-temperature heat pump, a numerical set is formed, including the operating status of the heat pump, the power of the compressor, the fluid flow rate, the temperature setting value, etc. The dynamic adjustment coefficient is normalized within a preset range. The purpose of normalization is to convert the original dynamic adjustment coefficient into a value within the standard range, so that the adjustment coefficient can adapt to different working environments and equipment characteristics. In the process of normalization, the original coefficient is scaled or translated to conform to a fixed range (for example, 0 to 1), so as to ensure that the application effect of the adjustment coefficient under different high-temperature heat pumps and environmental conditions is consistent when the adjustment is performed, and it is compared with the control range of each working parameter. The control range is set according to the design specifications and performance requirements of the high-temperature heat pump, and defines the possible minimum and maximum values ​​of each working parameter. For example, the power of the compressor may vary within a certain range, and the set value of the hot water temperature also has a reasonable range of variation. According to the normalized adjustment coefficient, it is determined which working parameters need to be adjusted under the current conditions and how much the adjustment is. This process actually determines the intensity of the working parameter correction based on the size of the adjustment coefficient. The start of the high-temperature heat pump is controlled according to the corrected parameters. At this time, the high-temperature heat pump will operate under the new working parameters, monitor the temperature of the hot water in real time based on the sensor, and compare it with the heating demand value of the rotor to determine whether the hot water temperature has reached the required temperature value. If it is found that the hot water temperature does not reach the preset value, the current working parameters will be re-evaluated and further adjustments will be made.

[0042] In one embodiment, if there is no match, the step of adjusting the operating parameters of the high-temperature heat pump until the output hot water temperature value matches the heating demand value of the rotor includes: Obtaining a difference between a temperature value of hot water output by the high-temperature heat pump and a heating demand value of the rotor, and obtaining an adjustment range of the high-temperature heat pump based on the difference; Performing preliminary adjustment processing on the working parameters of the high-temperature heat pump according to the adjustment range, wherein the preliminary adjustment processing at least includes adjusting the speed of the compressor to obtain a preliminary adjustment value of the speed of the compressor; Based on the preliminary adjustment value of the compressor speed, adjusting the condensing pressure of the high-temperature heat pump to obtain an adjustment value of the condensing pressure; According to the adjustment value of the condensing pressure, dynamically adjusting the refrigerant flow rate of the high-temperature heat pump to obtain an adjusted refrigerant flow rate value; According to the adjusted refrigerant flow value, the heat exchange efficiency is optimized to obtain the optimized heat exchange efficiency, and the heat exchange efficiency is associated with the compressor speed, the condensing pressure, and the refrigerant flow to obtain the final heat pump adjustment parameters; The operating parameters of the high-temperature heat pump are continuously adjusted based on the final heat pump adjustment parameters until the output hot water temperature value matches the heating demand value of the rotor.

[0043] In the above embodiment, the output hot water temperature value of the high-temperature heat pump is obtained in real time, and the temperature value is compared with the heating demand value of the rotor to obtain the difference value between the two. The difference value reflects the deviation between the hot water temperature and the heating demand of the rotor, and the range to be adjusted is determined according to the difference value. The larger the difference value, the more the output temperature of the high-temperature heat pump deviates from the demand of the rotor, and the adjustment range of the heat pump will be determined on this basis. This adjustment range determines which working parameters should be adjusted next and the adjustment range. For example, if the difference value is large, the system may take a larger adjustment range to adjust the hot water temperature to the target value as soon as possible. According to this adjustment range, the working parameters of the high-temperature heat pump are preliminarily adjusted, starting with the compressor speed, because the compressor speed directly affects the heating capacity of the heat pump. By adjusting the compressor speed, the operating efficiency of the heat pump can be changed, thereby affecting the temperature of the output hot water. After the preliminary adjustment of the compressor speed, the condensing pressure is further adjusted. The condensing pressure is related to the heating efficiency of the heat pump. By adjusting the condensing pressure according to the adjustment value of the compressor speed, the operating state of the heat pump can be further optimized. The increase in the condensing pressure represents an increase in the working temperature of the heat pump, and vice versa, it may cause the temperature of the heat pump to decrease. When the refrigerant flow is optimized, the heat exchange efficiency is processed to increase the hot water temperature. When the optimized heat exchange efficiency is combined with the compressor speed, condensing pressure, and refrigerant flow, the final heat pump adjustment parameters are obtained. Based on the final heat pump adjustment parameters, the system will continuously fine-tune the working parameters of the high-temperature heat pump until the output hot water temperature fully matches the heating demand value of the runner. During this process, the various control parameters of the heat pump (such as compressor speed, condensing pressure, refrigerant flow, etc.) will continue to be fine-tuned according to the feedback signal to ensure that the system can maintain the best working state at any time. Through this closed-loop feedback control mechanism, it can flexibly respond to different working environments and load changes, and always maintain the match between hot water temperature and heating demand.

[0044] In one embodiment, the step of associating the heat exchange efficiency with the compressor speed, the condensing pressure, and the refrigerant flow rate to obtain the final heat pump adjustment parameters includes: According to the relationship between the heat exchange efficiency and the compressor speed, condensing pressure, and refrigerant flow rate, a multi-dimensional mathematical model of heat exchange efficiency and working parameters is established to obtain the target optimization parameter space and obtain the preliminary boundary value of the optimization space; Performing local subdivision processing on the preliminary boundary value of the optimization space to obtain a more accurate working parameter adjustment range, and calculating a possible working parameter adjustment sequence based on the range to obtain a preliminary adjustment sequence; According to the preliminary adjustment sequence, the compressor speed is adjusted to obtain an adjustment value of the compressor speed, and the condensing pressure is further optimized and adjusted according to the adjustment value to obtain an adjustment value of the condensing pressure; Based on the adjustment value of the condensing pressure, the refrigerant flow rate is dynamically adjusted by an iterative method to obtain an adjusted refrigerant flow rate value, and the heat exchange efficiency is quickly estimated by flow adjustment to obtain an estimated heat exchange efficiency change rate; According to the heat exchange efficiency change rate, the step size and the number of iterations of the adjustment parameters are adjusted to optimize the convergence speed and accuracy during the adjustment process to obtain a final optimized convergence value; The compressor speed, condensing pressure and refrigerant flow are precisely adjusted according to the optimized convergence value to obtain the final adjustment parameters of the heat pump, and the balanced frequency modulation control of the motor is continuously adjusted according to the adjustment parameters to make the output hot water temperature value of the high-temperature heat pump match the rotor heating demand value.

[0045] Reference Figure 2 , a heat recovery system based on a rotary dehumidifier, comprising: The collection module 100 is used to collect and process the low-grade heat source discharged from the rotary dehumidifier, monitor the temperature and measure the flow rate of the low-grade hot water, and obtain the real-time temperature data and flow rate data of the low-grade hot water; The first calculation module 200 is used to calculate the product of the temperature data and the flow data, and combine the calculation result with the specific heat capacity of water to calculate the heat energy release per unit time of the low-grade hot water; A second calculation module 300 is used to obtain the current ambient humidity based on the rotary dehumidifier, and calculate the heating demand value of the rotary dehumidifier according to the current ambient humidity; The correlation module 400 is used to fit the correlation between the heating demand value and the heat energy release amount, and calculate the dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount; The control module 500 is used to control the low-grade hot water to enter the high-temperature heat pump, adjust the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient, and determine whether the temperature value of the hot water output by the high-temperature heat pump matches the heating demand value of the rotor; The adjustment module 600 is used to control the hot water to be delivered to the dehumidifier heating coil through the water pump if there is a match; if there is no match, adjust the working parameters of the high-temperature heat pump until the output hot water temperature value matches the heating demand value of the wheel.

[0046] In this embodiment, through real-time monitoring, intelligent adjustment and feedback mechanism, the problems of low recovery efficiency and instability of low-grade heat sources in traditional heat recovery technology are solved. First, by monitoring the temperature and measuring the flow rate of low-grade hot water discharged by the rotary dehumidifier, the heat energy release amount is accurately calculated to provide accurate data for heat recovery. Furthermore, based on the relationship between the current ambient humidity and the rotary heating demand value, the working parameters of the high-temperature heat pump are dynamically adjusted to ensure that the hot water temperature output by the heat pump matches the heating demand value. Unlike the traditional method that relies on manual intervention or fixed setting parameters, this method can respond to environmental changes in real time, optimize the heat recovery process through intelligent adjustment, avoid energy waste caused by untimely or inaccurate adjustment, improve the recovery efficiency of low-grade heat energy, reduce energy waste, and improve system stability and accuracy through intelligent control, significantly improving overall energy efficiency.

[0047] Reference Figure 3 In an embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the database of the heat recovery system. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a heat recovery method based on a rotary dehumidifier is implemented.

[0048] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a heat recovery method based on a rotary dehumidifier is implemented, comprising the steps of: collecting and processing low-grade heat sources discharged from the rotary dehumidifier, monitoring the temperature and measuring the flow rate of the low-grade hot water, and obtaining real-time temperature data and flow rate data of the low-grade hot water; multiplying the temperature data by the flow rate data, and combining the calculation result with the specific heat capacity of water to calculate the amount of heat energy released per unit time of the low-grade hot water; obtaining the current ambient humidity based on the rotary dehumidifier, and calculating the heat energy released per unit time of the low-grade hot water according to the current ambient humidity; Humidity calculates the heating demand value of the wheel in the rotary dehumidifier; fits the correlation between the heating demand value and the heat energy release amount, and calculates the dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount; controls the low-grade hot water to enter the high-temperature heat pump, adjusts the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient, and determines whether the hot water temperature value output by the high-temperature heat pump matches the heating demand value of the wheel; if it matches, controls the hot water to be transported to the dehumidifier heating coil through a water pump; if it does not match, adjusts the working parameters of the high-temperature heat pump until the output hot water temperature value matches the heating demand value of the wheel.

[0049] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0050] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A heat recovery method based on a rotary dehumidifier, characterized in that: include: The low-grade heat source discharged from the rotary dehumidifier is collected and processed, and the temperature and flow rate of the low-grade hot water are monitored and measured to obtain real-time temperature data and flow rate data of the low-grade hot water; The temperature data is multiplied by the flow data, and the calculated result is combined with the specific heat capacity of water to calculate the heat energy release per unit time of the low-grade hot water; Acquiring the current ambient humidity based on the rotary dehumidifier, and calculating the heating demand value of the rotary wheel in the rotary dehumidifier according to the current ambient humidity; Fitting the correlation between the heating demand value and the heat energy release amount, and calculating the dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount; Controlling the low-grade hot water to enter the high-temperature heat pump, adjusting the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient, and determining whether the temperature value of the hot water output by the high-temperature heat pump matches the heating demand value of the rotor; If they match, the hot water is delivered to the dehumidifier heating coil through the water pump; if they do not match, the working parameters of the high-temperature heat pump are adjusted until the output hot water temperature value matches the heating demand value of the wheel.

2. The heat recovery method based on the rotary dehumidifier according to claim 1 is characterized in that: The step of collecting and processing the low-grade heat source discharged from the rotary dehumidifier, monitoring the temperature and measuring the flow rate of the low-grade hot water, and obtaining real-time temperature data and flow rate data of the low-grade hot water includes: Based on the sensor, the flow velocity and temperature in the low-grade hot water pipeline are sampled and processed in real time to obtain the original flow velocity data and original temperature data; The original flow velocity data and the original temperature data are subjected to denoising to obtain denoised flow velocity data and temperature data: Acquire the inner diameter data of the pipeline, and correct the flow rate data based on the inner diameter data to obtain the real-time flow data of the low-grade hot water; Performing time series filtering on the temperature data, dividing the temperature data into a plurality of sub-data according to time points; A temperature trend analysis is performed on the plurality of sub-data to obtain a temperature change rate, and the temperature data is corrected according to the temperature change rate to obtain real-time temperature data.

3. The heat recovery method based on the rotary dehumidifier according to claim 1, characterized in that: The step of calculating the product of the temperature data and the flow data, and combining the calculation result with the specific heat capacity of water to calculate the heat energy release per unit time of the low-grade hot water includes: Performing time synchronization processing on the temperature data and the flow data to obtain a time matching sequence of the temperature data and the flow data, wherein each pair of temperature data and flow data corresponds to the same time period; Perform product calculation on each pair of temperature and flow data in the time matching sequence, and perform product calculation on the calculation result and the specific heat capacity of water to obtain instantaneous thermal energy corresponding to the flow and temperature in multiple time periods; The instantaneous heat energy corresponding to each time period is subjected to time integration processing to obtain the total heat energy release per unit time of the low-grade hot water, wherein the unit time is a preset minute or hour.

4. The heat recovery method based on the rotary dehumidifier according to claim 1, characterized in that: The step of obtaining the current ambient humidity based on the rotary dehumidifier and calculating the heating demand value of the rotary wheel in the rotary dehumidifier according to the current ambient humidity comprises: Based on the humidity sensor of the rotary dehumidifier, current environmental humidity data is acquired, and the current environmental humidity data is preprocessed to obtain a humidity data sequence; Marking the timestamp of the humidity data sequence, and performing time-weighted average processing on the humidity data sequence based on the timestamp to obtain a weighted humidity value; Calculating the dehumidification load required by the rotating wheel under the current ambient humidity according to the difference between the weighted humidity value and the preset humidity threshold; Acquire the working parameters of the wheel, match and analyze the dehumidification load with the working state of the rotary dehumidifier, and evaluate the deviation between the demand value and the actual working state, wherein the working state at least includes the current surface temperature of the wheel; The deviation is used as a correction factor and input into a linear regression model to adjust the dehumidification load. When the linear regression model converges, a heating demand value of the wheel is output.

5. The heat recovery method based on the rotary dehumidifier according to claim 1, characterized in that: The step of fitting the correlation between the heating demand value and the heat energy release amount to calculate the dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount includes: Converting the heating demand value and the heat energy release amount into time series data to obtain a set of continuous time series data pairs of heating demand value and heat energy release amount; A sliding window with a preset number of bits is set, and the time series data is processed in a local time interval based on the sliding window to obtain local statistical characteristics of the heating demand value and the heat energy release in each sliding window; Performing curve fitting on the relationship between the heating demand value and the heat energy release amount in each of the sliding windows to obtain a fitting coefficient, and generating a fitting relationship formula for each sliding window based on the fitting coefficient; Calculating the fitting error between the heating demand and the heat energy release amount of each sliding window based on the fitting relationship to obtain fitting error data for each window; According to the fitting coefficient and the fitting error data, weighted processing is performed on the relationship between the heating demand and the heat energy release amount to obtain a weighted fitting relationship; Based on the weighted fitting relationship and the current ambient humidity value, a dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount is calculated.

6. The heat recovery method based on the rotary dehumidifier according to claim 1, characterized in that: The step of adjusting the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient and judging whether the temperature value of the hot water output by the high-temperature heat pump matches the heating demand value of the rotor comprises: Collecting and processing the current working parameters of the high-temperature heat pump to obtain a numerical value set of the current working parameters; Normalizing the dynamic adjustment coefficient within a preset range to obtain a normalized adjustment coefficient; Comparing the normalized adjustment coefficient with the control range of each working parameter, obtaining the adjustment amplitude of the working parameter of the high-temperature heat pump according to the adjustment coefficient, and correcting and adjusting the working parameter according to the adjustment amplitude; The high-temperature heat pump is controlled to start based on the corrected and adjusted working parameters, the hot water temperature value output by the high-temperature heat pump is obtained based on a sensor, and it is determined whether the hot water temperature value matches the heating demand value of the wheel.

7. The heat recovery method based on a rotary dehumidifier according to claim 1, characterized in that: If there is no match, the step of adjusting the working parameters of the high-temperature heat pump until the output hot water temperature value matches the heating demand value of the runner includes: Obtaining a difference between a temperature value of hot water output by the high-temperature heat pump and a heating demand value of the rotor, and obtaining an adjustment range of the high-temperature heat pump based on the difference; Performing preliminary adjustment processing on the working parameters of the high-temperature heat pump according to the adjustment range, wherein the preliminary adjustment processing at least includes adjusting the speed of the compressor to obtain a preliminary adjustment value of the speed of the compressor; Based on the preliminary adjustment value of the compressor speed, adjusting the condensing pressure of the high-temperature heat pump to obtain an adjustment value of the condensing pressure; According to the adjustment value of the condensing pressure, dynamically adjusting the refrigerant flow rate of the high-temperature heat pump to obtain an adjusted refrigerant flow rate value; According to the adjusted refrigerant flow value, the heat exchange efficiency is optimized to obtain the optimized heat exchange efficiency, and the heat exchange efficiency is associated with the compressor speed, the condensing pressure, and the refrigerant flow to obtain the final heat pump adjustment parameters; The operating parameters of the high-temperature heat pump are continuously adjusted based on the final heat pump adjustment parameters until the output hot water temperature value matches the heating demand value of the rotor.

8. A heat recovery system based on a rotary dehumidifier, characterized in that: include: A collection module is used to collect and process the low-grade heat source discharged from the rotary dehumidifier, monitor the temperature and measure the flow rate of the low-grade hot water, and obtain real-time temperature data and flow rate data of the low-grade hot water; A first calculation module is used to calculate the product of the temperature data and the flow data, and combine the calculation result with the specific heat capacity of water to calculate the heat energy release per unit time of the low-grade hot water; A second calculation module, configured to obtain the current ambient humidity based on the rotary dehumidifier, and calculate the heating demand value of the rotary dehumidifier according to the current ambient humidity; A correlation module, used for fitting the correlation between the heating demand value and the heat energy release amount, and calculating a dynamic adjustment coefficient of the current ambient humidity to the heat energy release amount; a control module, used for controlling the low-grade hot water to enter the high-temperature heat pump, adjusting the working parameters of the high-temperature heat pump based on the dynamic adjustment coefficient, and determining whether the temperature value of the hot water output by the high-temperature heat pump matches the heating demand value of the runner; The adjustment module is used to control the hot water to be delivered to the dehumidifier heating coil through the water pump if there is a match; if there is no match, the working parameters of the high-temperature heat pump are adjusted until the output hot water temperature value matches the heating demand value of the wheel.

9. A computer device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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