Fresh air ventilator intelligent temperature control system with heat recovery function

Abstract

The invention belongs to the technical field of fresh air ventilator management and control, and particularly relates to a fresh air ventilator intelligent temperature control system with a heat recovery function, which comprises a multi-parameter intelligent sensing module, a self-adaptive temperature control module, an efficient heat recovery module, a fan rotating speed adjusting module, a bypass valve opening adjusting module and a user terminal, the self-adaptive temperature control module analyzes and generates a corresponding temperature control strategy based on indoor and outdoor environment monitoring data, and automatically adjusts and controls the heat recovery mode of the fresh air ventilator, the rotating speed of an air inlet fan, the rotating speed of an exhaust fan and the opening degree of a bypass valve based on the temperature control strategy. The operation management and control difficulty of the fresh air ventilator is reduced, the indoor environment comfort is guaranteed, the operation condition of the fresh air ventilator is analyzed through the intelligent tracking analysis module, reason investigation and analysis are conducted when tracking abnormal signals are generated, and the fresh air ventilator is checked and maintained according to needs. And efficient, stable, safe and energy-saving operation of the fresh air ventilator is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of fresh air ventilator control, and specifically to an intelligent temperature control system for a fresh air ventilator with a heat recovery function. Background Art

[0002] A fresh air ventilator is an environmental protection electrical appliance integrating ventilation, air change, and purification. Through mechanical air supply and exhaust, it forms a continuous fresh air flow field, discharges the dirty air in the room, introduces the filtered fresh air, and keeps the indoor air clean and fresh. It not only improves the indoor air quality but also helps with energy conservation and consumption reduction. It is an indispensable air treatment equipment in modern buildings;

[0003] Currently, when controlling a fresh air ventilator, control data is mainly input manually, and the fresh air ventilator operates according to the input control data. It is difficult to automatically execute corresponding temperature control strategies according to the indoor and outdoor environmental conditions and achieve operation tracking and alarm, and it is also unable to assist in diagnosing and outputting the abnormal operation of the fresh air ventilator, which is not conducive to ensuring the safe, stable, efficient, and energy-saving operation of the fresh air ventilator, and the level of intelligence and automation is low;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent temperature control system for a fresh air ventilator with a heat recovery function, which solves the problems in the prior art that it is difficult to automatically generate and execute corresponding temperature control strategies and achieve operation tracking and alarm, and it is also unable to assist in diagnosing and outputting the abnormal operation of the fresh air ventilator, which is not conducive to ensuring the safe, stable, efficient, and energy-saving operation of the fresh air ventilator, and the level of intelligence and automation is low.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An intelligent temperature control system for a fresh air ventilator with a heat recovery function includes a multi-parameter intelligent perception module, an adaptive temperature control module, an efficient heat recovery module, a fan speed regulation module, a bypass valve opening regulation module, and a user terminal; the multi-parameter intelligent perception module integrates a temperature and humidity sensor, a PM2.5 sensor, and a CO2 sensor, monitors the indoor and outdoor environment, and transmits the real-time monitoring data to the adaptive temperature control module in combination with Internet of Things technology;

[0008] The adaptive temperature control module analyzes the real-time monitoring data sent by the multi-parameter intelligent perception module to generate corresponding temperature control strategies. The high-efficiency heat recovery module automatically adjusts the heat recovery mode of the fresh air ventilator based on the temperature control strategy and conducts heat recovery based on the corresponding heat recovery mode. The fan speed adjustment module adjusts the speeds of the intake fan and the exhaust fan in the fresh air ventilator based on the temperature control strategy. The bypass valve opening adjustment module adjusts the opening of the bypass valve based on the temperature control strategy; the user terminal displays the real-time monitoring data, temperature control strategies, and control execution information, and is used to issue manual instructions for the operation of the fresh air ventilator.

[0009] Further, the adaptive temperature control module is communicatively connected to the intelligent tracking analysis module. The adaptive temperature control module sends the generated temperature control strategies to the intelligent tracking analysis module. The intelligent tracking analysis module analyzes the operating conditions of the fresh air ventilator within a unit time, generates a tracking qualified signal or a tracking abnormal signal through the analysis, and sends the tracking qualified signal or the tracking abnormal signal to the user terminal. When the user terminal receives the tracking abnormal signal, it issues a corresponding warning.

[0010] Further, the specific analysis process of the intelligent tracking analysis module includes:

[0011] Collect the speeds of the intake fan and the exhaust fan in the fresh air ventilator. Calculate the absolute value of the difference between the speed of the intake fan and the current corresponding standard intake air speed to obtain the intake rotation detection value. Calculate the absolute value of the difference between the speed of the exhaust fan and the current corresponding standard exhaust air speed to obtain the exhaust rotation detection value. And calculate the absolute value of the difference between the opening of the bypass valve of the fresh air ventilator and the current corresponding standard opening to obtain the valve opening detection value;

[0012] Calculate the weighted sum of the intake rotation detection value, the exhaust rotation detection value, and the valve opening detection value to obtain the self-control value of the ventilator. Compare the self-control value of the ventilator with the preset self-control threshold of the ventilator. If the self-control value of the ventilator exceeds the preset self-control threshold of the ventilator, it is determined that the fresh air ventilator is in a non-qualified self-control state;

[0013] Obtain the total duration of the fresh air ventilator being in a non-qualified self-control state within a unit time and mark it as the self-control warning time value. And calculate the average value of all the self-control values of the fresh air ventilator corresponding within a unit time to obtain the self-control decision value. Compare the self-control warning time value and the self-control decision value with the preset self-control warning time threshold and the preset self-control decision threshold respectively. If the self-control warning time value or the self-control decision value exceeds the corresponding preset threshold, a tracking abnormal signal is generated;

[0014] If both the automatic control warning value and the automatic control decision value do not exceed the corresponding preset thresholds, the energy consumption decision value of the fresh air ventilator per unit time is obtained, and the energy consumption decision value is numerically compared with the preset energy consumption decision threshold. If the energy consumption decision value exceeds the preset energy consumption decision threshold, a tracking anomaly signal is generated; if the energy consumption decision value does not exceed the preset energy consumption decision threshold, a tracking qualified signal is generated.

[0015] Further, the intelligent tracking analysis module is communicatively connected to the energy consumption decision evaluation module. The energy consumption decision evaluation module analyzes the energy consumption performance of the fresh air ventilator per unit time, obtains the energy consumption decision value accordingly, and sends the energy consumption decision value to the intelligent tracking analysis module.

[0016] Further, the specific analysis process of the energy consumption decision evaluation module is as follows:

[0017] During a unit time, several detection periods are set, the actual energy consumption data and the theoretical energy consumption data of the fresh air ventilator in the corresponding detection periods are collected, and the excess value of the actual energy consumption data compared with the corresponding theoretical energy consumption data is marked as the consumption excess detection value; the consumption excess detection value is numerically compared with the preset consumption excess detection threshold. If the consumption excess detection value exceeds the preset consumption excess detection threshold, the corresponding detection period is marked as a consumption warning period;

[0018] The number of consumption warning periods per unit time is obtained and the ratio is calculated with the total number of detection periods to obtain the consumption warning analysis value, and the consumption excess detection values of all consumption warning periods are averaged to obtain the consumption excess analysis value, and the maximum consumption excess detection value per unit time is marked as the consumption excess amplitude value; the energy consumption decision value is obtained by performing a weighted sum calculation on the consumption warning analysis value, the consumption excess analysis value, and the consumption excess amplitude value.

[0019] Further, the intelligent tracking analysis module is communicatively connected to the abnormal auxiliary analysis output module. The intelligent tracking analysis module sends the tracking qualified signal to the abnormal auxiliary analysis output module. When the abnormal auxiliary analysis output module receives the tracking qualified signal, it performs an auxiliary diagnosis analysis on the operation abnormality of the fresh air ventilator, generates an abnormal auxiliary diagnosis alarm signal or an abnormal auxiliary diagnosis qualified signal through analysis, and sends the abnormal auxiliary diagnosis alarm signal or the abnormal auxiliary diagnosis qualified signal to the user terminal. When the user terminal receives the abnormal auxiliary diagnosis alarm signal, it issues a corresponding warning.

[0020] Further, the specific analysis process of the abnormal auxiliary analysis output module is as follows:

[0021] Obtain the noise curve, vibration frequency curve, and vibration amplitude curve of the fresh air ventilator during operation per unit time. Establish a rectangular coordinate system with time as the X-axis and the noise decibel value as the Y-axis, and place the noise curve in the first quadrant of the rectangular coordinate system, with the starting point of the noise curve located on the Y-axis; draw a noise determination ray parallel to the X-axis and with endpoints on the Y-axis in the first quadrant of the rectangular coordinate system, obtain the area of the region enclosed by the part of the noise curve above the Y-axis and the Y-axis and mark it as the noise overtest value. Similarly, obtain the vibration frequency overtest value and the vibration amplitude overtest value;

[0022] Calculate the auxiliary output value by performing weighted summation of the noise overtest value, vibration frequency overtest value, and vibration amplitude overtest value, and compare the auxiliary output value with the preset auxiliary output threshold. If the auxiliary output value exceeds the preset auxiliary output threshold, generate an abnormal auxiliary diagnosis alarm signal; if the auxiliary output value does not exceed the preset auxiliary output threshold, generate an abnormal auxiliary diagnosis qualified signal.

[0023] Furthermore, the abnormal auxiliary analysis output module sends the abnormal auxiliary diagnosis qualified signal to the filtering diagnosis output module. When the filtering diagnosis output module receives the abnormal auxiliary diagnosis qualified signal, it generates a diagnosis qualified signal or a filtering hidden danger signal through analysis, and sends the diagnosis qualified signal or the filtering hidden danger signal to the user terminal. When the user terminal receives the filtering hidden danger signal, it issues a corresponding warning.

[0024] Furthermore, the specific analysis process of the filtering diagnosis output module includes:

[0025] Collect the time of the previous inspection and cleaning of the filter and mark it as the inspection and cleaning time, and mark the interval duration between the current time and the inspection and cleaning time as the inspection and cleaning interval value; and mark the operation duration of the fresh air ventilator and the average concentration of dust particles in the outside air entering during the interval duration between the current time and the inspection and cleaning time as the filtering required time value and the filtering dust collection value respectively;

[0026] Calculate the filter hidden danger value by performing weighted summation of the inspection and cleaning interval value, filtering required time value, and filtering dust collection value, and compare the filter hidden danger value with the preset filter hidden danger threshold. If the filter hidden danger value exceeds the preset filter hidden danger threshold, generate a filtering hidden danger signal.

[0027] Furthermore, if the filter hidden danger value does not exceed the preset filter hidden danger threshold, collect the concentration of dust particles in the air sent in and mark it as the particle detection value, and compare the particle detection value with the preset particle detection threshold. If the particle detection value exceeds the preset particle detection threshold, it is determined that the filter is in a filtering obstruction state;

[0028] Obtain the duration of the filter in the filtering obstruction state within a unit time and mark it as the filtering resistance value. Compare the filtering resistance value with a preset filtering resistance threshold value numerically. If the filtering resistance value exceeds the preset filtering resistance threshold value, generate a filtering anomaly signal; if the filtering resistance value does not exceed the preset filtering resistance threshold value, mark the maximum single continuous duration in the filtering obstruction state within a unit time as the obstruction holding value, and mark the average concentration of dust particles in the air sent in within a unit time as the particle performance value;

[0029] Calculate the diagnostic output value by performing a weighted sum of the filtering resistance value, the obstruction holding value, and the particle performance value. Compare the diagnostic output value with a preset diagnostic output threshold value numerically. If the diagnostic output value exceeds the preset diagnostic output threshold value, generate a filtering hidden danger signal; if the diagnostic output value does not exceed the preset diagnostic output threshold value, generate a diagnostic qualified signal.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. In the present invention, through the adaptive temperature control module, analyze the indoor and outdoor environmental monitoring data and generate corresponding temperature control strategies, automatically adjust and control the heat recovery mode, the intake fan speed, the exhaust fan speed, and the bypass valve opening degree of the fresh air ventilator based on the temperature control strategies, reduce the operation management and control difficulty of the fresh air ventilator and ensure the comfort of the indoor environment, and analyze the operation status of the fresh air ventilator through the intelligent tracking analysis module, and make corresponding optimization and improvement measures when generating a tracking anomaly signal to ensure the efficient, stable, safe and energy-saving operation of the fresh air ventilator;

[0032] 2. In the present invention, through the abnormal auxiliary analysis output module, perform auxiliary diagnosis and analysis on the operation anomalies of the fresh air ventilator, pause the operation of the fresh air ventilator and perform inspection and maintenance when generating an abnormal auxiliary diagnosis alarm signal, reduce the operation safety hidden danger of the fresh air ventilator, is beneficial to improving the service life of the fresh air ventilator and ensuring its safe operation, and when generating an abnormal auxiliary diagnosis qualified signal, analyze the condition of the filter in the fresh air ventilator through the filter diagnosis output module, replace or clean the filter in the fresh air ventilator when generating a filtering hidden danger signal, ensure its filtering effect on the input air, is beneficial to maintaining the indoor air quality, with high intelligent level and small supervision difficulty. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the drawings;

[0034] Figure 1 It is the system block diagram of the first embodiment in the present invention;

[0035] Figure 2 It is the system block diagram of the second and third embodiments in the present invention. Detailed implementation manners

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] Embodiment 1: As Figure 1 shown, an intelligent temperature control system for a fresh air ventilator with a heat recovery function proposed by the present invention includes a multi-parameter intelligent perception module, an adaptive temperature control module, an efficient heat recovery module, a fan speed regulation module, a bypass valve opening regulation module, an intelligent tracking and analysis module, and a user terminal;

[0038] Among them, the multi-parameter intelligent perception module integrates temperature and humidity sensors, PM2.5 sensors, CO2 sensors, etc., monitors the indoor and outdoor environments, and transmits the real-time monitoring data to the adaptive temperature control module in combination with the Internet of Things technology;

[0039] The adaptive temperature control module analyzes based on the real-time monitoring data sent by the multi-parameter intelligent perception module (mainly through the PID-fuzzy control algorithm analysis), generates corresponding temperature control strategies, and sends the generated temperature control strategies to the efficient heat recovery module, the fan speed regulation module, the bypass valve opening regulation module, the intelligent tracking and analysis module, the intelligent tracking and analysis module, and the user terminal;

[0040] The efficient heat recovery module automatically adjusts the heat recovery mode of the fresh air ventilator (including sensible heat recovery mode, total heat recovery mode, etc.) based on the temperature control strategy, and performs heat recovery based on the corresponding heat recovery mode. The fan speed regulation module adjusts the speeds of the intake fan and the exhaust fan in the fresh air ventilator based on the temperature control strategy. The bypass valve opening regulation module adjusts the opening of the bypass valve based on the temperature control strategy to achieve automatic adjustment and control of the fresh air ventilator, reduce the operation and management difficulty of the fresh air ventilator, and improve its intelligent level;

[0041] The user terminal displays the real-time monitoring data, temperature control strategies, and control execution information, and is used to issue manual instructions for the operation of the fresh air ventilator to achieve manual adjustment and control of the fresh air ventilator, and further ensure the efficient operation of the fresh air ventilator.

[0042] The intelligent tracking and analysis module analyzes the operating conditions of the fresh air ventilator within a unit time, generates a tracking qualified signal or a tracking abnormal signal through the analysis, and sends the tracking qualified signal or the tracking abnormal signal to the user terminal. When the user terminal receives the tracking abnormal signal, it issues a corresponding warning to remind the user to promptly conduct a cause investigation and analysis and inspect and repair the fresh air ventilator as needed, ensuring the efficient, stable, safe, and energy-saving operation of the fresh air ventilator. The specific analysis process of the intelligent tracking and analysis module is as follows:

[0043] The rotational speed of the intake fan and the rotational speed of the exhaust fan in the fresh air ventilator are collected. The difference between the rotational speed of the intake fan and the corresponding standard intake rotational speed at present is calculated and the absolute value is taken to obtain the intake rotation detection value. The difference between the rotational speed of the exhaust fan and the corresponding standard exhaust rotational speed at present is calculated and the absolute value is taken to obtain the exhaust rotation detection value. Also, the difference between the bypass valve opening degree of the fresh air ventilator and the corresponding standard opening degree at present is calculated and the absolute value is taken to obtain the valve opening detection value;

[0044] The self-control value of the ventilator is obtained by performing a weighted summation calculation on the intake rotation detection value, the exhaust rotation detection value, and the valve opening detection value. That is, corresponding preset weight coefficients are assigned to the intake rotation detection value, the exhaust rotation detection value, and the valve opening detection value, and the intake rotation detection value, the exhaust rotation detection value, and the valve opening detection value are respectively multiplied by the corresponding preset weight coefficients, and the sum value of the three product results is marked as the self-control value of the ventilator. Moreover, the larger the value of the self-control value of the ventilator, the worse the overall real-time execution status of the temperature control strategy;

[0045] The self-control value of the ventilator is numerically compared with the preset self-control threshold of the ventilator. If the self-control value of the ventilator exceeds the preset self-control threshold of the ventilator, indicating that the overall real-time execution status of the temperature control strategy is poor, it is determined that the fresh air ventilator is in a non-qualified self-control state;

[0046] The total duration of the fresh air ventilator being in a non-qualified self-control state within a unit time is obtained and marked as the self-control warning time value. Also, the average value of all the self-control values of the fresh air ventilator corresponding to the unit time is calculated to obtain the self-control decision value. The self-control warning time value and the self-control decision value are numerically compared with the preset self-control warning time threshold and the preset self-control decision threshold respectively. If the self-control warning time value or the self-control decision value exceeds the corresponding preset threshold, indicating that the control execution performance of the fresh air ventilator is poor, a tracking abnormal signal is generated;

[0047] If both the automatic control warning value and the automatic control decision value do not exceed the corresponding preset thresholds, it indicates that the control execution performance of the fresh air ventilator is good. Then, obtain the energy consumption decision value of the fresh air ventilator per unit time, and compare the energy consumption decision value with the preset energy consumption decision threshold. If the energy consumption decision value exceeds the preset energy consumption decision threshold, it indicates that the energy consumption performance of the fresh air ventilator per unit time is poor, and then generate a tracking anomaly signal; if the energy consumption decision value does not exceed the preset energy consumption decision threshold, it indicates that the energy consumption performance of the fresh air ventilator per unit time is good, and then generate a tracking qualified signal.

[0048] It should be noted that the intelligent tracking analysis module is communicatively connected to the energy consumption decision evaluation module. The energy consumption decision evaluation module analyzes the energy consumption performance of the fresh air ventilator per unit time, obtains the energy consumption decision value accordingly, and sends the energy consumption decision value to the intelligent tracking analysis module. It can not only reasonably judge the energy consumption performance of the fresh air ventilator per unit time, but also provide data support for the analysis process of the intelligent tracking analysis module to ensure the comprehensiveness of its analysis and the accuracy of the results. The specific analysis process of the energy consumption decision evaluation module is as follows:

[0049] Set several detection time periods within a unit time, and the duration of each detection time period is the same; collect the actual energy consumption data and theoretical energy consumption data of the fresh air ventilator in the corresponding detection time periods, and mark the excess value of the actual energy consumption data compared with the corresponding theoretical energy consumption data as the consumption excess detection value; compare the consumption excess detection value with the preset consumption excess detection threshold. If the consumption excess detection value exceeds the preset consumption excess detection threshold, it indicates that the energy consumption in the corresponding detection time period is too large, and then mark the corresponding detection time period as a consumption warning time period;

[0050] Obtain the number of consumption warning time periods within a unit time and calculate the ratio with the total number of detection time periods to obtain the consumption warning analysis value, and calculate the average value of the consumption excess detection values of all consumption warning time periods to obtain the consumption excess analysis value, and mark the largest consumption excess detection value within a unit time as the consumption excess amplitude value;

[0051] Calculate the energy consumption decision value by weighted summation of the consumption warning analysis value, the consumption excess analysis value, and the consumption excess amplitude value, that is, assign corresponding preset weight coefficients to the consumption warning analysis value, the consumption excess analysis value, and the consumption excess amplitude value in advance, multiply the consumption warning analysis value, the consumption excess analysis value, and the consumption excess amplitude value by the corresponding preset weight coefficients respectively, and mark the sum value of the three product results as the energy consumption decision value; moreover, the larger the value of the energy consumption decision value, the more abnormal the comprehensive energy consumption performance of the fresh air ventilator per unit time.

[0052] Embodiment 2: As Figure 2As shown, the difference between this embodiment and the first embodiment is that the intelligent tracking and analysis module is communicatively connected to the abnormal auxiliary analysis and output module. The intelligent tracking and analysis module sends a tracking qualified signal to the abnormal auxiliary analysis and output module. When the abnormal auxiliary analysis and output module receives the tracking qualified signal, it conducts an auxiliary diagnosis and analysis on the abnormal operation of the fresh air ventilator, and generates an abnormal auxiliary diagnosis alarm signal or an abnormal auxiliary diagnosis qualified signal through the analysis;

[0053] And it sends the abnormal auxiliary diagnosis alarm signal or the abnormal auxiliary diagnosis qualified signal to the user terminal. When the user terminal receives the abnormal auxiliary diagnosis alarm signal, it issues a corresponding warning to remind the user to pause the operation of the fresh air ventilator and conduct inspection and maintenance, reducing the potential safety hazards in the operation of the fresh air ventilator, which is beneficial to improving the service life of the fresh air ventilator and ensuring its safe operation. The specific analysis process of the abnormal auxiliary analysis and output module is as follows:

[0054] Obtain the noise curve, vibration frequency curve, and vibration amplitude curve of the fresh air ventilator during operation within a unit time. Establish a rectangular coordinate system with time as the X-axis and the noise decibel value as the Y-axis, and place the noise curve in the first quadrant of the rectangular coordinate system, and the starting point of the noise curve is located on the Y-axis; In the first quadrant of the rectangular coordinate system, draw a noise determination ray parallel to the X-axis and with endpoints on the Y-axis, obtain the area of the region enclosed by the part of the noise curve above the Y-axis and the Y-axis and mark it as the noise over-measurement value. Similarly, obtain the vibration frequency over-measurement value and the vibration amplitude over-measurement value (i.e., also analyze by substituting into the coordinate system);

[0055] Calculate the auxiliary output value by performing a weighted sum calculation on the noise over-measurement value, vibration frequency over-measurement value, and vibration amplitude over-measurement value. That is, assign corresponding preset weight coefficients to the noise over-measurement value, vibration frequency over-measurement value, and vibration amplitude over-measurement value in advance, multiply the noise over-measurement value, vibration frequency over-measurement value, and vibration amplitude over-measurement value by the corresponding preset weight coefficients respectively, and sum the three sets of product results to obtain the auxiliary output value accordingly; Moreover, the larger the value of the auxiliary analysis value, the greater the probability that the fresh air ventilator is operating abnormally;

[0056] Compare the auxiliary output value with the preset auxiliary output threshold. If the auxiliary output value exceeds the preset auxiliary output threshold, it indicates that the probability of the fresh air ventilator operating abnormally is relatively large, and an abnormal auxiliary diagnosis alarm signal is generated; If the auxiliary output value does not exceed the preset auxiliary output threshold, it indicates that the probability of the fresh air ventilator operating abnormally is relatively small, and an abnormal auxiliary diagnosis qualified signal is generated.

[0057] Embodiment Three: As Figure 2As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the abnormal auxiliary analysis output module sends the abnormal auxiliary diagnosis qualified signal to the filtering diagnosis output module. When the filtering diagnosis output module receives the abnormal auxiliary diagnosis qualified signal, it analyzes to generate a diagnosis qualified signal or a filtering hidden danger signal, and sends the diagnosis qualified signal or the filtering hidden danger signal to the user terminal;

[0058] When the user terminal receives the filtering hidden danger signal, it issues a corresponding warning to remind the user to replace or clean the filter in the fresh air ventilator in time to ensure its filtering effect on the input air, which is beneficial to maintaining the indoor air quality. The specific analysis process of the filtering diagnosis output module is as follows:

[0059] Collect the moment of the previous inspection and cleaning of the filter and mark it as the inspection and cleaning moment, and mark the interval duration between the current moment and the inspection and cleaning moment as the inspection and cleaning interval value; and mark the operation duration of the fresh air ventilator and the average concentration of dust particles in the outside air entering during the interval duration between the current moment and the inspection and cleaning moment as the filtering required time value and the filtering dust collection value respectively;

[0060] Calculate the filter hidden danger value by weighted summing the inspection and cleaning interval value, the filtering required time value and the filtering dust collection value, that is, assign corresponding preset weight coefficients to the inspection and cleaning interval value, the filtering required time value and the filtering dust collection value in advance, multiply the inspection and cleaning interval value, the filtering required time value and the filtering dust collection value by the corresponding preset weight coefficients respectively, and mark the sum value of the three product results as the filter hidden danger value;

[0061] Moreover, the larger the value of the filter hidden danger value, the more urgently it is necessary to check and clean the filter in time to ensure its filtering effect; compare the filter hidden danger value with the preset filter hidden danger threshold. If the filter hidden danger value exceeds the preset filter hidden danger threshold, it indicates that the filter needs to be checked and cleaned in time to ensure its filtering effect, and then a filtering hidden danger signal is generated.

[0062] Furthermore, if the filter hidden danger value does not exceed the preset filter hidden danger threshold, collect the concentration of dust particles in the incoming air and mark it as the particle detection value, compare the particle detection value with the preset particle detection threshold. If the particle detection value exceeds the preset particle detection threshold, it indicates that the real-time filtering effect of the filter is poor, and then it is judged that the filter is in a filtering obstacle state;

[0063] Obtain the duration of the filter in the filtering obstacle state per unit time and mark it as the filtering obstacle time value, compare the filtering obstacle time value with the preset filtering obstacle time threshold. If the filtering obstacle time value exceeds the preset filtering obstacle time threshold, it indicates that the filtering performance of the fresh air ventilator for the input air per unit time is poor, and then a filtering abnormal signal is generated;

[0064] If the filtering resistance time value does not exceed the preset filtering resistance time threshold, mark the maximum single continuous duration in the filtering obstruction state within a unit time as the obstruction holding value, and mark the average concentration of dust particles in the air sent in within a unit time as the particle performance value;

[0065] Calculate the diagnostic output value by performing a weighted sum calculation on the filtering resistance time value, the obstruction holding value, and the particle performance value. That is, assign corresponding preset weight coefficients to the filtering resistance time value, the obstruction holding value, and the particle performance value in advance, multiply the filtering resistance time value, the obstruction holding value, and the particle performance value by the corresponding preset weight coefficients respectively, and mark the sum value of the three sets of product results as the diagnostic output value; moreover, the larger the value of the diagnostic output value, the worse the overall process performance of the fresh air ventilator within a unit time;

[0066] Compare the diagnostic output value with the preset diagnostic output threshold. If the diagnostic output value exceeds the preset diagnostic output threshold, indicating that the overall filtering performance of the fresh air ventilator for the input air within a unit time is poor, then generate a filtering hazard signal; if the diagnostic output value does not exceed the preset diagnostic output threshold, indicating that the overall filtering performance of the fresh air ventilator for the input air within a unit time is good, then generate a diagnostic qualified signal.

[0067] The working principle of the present invention: When in use, the indoor and outdoor environment is monitored by the multi-parameter intelligent sensing module, the adaptive temperature control module analyzes based on the real-time monitoring data and generates corresponding temperature control strategies, the high-efficiency heat recovery module, the fan speed adjustment module, and the bypass valve opening adjustment module adjust and control the heat recovery mode, the inlet fan speed, the exhaust fan speed, and the bypass valve opening of the fresh air ventilator based on the temperature control strategy, reducing the operation management difficulty of the fresh air ventilator and ensuring the comfort of the indoor environment, and analyzing the operating conditions of the fresh air ventilator within a unit time through the intelligent tracking analysis module. When a tracking abnormal signal is generated, remind the user to conduct a cause investigation and analysis and check and repair the fresh air ventilator as needed, ensuring the efficient, stable, safe, and energy-saving operation of the fresh air ventilator, with high levels of intelligence and automation.

[0068] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not elaborate on all details, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principle and practical application of the present invention, enabling those skilled in the relevant technical field to understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An intelligent temperature control system for a fresh air ventilator with heat recovery function, characterized in that: It includes a multi-parameter intelligent sensing module, an adaptive temperature control module, a high-efficiency heat recovery module, a fan speed adjustment module, a bypass valve opening adjustment module and a user terminal; the multi-parameter intelligent sensing module monitors the indoor and outdoor environment, and transmits the real-time monitoring data to the adaptive temperature control module in combination with the Internet of Things technology; The adaptive temperature control module analyzes the real-time monitoring data sent by the multi-parameter intelligent sensing module and generates a corresponding temperature control strategy. The high-efficiency heat recovery module automatically adjusts the heat recovery mode of the fresh air ventilation machine based on the temperature control strategy, and performs heat recovery based on the corresponding heat recovery mode. The fan speed adjustment module adjusts the speed of the intake fan and the exhaust fan in the fresh air ventilation machine based on the temperature control strategy. The bypass valve opening adjustment module adjusts the bypass valve opening based on the temperature control strategy. The user terminal displays the real-time monitoring data, temperature control strategy and control execution information, and is used to issue manual instructions for the operation of the fresh air ventilation machine.

2. The intelligent temperature control system for fresh air ventilator with heat recovery function according to claim 1 is characterized in that: The adaptive temperature control module is communicatively connected to the intelligent tracking and analysis module. The adaptive temperature control module sends the generated temperature control strategy to the intelligent tracking and analysis module. The intelligent tracking and analysis module analyzes the operating status of the fresh air ventilation machine per unit time, generates a tracking qualified signal or a tracking abnormal signal through analysis, and sends the tracking qualified signal or the tracking abnormal signal to the user terminal.

3. The intelligent temperature control system for fresh air ventilator with heat recovery function according to claim 2 is characterized in that: The specific analysis process of the intelligent tracking analysis module includes: The total time that the fresh air ventilator is in an unqualified state of automatic control within a unit time is obtained and marked as the automatic control alarm value, and the automatic control decision value is obtained by averaging all the automatic control values ​​of the fresh air ventilator corresponding to the unit time. If the automatic control alarm value or the automatic control decision value exceeds the corresponding preset threshold, a tracking abnormality signal is generated; If neither the automatic control alarm value nor the automatic control decision value exceeds the corresponding preset threshold value, the energy consumption decision value of the fresh air ventilation fan per unit time is obtained. If the energy consumption decision value exceeds the preset energy consumption decision threshold, a tracking abnormality signal is generated; if the energy consumption decision value does not exceed the preset energy consumption decision threshold, a tracking qualified signal is generated.

4. The intelligent temperature control system for fresh air ventilator with heat recovery function according to claim 3 is characterized in that: The intelligent tracking and analysis module is communicatively connected to the energy consumption decision evaluation module. The energy consumption decision evaluation module analyzes the energy consumption performance of the fresh air ventilator per unit time, obtains an energy consumption decision value accordingly, and sends the energy consumption decision value to the intelligent tracking and analysis module.

5. The intelligent temperature control system for fresh air ventilator with heat recovery function according to claim 4 is characterized in that: The specific analysis process of the energy consumption decision evaluation module is as follows: a number of detection periods are set within a unit time, and if the over-consumption detection value exceeds the preset over-consumption detection threshold, the corresponding detection period is marked as a consumption warning period; The number of alarm consumption periods per unit time is obtained and the ratio thereof is calculated with the total number of detection periods to obtain the alarm consumption analysis value, and the average of the excess consumption detection values ​​of all alarm consumption periods is calculated to obtain the excess consumption analysis value, and the maximum excess consumption detection value per unit time is marked as the excess consumption amplitude value; The energy consumption decision value is calculated by weighted summing up the consumption warning analysis value, the excessive consumption analysis value and the excessive consumption amplitude value.

6. The intelligent temperature control system for fresh air ventilator with heat recovery function according to claim 1, characterized in that: The intelligent tracking and analysis module is communicatively connected to the abnormal auxiliary analysis output module. The intelligent tracking and analysis module sends a tracking qualified signal to the abnormal auxiliary analysis output module. When the abnormal auxiliary analysis output module receives the tracking qualified signal, it performs auxiliary diagnosis and analysis on the operation abnormality of the fresh air ventilation machine, and sends the abnormal auxiliary diagnosis alarm signal or the abnormal auxiliary diagnosis qualified signal to the user terminal.

7. The intelligent temperature control system for fresh air ventilator with heat recovery function according to claim 6 is characterized in that: The specific analysis process of the abnormal auxiliary analysis output module is as follows: the noise curve, vibration frequency curve and vibration amplitude curve of the fresh air ventilation machine during operation are obtained in unit time, and the auxiliary output value is calculated by weighted summation of the noise excess value, vibration frequency excess value and amplitude excess value. If the auxiliary output value exceeds the preset auxiliary output threshold, an abnormal auxiliary diagnosis alarm signal is generated; if the auxiliary output value does not exceed the preset auxiliary output threshold, an abnormal auxiliary diagnosis qualified signal is generated.

8. The intelligent temperature control system for fresh air ventilator with heat recovery function according to claim 7 is characterized in that: The abnormal auxiliary analysis output module sends the abnormal auxiliary diagnosis qualified signal to the filtering diagnosis output module. When the filtering diagnosis output module receives the abnormal auxiliary diagnosis qualified signal, it generates a diagnosis qualified signal or a filtering hidden danger signal through analysis, and sends the diagnosis qualified signal or the filtering hidden danger signal to the user terminal.

9. The intelligent temperature control system for fresh air ventilator with heat recovery function according to claim 8, characterized in that: The specific analysis process of the filter diagnosis output module is as follows: the filter hidden danger value is obtained by weighted summing up the inspection and cleaning interval value, the filtration time value and the filtration dust collection value. If the filter hidden danger value exceeds the preset filter hidden danger threshold, a filter hidden danger signal is generated.

10. The intelligent temperature control system for fresh air ventilator with heat recovery function according to claim 9, characterized in that: If the filter hidden danger value does not exceed the preset filter hidden danger threshold, the diagnostic output value is calculated by weighted summing up the filtration resistance time value, resistance amplitude value and particle performance value. If the diagnostic output value exceeds the preset diagnostic output threshold, a filter hidden danger signal is generated; otherwise, a diagnostic qualification signal is generated.

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