Fresh air ventilator with heat recovery function intelligent temperature control system

Abstract

The application belongs to the technical field of fresh air ventilator management and control, and specifically relates to an intelligent temperature control system of a fresh air ventilator with heat recovery function, comprising 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 degree adjusting module and a user terminal; the application analyzes and generates corresponding temperature control strategies based on indoor and outdoor environment monitoring data through the self-adaptive temperature control module, and automatically adjusts and controls the heat recovery mode, the inlet fan rotating speed, the exhaust fan rotating speed and the bypass valve opening degree of the fresh air ventilator based on the temperature control strategies, thereby reducing the operation management and control difficulty of the fresh air ventilator and ensuring the indoor environment comfort, and the operation condition of the fresh air ventilator is analyzed through the intelligent tracking analysis module; when a tracking abnormal signal is generated, cause investigation and analysis are performed, and the fresh air ventilator is checked and maintained as required, so that the fresh air ventilator is efficiently, stably, safely and energy-savingly operated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fresh air ventilator control, and particularly relates to an intelligent temperature control system of a fresh air ventilator with heat recovery function. BACKGROUND

[0002] The fresh air ventilator is an environmental protection appliance integrating ventilation, air exchange and purification, which forms a continuous fresh air flow field by mechanical air supply and air induction, discharges indoor dirty air, and introduces fresh air filtered to keep indoor air clean and fresh.

[0003] At present, the fresh air ventilator is mainly controlled by manual input of control data, and the fresh air ventilator operates according to the input control data, which is difficult to automatically execute corresponding temperature control strategies and realize operation tracking and alarm according to indoor and outdoor environmental conditions, and cannot assist in diagnosing and outputting the operation abnormality of the fresh air ventilator, which is not conducive to ensuring the safe, stable and efficient operation of the fresh air ventilator, and the intelligent and automatic level is low.

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

[0005] The present application aims to provide an intelligent temperature control system of a fresh air ventilator with heat recovery function, which solves the problem that the prior art cannot automatically generate, execute corresponding temperature control strategies and realize operation tracking and alarm, and cannot assist in diagnosing and outputting the operation abnormality of the fresh air ventilator, which is not conducive to ensuring the safe, stable and efficient operation of the fresh air ventilator, and the intelligent and automatic level is low.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

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

[0008] The adaptive temperature control module analyzes real-time monitoring data sent by the multi-parameter intelligent sensing module to generate a corresponding temperature control strategy. The high-efficiency heat recovery module automatically adjusts the heat recovery mode of the fresh air ventilator based on the temperature control strategy, recovers heat based on the corresponding heat recovery mode, the fan speed adjustment module adjusts the speed of the inlet fan and the exhaust fan in the fresh air ventilator based on the temperature control strategy, and the bypass valve opening adjustment module adjusts the opening of the bypass valve based on the temperature control strategy. The user terminal displays 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 strategy to the intelligent tracking analysis module. The intelligent tracking analysis module analyzes the operating conditions of the fresh air ventilator in a 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. The user terminal issues a corresponding warning when receiving the tracking abnormal signal.

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

[0011] The speed of the inlet fan and the speed of the exhaust fan in the fresh air ventilator are collected. The difference between the speed of the inlet fan and the corresponding standard inlet fan speed is calculated and the absolute value is taken to obtain an inlet detection value. The difference between the speed of the exhaust fan and the corresponding standard exhaust fan speed is calculated and the absolute value is taken to obtain an exhaust detection value. The difference between the opening of the bypass valve of the fresh air ventilator and the corresponding standard opening is calculated and the absolute value is taken to obtain a valve opening detection value.

[0012] The ventilator self-control value is calculated by weighted sum of the inlet detection value, the exhaust detection value and the valve opening detection value. The ventilator self-control value is compared with the preset ventilator self-control threshold value. If the ventilator self-control value exceeds the preset ventilator self-control threshold value, it is determined that the fresh air ventilator is in a self-control unqualified state.

[0013] The total duration of the fresh air ventilator in the self-control unqualified state in a unit time is obtained and marked as a self-control warning time value. The average of all ventilator self-control values corresponding to the fresh air ventilator in a unit time is calculated to obtain a self-control decision value. The self-control warning time value and the self-control decision value are compared with the preset self-control warning time threshold value and the preset self-control decision threshold value, respectively. If the self-control warning time value or the self-control decision value exceeds the corresponding preset threshold value, a tracking abnormal signal is generated.

[0014] If neither the self-control alarm time value nor the self-control decision value exceeds the corresponding preset threshold value, an energy consumption decision value of the fresh air ventilator in a unit time is obtained, the energy consumption decision value is compared with a preset energy consumption decision threshold value, if the energy consumption decision value exceeds the preset energy consumption decision threshold value, a tracking abnormal signal is generated; if the energy consumption decision value does not exceed the preset energy consumption decision threshold value, a tracking qualified signal is generated.

[0015] Further, the intelligent tracking analysis module is in communication connection with the energy consumption decision evaluation module, the energy consumption decision evaluation module analyzes the energy consumption performance of the fresh air ventilator in a unit time, thereby obtaining an energy consumption decision value, 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] A plurality of detection time periods are set in a unit time, actual energy consumption data and theoretical energy consumption data of the fresh air ventilator in the corresponding detection time periods are collected, an excess value of the actual energy consumption data compared with the corresponding theoretical energy consumption data is marked as a consumption excess detection value; the consumption excess detection value is compared with a preset consumption excess detection threshold value, if the consumption excess detection value exceeds the preset consumption excess detection threshold value, the corresponding detection time period is marked as a consumption alarm time period;

[0018] The number of the consumption alarm time periods in a unit time is obtained, and a ratio calculation is performed on the number and the total number of the detection time periods to obtain a consumption alarm analysis value, the consumption excess detection values of all the consumption alarm time periods are averaged to obtain a consumption excess analysis value, and the largest consumption excess detection value in a unit time is marked as a consumption excess amplitude value; the consumption alarm analysis value, the consumption excess analysis value and the consumption excess amplitude value are weighted and summed to obtain the energy consumption decision value.

[0019] Further, the intelligent tracking analysis module is in communication connection with 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, the running abnormality of the fresh air ventilator is analyzed and diagnosed, an abnormal auxiliary diagnosis alarm signal or an abnormal auxiliary diagnosis qualified signal is generated through the analysis, and the abnormal auxiliary diagnosis alarm signal or the abnormal auxiliary diagnosis qualified signal is sent to the user terminal, when the user terminal receives the abnormal auxiliary diagnosis alarm signal, a corresponding early warning is issued.

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

[0021] The noise curve, vibration frequency curve and vibration amplitude curve of the fresh air ventilator in operation in a unit time are obtained, a rectangular coordinate system is established with time as the X-axis and noise decibel value as the Y-axis, and the noise curve is placed in the first quadrant of the rectangular coordinate system, and the starting point of the noise curve is located on the Y-axis; a noise judgment ray parallel to the X-axis and with its end point on the Y-axis is drawn in the first quadrant of the rectangular coordinate system, the area of the region surrounded by the noise curve above the noise judgment ray is obtained and marked as the noise over-measurement value, and the vibration frequency over-measurement value and the vibration amplitude over-measurement value are obtained in the same way.

[0022] The auxiliary output value is calculated by weighted summation of the noise over-measurement value, the vibration frequency over-measurement value and the vibration amplitude over-measurement value, the auxiliary output value is compared with the preset auxiliary output threshold value, if the auxiliary output value exceeds the preset auxiliary output threshold value, an abnormal auxiliary diagnosis alarm signal is generated; if the auxiliary output value does not exceed the preset auxiliary output threshold value, an abnormal auxiliary diagnosis qualified signal is generated.

[0023] Further, the abnormal auxiliary analysis output module sends the abnormal auxiliary diagnosis qualified signal to the filtering diagnosis output module, the filtering diagnosis output module generates a diagnosis qualified signal or a filtering hidden danger signal by analysis when receiving the abnormal auxiliary diagnosis qualified signal, and sends the diagnosis qualified signal or the filtering hidden danger signal to the user terminal, and the user terminal sends a corresponding early warning when receiving the filtering hidden danger signal.

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

[0025] The time of the last adjacent filter inspection and cleaning is collected and marked as the inspection and cleaning time, the interval time between the current time and the inspection and cleaning time is marked as the inspection and cleaning interval value; and the running time of the fresh air ventilator and the average concentration of dust particles in the external air entering the fresh air ventilator within the interval time between the current time and the inspection and cleaning time are marked as the filtering required time value and the filtering dust collection value, respectively.

[0026] The filter hidden danger value is calculated by weighted summation of the inspection and cleaning interval value, the filtering required time value and the filtering dust collection value, and the filter hidden danger value is compared with the preset filter hidden danger threshold value, if the filter hidden danger value exceeds the preset filter hidden danger threshold value, a filtering hidden danger signal is generated.

[0027] Further, if the filter hidden danger value does not exceed the preset filter hidden danger threshold value, the concentration of dust particles in the air is collected and marked as the particle detection value, the particle detection value is compared with the preset particle detection threshold value, if the particle detection value exceeds the preset particle detection threshold value, it is judged that the filter is in a filtering obstruction state.

[0028] The length of time that the filter is in the filtering hindering state in a unit time is obtained and marked as a filtering hindering time value, the filtering hindering time value is compared with a preset filtering hindering time threshold value, if the filtering hindering time value exceeds the preset filtering hindering time threshold value, a filtering abnormal signal is generated, if the filtering hindering time value does not exceed the preset filtering hindering time threshold value, the maximum single continuous time length in the filtering hindering state in a unit time is marked as a hindering holding amplitude value, and the average concentration of dust particles in the air sent in a unit time is marked as a particle performance value;

[0029] The diagnostic output value is calculated by weighted summation of the filtering hindering time value, the hindering holding amplitude value and the particle performance value, the diagnostic output value is compared with a preset diagnostic output threshold value, if the diagnostic output value exceeds the preset diagnostic output threshold value, a filtering hidden danger signal is generated, if the diagnostic output value does not exceed the preset diagnostic output threshold value, a diagnostic qualified signal is generated.

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

[0031] 1、In the present application, the self-adaptive temperature control module analyzes and generates the 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 speed of the inlet fan, the speed of the exhaust fan and the opening degree of the bypass valve based on the temperature control strategy, which reduces the operation and control difficulty of the fresh air ventilator and ensures the indoor environment comfort, and the intelligent tracking analysis module analyzes the operation condition of the fresh air ventilator, and makes corresponding optimization improvement measures when the tracking abnormal signal is generated, which ensures the efficient, stable and safe and energy-saving operation of the fresh air ventilator;

[0032] 2、In the present application, the abnormal auxiliary analysis output module assists in diagnosing and analyzing the operation abnormality of the fresh air ventilator, suspends the operation of the fresh air ventilator and performs inspection and maintenance when the abnormal auxiliary diagnosis alarm signal is generated, which reduces the operation safety hidden danger of the fresh air ventilator, is beneficial to prolong the service life of the fresh air ventilator and ensure its safe operation, and the filtering diagnosis output module analyzes the condition of the filter in the fresh air ventilator when the abnormal auxiliary diagnosis qualified signal is generated, and the filter in the fresh air ventilator is replaced or cleaned when the filtering hidden danger signal is generated, which ensures the filtering effect of the input air, is beneficial to maintain the indoor air quality, has high intelligent level and small supervision difficulty. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to facilitate the understanding of those skilled in the art, the present application will be further described below with reference to the accompanying drawings;

[0034] Figure 1 The system block diagram of example one in the present application;

[0035] Figure 2 The system block diagram of example two and example three in the present application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0037] Embodiment one: as shown in the figure, the present application proposes an intelligent temperature control system of a fresh air ventilator with heat recovery function, which comprises a multi-parameter intelligent sensing module, a self-adaptive temperature control module, a high-efficiency heat recovery module, a fan speed regulation module, a bypass valve opening degree regulation module, an intelligent tracking analysis module and a user terminal. Figure 1

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

[0039] The self-adaptive temperature control module analyzes the real-time monitoring data sent by the multi-parameter intelligent sensing module (mainly through PID-fuzzy control algorithm analysis), generates the corresponding temperature control strategy, and sends the generated temperature control strategy to the high-efficiency heat recovery module, the fan speed regulation module, the bypass valve opening degree regulation module, the intelligent tracking analysis module, the intelligent tracking analysis module and the user terminal.

[0040] The high-efficiency heat recovery module automatically adjusts the heat recovery mode (including sensible heat recovery mode and total heat recovery mode, etc.) of the fresh air ventilator based on the temperature control strategy, recovers heat based on the corresponding heat recovery mode, the fan speed regulation module adjusts the speed of the inlet fan and the exhaust fan in the fresh air ventilator based on the temperature control strategy, and the bypass valve opening degree regulation module adjusts the opening degree of the bypass valve based on the temperature control strategy, so as to realize the automatic adjustment and control of the fresh air ventilator, reduce the operation control difficulty of the fresh air ventilator and improve its intelligent level.

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

[0042] ​The intelligent tracking analysis module analyzes the operation state of the fresh air ventilator in a 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. When the user terminal receives the tracking abnormal signal, it issues a corresponding early warning to remind the user to investigate and analyze the cause in a timely manner and to check and maintain the fresh air ventilator as needed, so as to ensure efficient, stable, safe and energy-saving operation of the fresh air ventilator. The specific analysis process of the intelligent tracking analysis module is as follows:

[0043] The rotational speed of the air inlet fan and the rotational speed of the air outlet fan in the fresh air ventilator are collected, the rotational speed of the air inlet fan is difference calculated with the current corresponding standard air inlet rotational speed and the absolute value is taken to obtain an inlet rotation detection value, the rotational speed of the air outlet fan is difference calculated with the current corresponding standard air outlet rotational speed and the absolute value is taken to obtain an outlet rotation detection value, and the bypass valve opening degree of the fresh air ventilator is difference calculated with the current corresponding standard opening degree and the absolute value is taken to obtain a valve opening detection value;

[0044] The ventilator self-control value is calculated by weighted summation of the inlet rotation detection value, the outlet rotation detection value and the valve opening detection value, that is, the inlet rotation detection value, the outlet rotation detection value and the valve opening detection value are respectively assigned with corresponding preset weight coefficients, and the inlet rotation detection value, the outlet rotation detection value and the valve opening detection value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three groups of product results is marked as the ventilator self-control value. The larger the ventilator self-control value, the worse the comprehensive real-time execution state of the temperature control strategy;

[0045] The ventilator self-control value is compared with the preset ventilator self-control threshold value. If the ventilator self-control value exceeds the preset ventilator self-control threshold value, it indicates that the comprehensive real-time execution state of the temperature control strategy is poor, and it is judged that the fresh air ventilator is in a self-control unqualified state;

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

[0047] If neither the self-control warning time value nor the self-control decision value exceeds the corresponding preset threshold value, it indicates that the control execution performance of the fresh air ventilator is good, and the energy consumption decision value of the fresh air ventilator in a unit time is obtained. The energy consumption decision value is compared with the preset energy consumption decision threshold value. If the energy consumption decision value exceeds the preset energy consumption decision threshold value, it indicates that the energy consumption performance of the fresh air ventilator in a unit time is poor, and a tracking abnormal signal is generated. If the energy consumption decision value does not exceed the preset energy consumption decision threshold value, it indicates that the energy consumption performance of the fresh air ventilator in a unit time is good, and a tracking qualified signal is generated.

[0048] It should be noted that the intelligent tracking analysis module is in communication connection with the energy consumption decision evaluation module. The energy consumption decision evaluation module analyzes the energy consumption performance of the fresh air ventilator in a unit time, thereby obtaining the energy consumption decision value, and sends the energy consumption decision value to the intelligent tracking analysis module. Not only can the energy consumption performance of the fresh air ventilator in a unit time be reasonably judged, but also data support can be provided for the analysis process of the intelligent tracking analysis module, ensuring its analysis comprehensiveness and result accuracy. The specific analysis process of the energy consumption decision evaluation module is as follows:

[0049] A plurality of detection periods are set in a unit time, and the duration of each detection period is the same. The actual energy consumption data and the theoretical energy consumption data of the fresh air ventilator in the corresponding detection period are collected, and the excess value of the actual energy consumption data compared with the corresponding theoretical energy consumption data is marked as a consumption excess detection value. The consumption excess detection value is compared with the preset consumption excess detection threshold value. If the consumption excess detection value exceeds the preset consumption excess detection threshold value, it indicates that the energy consumption of the corresponding detection period is too large, and the corresponding detection period is marked as a consumption warning period.

[0050] The number of consumption warning periods in a unit time is obtained, and a consumption warning analysis value is obtained by ratio calculation of the total number of detection periods. The consumption excess detection values of all consumption warning periods are averaged to obtain a consumption excess analysis value, and the maximum consumption excess detection value in a unit time is marked as a consumption excess amplitude value.

[0051] The energy consumption decision value is obtained by weighted summation calculation of the consumption warning analysis value, the consumption excess analysis value and the consumption excess amplitude value, that is, the corresponding preset weight coefficients are assigned to the consumption warning analysis value, the consumption excess analysis value and the consumption excess amplitude value in advance, and the consumption warning analysis value, the consumption excess analysis value and the consumption excess amplitude value are multiplied by the corresponding preset weight coefficients, and the sum of the three groups of product results is marked as the energy consumption decision value. Moreover, the larger the value of the energy consumption decision value, the more abnormal the energy consumption performance of the fresh air ventilator in a unit time.

[0052] Embodiment two: as Figure 2As shown, the difference between the embodiment and embodiment one is that the intelligent tracking analysis module is in communication connection with the abnormal auxiliary analysis output module, the intelligent tracking analysis module sends the tracking qualified signal to the abnormal auxiliary analysis output module, the abnormal auxiliary analysis output module performs auxiliary diagnosis analysis on the operation abnormality of the fresh air ventilator when receiving the tracking qualified signal, and generates an abnormal auxiliary diagnosis alarm signal or an abnormal auxiliary diagnosis qualified signal through analysis;

[0053] and sends the abnormal auxiliary diagnosis alarm signal or the abnormal auxiliary diagnosis qualified signal to the user terminal, the user terminal sends a corresponding early warning when receiving the abnormal auxiliary diagnosis alarm signal to remind the user to suspend the operation of the fresh air ventilator and perform inspection and maintenance, thereby reducing the operation safety hazards of the fresh air ventilator and being beneficial to prolonging the service life of the fresh air ventilator and ensuring the safe operation thereof; the specific analysis process of the abnormal auxiliary analysis output module is as follows:

[0054] The noise curve, vibration frequency curve and vibration amplitude curve of the fresh air ventilator during operation within a unit time are obtained, a rectangular coordinate system is established with time as the X-axis and noise decibel value as the Y-axis, and the noise curve is placed in the first quadrant of the rectangular coordinate system, and the starting point of the noise curve is located on the Y-axis; a noise judgment ray parallel to the X-axis and with the end point located on the Y-axis is drawn in the first quadrant of the rectangular coordinate system, the area of the region surrounded by the noise curve above the noise judgment ray is obtained and marked as the noise over-measured value, and the vibration frequency over-measured value and the vibration amplitude over-measured value are obtained in the same way (i.e. the same analysis through coordinate system);

[0055] The auxiliary output value is calculated by weighted summation of the noise over-measured value, the vibration frequency over-measured value and the vibration amplitude over-measured value, i.e. the noise over-measured value, the vibration frequency over-measured value and the vibration amplitude over-measured value are respectively assigned with corresponding preset weight coefficients, and the noise over-measured value, the vibration frequency over-measured value and the vibration amplitude over-measured value are respectively multiplied by the corresponding preset weight coefficients, and the three groups of product results are summed up to obtain the auxiliary output value; and the larger the auxiliary analysis value, the greater the probability of abnormal operation of the fresh air ventilator;

[0056] The auxiliary output value is compared with the preset auxiliary output threshold value, if the auxiliary output value exceeds the preset auxiliary output threshold value, it indicates that the probability of abnormal operation of the fresh air ventilator 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 value, it indicates that the probability of abnormal operation of the fresh air ventilator is relatively small, and an abnormal auxiliary diagnosis qualified signal is generated.

[0057] Embodiment three: as Figure 2As shown, the difference between the embodiment and the embodiment one and the embodiment two is that the abnormal auxiliary analysis output module sends the abnormal auxiliary diagnosis qualified signal to the filtering diagnosis output module, the filtering diagnosis output module generates the diagnosis qualified signal or the filtering hidden danger signal by analyzing when receiving the abnormal auxiliary diagnosis qualified 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, the corresponding early warning is sent out to remind the user to replace or clean the filter in the fresh air ventilator in time, so as to ensure the filtering effect of the filter on the input air, which is conducive to maintaining the indoor air quality; the specific analysis process of the filtering diagnosis output module is as follows:

[0059] The time when the filter is checked and cleaned last time is collected and marked as the check and clean time, the interval time between the current time and the check and clean time is marked as the check and clean interval value; and the running time of the fresh air ventilator and the average concentration of dust particles in the outside air entering between the current time and the check and clean time are marked as the filtering required time value and the filtering dust collection value respectively;

[0060] The filter hidden danger value is calculated by weighted summation of the check and clean interval value, the filtering required time value and the filtering dust collection value, that is, the check and clean interval value, the filtering required time value and the filtering dust collection value are respectively assigned with corresponding preset weight coefficients, and the check and clean interval value, the filtering required time value and the filtering dust collection value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three groups of product results is marked as the filter hidden danger value;

[0061] Furthermore, the larger the value of the filter hidden danger value is, the more necessary it is to check and clean the filter in time to ensure its filtering effect; the filter hidden danger value is compared with the preset filter hidden danger threshold value, if the filter hidden danger value exceeds the preset filter hidden danger threshold value, it means that the filter needs to be checked and cleaned in time to ensure its filtering effect, and the filtering hidden danger signal is generated.

[0062] Further, if the filter hidden danger value does not exceed the preset filter hidden danger threshold value, the concentration of dust particles in the input air is collected and marked as the particle detection value, the particle detection value is compared with the preset particle detection threshold value, if the particle detection value exceeds the preset particle detection threshold value, it means that the real-time filtering effect of the filter is poor, and it is judged that the filter is in the filtering hindering state;

[0063] The length of time when the filter is in the filtering hindering state in unit time is obtained and marked as the filtering hindering time value, the filtering hindering time value is compared with the preset filtering hindering time threshold value, if the filtering hindering time value exceeds the preset filtering hindering time threshold value, it means that the filtering performance of the fresh air ventilator on the input air in unit time is poor, and the filtering abnormal signal is generated;

[0064] If the filter resistance time value does not exceed the preset filter resistance time threshold, the maximum single duration of the filter resistance state in a unit of time is marked as the resistance duration value, and the average concentration of dust particles in the air in a unit of time is marked as the particle performance value;

[0065] The diagnostic output value is calculated by weighted summation of the filter resistance time value, the resistance duration value and the particle performance value, that is, the filter resistance time value, the resistance duration value and the particle performance value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three groups of product results is marked as the diagnostic output value; the larger the value of the diagnostic output value, the worse the overall performance of the fresh air ventilator in a unit of time;

[0066] The diagnostic output value is compared with the preset diagnostic output threshold value, if the diagnostic output value exceeds the preset diagnostic output threshold value, it indicates that the overall performance of the fresh air ventilator for the input air in a unit of time is poor, and a filter risk signal is generated; if the diagnostic output value does not exceed the preset diagnostic output threshold value, it indicates that the overall performance of the fresh air ventilator for the input air in a unit of time is good, and a diagnostic qualified signal is generated.

[0067] The working principle of the present application is: when in use, the indoor and outdoor environment is monitored by the multi-parameter intelligent sensing module, the self-adaptive temperature control module analyzes the real-time monitoring data and generates the corresponding temperature control strategy, the high-efficiency heat recovery module adjusts and controls the heat recovery mode, the inlet fan speed, the exhaust fan speed and the bypass valve opening degree of the fresh air ventilator based on the temperature control strategy, reduces the operation control difficulty of the fresh air ventilator and ensures the indoor environment comfort, and the intelligent tracking analysis module analyzes the operating condition of the fresh air ventilator in a unit of time, reminds the user to investigate and analyze the cause when generating a tracking abnormal signal, and checks and maintains the fresh air ventilator as needed, ensures the efficient, stable and safe and energy-saving operation of the fresh air ventilator, and has high intelligence and automation level.

[0068] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details, nor limit the present application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present application. The present application selects and describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. The present application is limited by the claims and their entire scope and equivalents.

Claims

1. A fresh air ventilator intelligent temperature control system with heat recovery function, characterized in that, The system comprises a multi-parameter intelligent sensing module, an 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 multi-parameter intelligent sensing module monitors the indoor and outdoor environment and transmits 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 to generate a corresponding temperature control strategy, the efficient heat recovery module automatically adjusts the heat recovery mode of the fresh air ventilator based on the temperature control strategy, the fan rotating speed adjusting module adjusts the rotating speed of the inlet fan and the exhaust fan in the fresh air ventilator based on the temperature control strategy, and the bypass valve opening adjusting module adjusts the opening of the bypass valve based on the temperature control strategy; the user terminal displays the real-time monitoring data, the temperature control strategy, and the control execution information, and is used to issue manual instructions for the operation of the fresh air ventilator; The adaptive temperature control module is communicatively connected to an intelligent tracking analysis module, the adaptive temperature control module sends the generated temperature control strategy to the intelligent tracking analysis module, the intelligent tracking analysis module analyzes the operating conditions of the fresh air ventilator in a 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, the user terminal issues a corresponding warning when receiving the tracking abnormal signal; The intelligent tracking analysis module is communicatively connected to an abnormal auxiliary analysis output module, the intelligent tracking analysis module sends the tracking qualified signal to the abnormal auxiliary analysis output module, the abnormal auxiliary analysis output module performs auxiliary diagnostic analysis on the operating abnormality of the fresh air ventilator when receiving the tracking qualified signal, generates an abnormal auxiliary diagnostic alarm signal or an abnormal auxiliary diagnostic qualified signal through analysis, and sends the abnormal auxiliary diagnostic alarm signal or the abnormal auxiliary diagnostic qualified signal to the user terminal, the user terminal issues a corresponding warning when receiving the abnormal auxiliary diagnostic alarm signal; The specific analysis process of the abnormal auxiliary analysis output module is as follows: obtaining the noise curve, the vibration frequency curve, and the vibration amplitude curve of the fresh air ventilator during operation in a unit time, establishing a rectangular coordinate system with time as the X-axis and noise decibel value as the Y-axis, and placing the noise curve in the first quadrant of the rectangular coordinate system with the starting point of the noise curve on the Y-axis; drawing a noise judgment ray parallel to the X-axis and with the end point on the Y-axis in the first quadrant of the rectangular coordinate system, obtaining the area of the region surrounded by the noise curve above the noise judgment ray and the noise judgment ray, and marking it as the noise over-measured value, and similarly obtaining the vibration frequency over-measured value and the vibration amplitude over-measured value; The auxiliary output value is calculated by weighted summation of the noise over-measured value, the vibration frequency over-measured value, and the vibration amplitude over-measured value, the auxiliary output value is compared with the preset auxiliary output threshold value, if the auxiliary output value exceeds the preset auxiliary output threshold value, the abnormal auxiliary diagnostic alarm signal is generated; if the auxiliary output value does not exceed the preset auxiliary output threshold value, the abnormal auxiliary diagnostic qualified signal is generated; The abnormal auxiliary analysis output module sends an 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 by 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 early warning.

2. The intelligent temperature control system of a fresh air ventilator with heat recovery function according to claim 1, characterized in that, The specific analysis process of the filtering diagnosis output module includes: The time when the adjacent last filter inspection and cleaning is performed is collected and marked as the inspection and cleaning time. The interval between the current time and the inspection and cleaning time is marked as the inspection and cleaning interval value. The running time of the fresh air ventilator and the average concentration of dust particles in the external air entering within the interval between the current time and the inspection and cleaning time are marked as the filtering required time value and the filtering dust collection value, respectively. The filter hidden danger value is calculated by weighted summation of the inspection and cleaning interval value, the filtering required time value, and the filtering dust collection value. The filter hidden danger value is compared with the preset filter hidden danger threshold value. If the filter hidden danger value exceeds the preset filter hidden danger threshold value, a filtering hidden danger signal is generated. If the filter hidden danger value does not exceed the preset filter hidden danger threshold value, the concentration of dust particles in the air is collected and marked as the particle detection value. The particle detection value is compared with the preset particle detection threshold value. If the particle detection value exceeds the preset particle detection threshold value, it is determined that the filter is in a filtering obstruction state. The length of time when the filter is in the filtering obstruction state within a unit time is obtained and marked as the filtering obstruction time value. The filtering obstruction time value is compared with the preset filtering obstruction time threshold value. If the filtering obstruction time value exceeds the preset filtering obstruction time threshold value, a filtering abnormal signal is generated. If the filtering obstruction time value does not exceed the preset filtering obstruction time threshold value, the maximum single continuous time in the filtering obstruction state within a unit time is marked as the obstruction amplitude value, and the average concentration of dust particles in the air within a unit time is marked as the particle performance value. The diagnosis output value is calculated by weighted summation of the filtering obstruction time value, the obstruction amplitude value, and the particle performance value. The diagnosis output value is compared with the preset diagnosis output threshold value. If the diagnosis output value exceeds the preset diagnosis output threshold value, a filtering hidden danger signal is generated. If the diagnosis output value does not exceed the preset diagnosis output threshold value, a diagnosis qualified signal is generated.

3. The intelligent temperature control system of a fresh air ventilator with heat recovery function according to claim 1, characterized in that, The specific analysis process of the intelligent tracking analysis module includes: 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 current corresponding standard intake rotational speed is calculated and the absolute value is taken to obtain the intake detection value. The difference between the rotational speed of the exhaust fan and the current corresponding standard exhaust rotational speed is calculated and the absolute value is taken to obtain the exhaust detection value. The difference between the bypass valve opening of the fresh air ventilator and the current corresponding standard opening is calculated and the absolute value is taken to obtain the valve opening detection value. The ventilator self-control value is calculated by weighted summation of the intake detection value, the exhaust detection value, and the valve opening detection value. The ventilator self-control value is compared with the preset ventilator self-control threshold value. If the ventilator self-control value exceeds the preset ventilator self-control threshold value, it is determined that the fresh air ventilator is in a self-control unqualified state. The total time length of the fresh air ventilator in the self-control unqualified state in a unit time is obtained and marked as a self-control alarm time value, and all self-control values of the fresh air ventilator in a unit time are averaged to obtain a self-control decision value. If the self-control alarm time value or the self-control decision value exceeds the corresponding preset threshold value, a tracking abnormal signal is generated; If the self-control alarm time value and the self-control decision value do not exceed the corresponding preset threshold value, the energy consumption decision value of the fresh air ventilator in a unit time is obtained. If the energy consumption decision value exceeds the preset energy consumption decision threshold value, a tracking abnormal signal is generated. If the energy consumption decision value does not exceed the preset energy consumption decision threshold value, a tracking qualified signal is generated.

4. The intelligent temperature control system of a fresh air ventilator with heat recovery function according to claim 3, characterized in that, The intelligent tracking analysis module is in communication connection with the energy consumption decision evaluation module. The energy consumption decision evaluation module analyzes the energy consumption performance of the fresh air ventilator in a unit time, thereby obtaining the energy consumption decision value, and sends the energy consumption decision value to the intelligent tracking analysis module.

5. The intelligent temperature control system of a fresh air ventilator with heat recovery function according to claim 4, characterized in that, The specific analysis process of the energy consumption decision evaluation module is as follows: a plurality of detection periods are set in a unit time. The actual energy consumption data and the theoretical energy consumption data of the fresh air ventilator in the corresponding detection period are collected. The excess value of the actual energy consumption data compared with the corresponding theoretical energy consumption data is marked as an energy consumption excess detection value; If the energy consumption excess detection value exceeds the preset energy consumption excess detection threshold value, the corresponding detection period is marked as an energy consumption alarm period; The number of the energy consumption alarm periods in a unit time is obtained and is ratio calculated with the total number of the detection periods to obtain an energy consumption alarm analysis value. The energy consumption excess detection values of all the energy consumption alarm periods are averaged to obtain an energy consumption excess analysis value. The maximum energy consumption excess detection value in a unit time is marked as an energy consumption excess amplitude value; The energy consumption decision value is obtained by weighted summation calculation of the energy consumption alarm analysis value, the energy consumption excess analysis value and the energy consumption excess amplitude value.

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