Indoor ventilation device startup judgment method, medium and electronic device

Abstract

The present invention provides a method, medium, and electronic device for determining whether to activate an indoor ventilation device. These methods, belonging to the technical field of ventilation devices, include: acquiring indoor harmful gas detection data in real time and recording fluctuations in the detection data; calculating the aging rates of multiple harmful gas sensor arrays; inputting the detection data into compensation equations using a pre-established aging compensation equation set to obtain compensated detection data; comparing the compensated detection data and its fluctuations with preset harmful gas concentration thresholds and fluctuation thresholds; determining activation conditions based on the compensated detection data using a pre-set empirical formula; and sending a startup command to the indoor ventilation device when the preset harmful gas concentration threshold is exceeded and the activation conditions are met. This method solves the technical problem of sensor performance gradually fading, which can lead to decreased stability and reliability of detection data and easily lead to misjudgments or missed detections.

Description

Technical Field

[0001] The present invention belongs to the technical field of ventilation devices, and in particular relates to a method, a medium and an electronic device for determining the start-up of an indoor ventilation device. Background Art

[0002] Harmful gases, such as carbon monoxide and volatile organic compounds, are often produced in confined spaces. Existing technology often installs harmful gas sensors in confined spaces to detect harmful gases. When the concentration of harmful gases exceeds a preset threshold, the ventilation device is activated for ventilation.

[0003] However, as the usage time increases, the sensor performance will gradually decline, resulting in a decrease in the stability and reliability of the detection data, which can easily lead to problems such as misjudgment or missed judgment. Summary of the Invention

[0004] In view of this, the present invention provides a method, medium and electronic device for determining the start-up of an indoor ventilation device, which can solve the technical problem that the performance of the sensor gradually decays, resulting in a decrease in the stability and reliability of the detection data, and easily leading to misjudgment or missed judgment.

[0005] The present invention is achieved in that:

[0006] A first aspect of the present invention provides a method for determining whether an indoor ventilation device is started, comprising the following steps:

[0007] S10, acquiring detection data of multiple indoor harmful gas sensor arrays in real time;

[0008] S20, recording fluctuations in detection data of the plurality of harmful gas sensor arrays within a preset time period;

[0009] S30, calculating the aging rates of the plurality of harmful gas sensor arrays according to the fluctuation of the detection data;

[0010] S40, based on the aging rates of the plurality of harmful gas sensor arrays, using a pre-established aging compensation equation group, inputting the detection data into the compensation equation for calculation to obtain compensated detection data;

[0011] S50, comparing the compensated detection data and data fluctuation with a preset harmful gas concentration threshold and fluctuation threshold;

[0012] S60, judging the start condition using a preset empirical formula based on the compensated detection data;

[0013] S70: When the compensated detection data exceeds the preset harmful gas concentration threshold and satisfies the start-up condition, a start-up instruction is sent to the indoor ventilation device.

[0014] On the basis of the above technical solution, the method for determining whether an indoor ventilation device is started according to the present invention can be further improved as follows:

[0015] The harmful gas sensor array is specifically a sensor array composed of multiple gas sensors, each sensor detects different types of harmful gases, and the detection data constitute a sensor data matrix.

[0016] Furthermore, the harmful gas sensor array is specifically composed of A sensor array composed of gas sensors, each sensor detects different types of harmful gases, and the detection data constitutes a sensor data matrix :

[0017] ;

[0018] Where, Indicates the The sensor in The detection value at each time point, , ; is the number of sensors, the value range is 3-8; is the number of sampling time points.

[0019] Data fluctuation is determined by the fluctuation matrix express:

[0020] ;

[0021] Where, Indicates the relative rate of change between adjacent time points.

[0022] Sensor Aging Rate Matrix The calculation is as follows:

[0023] ;

[0024] Where, is the time weight coefficient, satisfying and .

[0025] The aging compensation equations are expressed in matrix form:

[0026] ;

[0027] Where, is the data matrix after compensation; is the compensation coefficient matrix, obtained through experimental calibration; is the acceleration influence coefficient, ranging from 0.01 to 0.1; is the second-order time derivative of the detection data; is the attenuation coefficient, ranging from 0.1 to 0.5; is the time constant; is the identity matrix; For the running time.

[0028] The starting condition judgment adopts the comprehensive evaluation function:

[0029] ;

[0030] Where, For the The concentration of harmful gases detected by each sensor after compensation; is the corresponding harmful gas concentration threshold; is the weight coefficient, satisfying ; is the sensitivity index, ranging from 1.5 to 2.5; is the volatility factor; is the real-time fluctuation value; is the fluctuation threshold.

[0031] when The start command is triggered when To start the threshold, calibrate it through the following steps:

[0032] Step 1: Under standard conditions, gradually increase the concentration of various harmful gases and record the detection data when ventilation needs are triggered;

[0033] Step 2: Substitute the obtained data into the evaluation function and take the lower limit of the 90% confidence interval as ;

[0034] Step 3: Repeat steps 1-2 under different temperature and humidity conditions to establish Temperature and humidity correction model:

[0035] ;

[0036] Where, is the threshold value under standard conditions; are the temperature and humidity correction factors respectively; are the temperature and humidity deviations, respectively.

[0037] Hazardous gas concentration threshold The default ranges are:

[0038] ;

[0039] Among them, for different types of harmful gases, the threshold ranges are as follows:

[0040] 1) For gas: , the optimal value is 1000ppm;

[0041] 2) For gas: , the optimal value is 10ppm;

[0042] 3) For : , the optimal value is 0.4mg / m³;

[0043] 4) For : , the optimal value is 50μg / m³;

[0044] 5) For formaldehyde: , the optimal value is 0.1mg / m³.

[0045] Fluctuation threshold The default ranges are:

[0046] ;

[0047] Among them, for different types of harmful gases, the fluctuation threshold ranges are as follows:

[0048] 1) For gas: , the optimal value is 0.20;

[0049] 2) For gas: , the optimal value is 0.15;

[0050] 3) For : , the optimal value is 0.25;

[0051] 4) For : , the optimal value is 0.30;

[0052] 5) For formaldehyde: , the optimal value is 0.20.

[0053] The optimal values ​​of the above thresholds are obtained based on the following experiments:

[0054] Step 1: Under standard laboratory conditions ( , relative humidity ), 100 groups of gas samples with different concentration gradients were tested;

[0055] Step 2: Use response surface methodology to establish a threshold optimization model:

[0056] ;

[0057] Where, is a comprehensive evaluation indicator; is the response time indicator; To trigger the accuracy indicator; is the energy consumption indicator; is the weight coefficient and satisfies ;

[0058] Step 3: Solve the optimal threshold combination through particle swarm optimization algorithm:

[0059] ;

[0060] Where, and are the optimal values ​​of concentration threshold and fluctuation threshold, respectively.

[0061] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for determining the start-up of an indoor ventilation device.

[0062] The third aspect of the present invention provides an electronic device for determining whether to start an indoor ventilation device, which comprises a processor, a memory, and an array of multiple harmful gas sensors. The memory is used to store the step program of the above-mentioned method, and the processor reads the memory and executes the steps.

[0063] Compared with the prior art, the method, medium, and electronic device for determining whether to start an indoor ventilation device provided by the present invention have the following beneficial effects:

[0064] 1) More accurate sensor aging compensation. This method constructs a sensor aging rate model based on test data fluctuations and uses a matrix-based aging compensation equation system to compensate the raw test data. This method effectively eliminates the impact of sensor aging on test data, improving data reliability.

[0065] 2) Comprehensive error compensation for test data. In addition to aging, this method also considers the impact of environmental temperature and humidity, dynamic changes in test data, and other factors on test values. By introducing second-order time derivatives and attenuation terms, it effectively compensates for various interference factors, making the compensated test data closer to the true value.

[0066] 3) Smarter start-up condition determination. This method uses a comprehensive evaluation function to analyze compensated detection data and fluctuations. Weight coefficients and sensitivity indices are used to flexibly control the impact of different harmful gases. Compared to simple threshold comparisons, this comprehensive judgment based on empirical formulas is more accurate and reliable.

[0067] 4) Stronger adaptability. This method also introduces a dynamic correction model for the startup threshold to address different temperature and humidity environments, further improving the adaptability and robustness of the system.

[0068] In summary, the indoor ventilation device startup judgment method proposed in the present invention integrates multiple core technologies such as gas monitoring, aging compensation, and dynamic threshold judgment. It can effectively solve the technical problems existing in the existing technology that the stability and reliability of the detection data will decrease due to the gradual attenuation of sensor performance, which is easy to cause misjudgment or missed judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, 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.

[0071] like Figure 1 FIG. 1 is a flow chart of a method for determining the start of an indoor ventilation device according to the first aspect of the present invention. The method comprises the following steps:

[0072] S10, acquiring detection data of multiple indoor harmful gas sensor arrays in real time;

[0073] S20, recording fluctuations in detection data of multiple harmful gas sensor arrays within a preset time period;

[0074] S30, calculating the aging rates of multiple harmful gas sensor arrays based on fluctuations in the detection data;

[0075] S40, based on the aging rates of the plurality of harmful gas sensor arrays, using a pre-established aging compensation equation group to input the detection data into the compensation equation for calculation to obtain compensated detection data;

[0076] S50, comparing the compensated detection data and data fluctuation with the preset harmful gas concentration threshold and fluctuation threshold;

[0077] S60, judging the starting condition using a preset empirical formula based on the compensated detection data;

[0078] S70: When the compensated detection data exceeds a preset harmful gas concentration threshold and satisfies a start-up condition, a start-up instruction is sent to the indoor ventilation device.

[0079] The specific implementation of the above steps is described in detail below:

[0080] The specific implementation of step S10 is as follows: This step aims to obtain the detection data of multiple harmful gas sensor arrays in the room in real time. First, the system is equipped with The sensor array consists of gas sensors, each sensor detects different types of harmful gases. The detection data of these sensors constitute a dimensional sensor data matrix ,in Indicates the The sensor in Detection value at a time point. Number of sensors The value range is , the number of sampling time points Set according to actual situation.

[0081] The specific implementation steps are as follows: First, the system collects the detection data of each gas sensor in real time. The sampling frequency can be set according to actual needs. Usually Sampling once. Then, the detection data of each sensor is organized into a sensor data matrix in chronological order. The matrix Row represents The detection value sequence of the sensor, Column represents The sensor data matrix is ​​constructed Passed to the subsequent data processing module. It contains real-time detection information of various harmful gases in the room, providing a basis for subsequent data analysis and judgment.

[0082] Through the above steps, the system can obtain the detection data of multiple harmful gas sensor arrays in the room in real time, providing necessary input for subsequent data analysis and startup judgment.

[0083] The specific implementation of step S20 is as follows: This step is to record the fluctuation of the detection data of the plurality of harmful gas sensor arrays within a preset time period. The fluctuation is achieved by constructing a fluctuation matrix To indicate that Indicates the sensors at adjacent time points and The relative rate of change between .

[0084] The specific implementation steps are as follows: First, traverse the sensor data matrix For each row (sensor) and each column (time point), calculate the relative rate of change between adjacent time points Then, the calculated relative rate of change Fill in the volatility matrix In the matrix, the dimension is Finally, the constructed volatility matrix The matrix is ​​passed to the subsequent data analysis module. This matrix describes the fluctuation of the detection data of each sensor within the preset time period and provides the necessary input for the subsequent calculation of the sensor aging rate.

[0085] Through the above steps, the system can record the fluctuations in detection data of multiple harmful gas sensor arrays within a preset time period, providing necessary data support for subsequent data analysis and startup judgment.

[0086] The specific implementation of step S30 is as follows: This step is to calculate the aging rate of multiple harmful gas sensor arrays based on the fluctuation of detection data. The sensor aging rate is calculated by constructing an aging rate matrix To indicate that Indicates the The aging rate of the sensor.

[0087] The specific implementation steps are as follows: First, introduce a set of time weight coefficients ,satisfy and These coefficients reflect the degree of influence of different time points on the aging rate. Recent fluctuations have a greater impact on the aging rate. Next, traverse the fluctuation matrix For each row (sensor), calculate the aging rate of the sensor Then, the calculated aging rate Fill in the aging rate matrix In the matrix, the dimension is Finally, the constructed aging rate matrix The matrix is ​​passed to the subsequent data compensation module. This matrix describes the aging degree of each sensor and provides the necessary parameters for subsequent data compensation.

[0088] Through the above steps, the system can calculate the aging rate of multiple harmful gas sensor arrays based on the fluctuation of the detection data, providing basic data for subsequent data compensation.

[0089] The specific implementation of step S40 is as follows: This step is to use the pre-established aging compensation equation group to compensate the original detection data to obtain compensated detection data. The aging compensation equation group is expressed in matrix form as follows:

[0090] ;

[0091] in, is the data matrix after compensation, is the compensation coefficient matrix, is the acceleration influence coefficient, is the second-order time derivative of the detection data, is the attenuation coefficient, is the time constant, is the identity matrix, For the running time.

[0092] The specific implementation steps are as follows: First, obtain the aging rate matrix calculated in the above steps Then, the compensation coefficient matrix is ​​obtained through experimental calibration This matrix reflects the degree of influence of aging on the test data. Next, calculate the test data matrix The second-order time derivative of This term describes the dynamic change characteristics of the test data. Finally, the above parameters are substituted into the aging compensation equation group to calculate the compensated test data matrix and pass it to the subsequent start condition judgment module.

[0093] Through the above steps, the system can use the pre-established aging compensation model to compensate the original detection data, eliminate the impact of sensor aging on the detection data, and provide more accurate data support for subsequent startup condition judgment.

[0094] The specific implementation of step S50 is as follows: This step is intended to compare the compensated detection data and data fluctuation with the preset harmful gas concentration threshold and fluctuation threshold.

[0095] The specific implementation steps are as follows: First, obtain the compensated detection data matrix calculated in the above steps and the volatility matrix Then, from the preset harmful gas concentration threshold vector and fluctuation threshold vector Extract the corresponding threshold parameters. These threshold parameters have default value ranges according to different types of harmful gases. Then, compare the compensated detection data one by one The corresponding concentration threshold , and the fluctuation value The corresponding fluctuation threshold Finally, the comparison result is passed to the subsequent start condition judgment module. The purpose of this step is to filter out sensor data that exceeds the preset threshold and provide a basis for the final start judgment.

[0096] Through the above steps, the system can compare the compensated detection data and fluctuation conditions with the preset threshold value, providing necessary input information for subsequent startup condition judgment.

[0097] The specific implementation of step S60 is as follows: This step is intended to use a pre-set empirical formula to determine the start condition based on the compensated detection data. The start condition determination uses the following comprehensive evaluation function :

[0098] ;

[0099] in, is the weight coefficient, satisfying ; is the sensitivity index, with a value range of ; is the volatility factor; is the real-time fluctuation value; is the fluctuation threshold.

[0100] The specific implementation steps are as follows: First, obtain the compensated detection data calculated in the above steps and fluctuation value Then, according to the characteristics of different types of harmful gases, determine their respective weight coefficients and sensitivity index Usually, gases with greater harmfulness are given higher weights. Next, calculate the comprehensive evaluation function The function comprehensively considers the degree of detection value relative to the threshold and the fluctuation range. Finally, the calculated comprehensive evaluation function value With the preset start threshold For comparison. , the start condition is considered to be met.

[0101] Through the above steps, the system can use the pre-set empirical formula to make a comprehensive judgment on the starting conditions based on the compensated detection data, providing a basis for sending the final starting instruction.

[0102] The specific implementation of step S70 is as follows: this step is intended to send a start instruction to the indoor ventilation device when the start conditions are met.

[0103] The specific implementation steps are as follows: First, obtain the comprehensive evaluation function value calculated in the previous steps Then, With the preset start threshold For comparison. When the temperature is low, a start command is sent to the indoor ventilation device. This command can be in the form of a digital signal, an analog signal, or a network command, depending on the interface type of the ventilation device. For different temperature and humidity environments, the following correction model can be used to adjust the start threshold :

[0104] ;

[0105] in, is the threshold value under standard conditions, are the correction coefficients for temperature and humidity, respectively. are the deviations of temperature and humidity, respectively.

[0106] Through these steps, when the system determines that the activation conditions are met, it will send a start command to the indoor ventilation device to adjust the indoor environment and eliminate the harm of harmful gases. The activation threshold can also be dynamically adjusted to different temperature and humidity environments to improve the system's adaptability.

[0107] In summary, the indoor ventilation device startup determination method of the present invention includes seven specific steps, namely:

[0108] S10) acquiring detection data of multiple indoor harmful gas sensor arrays in real time;

[0109] S20) recording the fluctuation of detection data of multiple harmful gas sensor arrays within a preset time period;

[0110] S30) calculating the aging rate of multiple harmful gas sensor arrays based on the fluctuation of the detection data;

[0111] S40) compensating the test data using a pre-established aging compensation equation group to obtain compensated test data;

[0112] S50) comparing the compensated detection data and fluctuations with the preset harmful gas concentration threshold and fluctuation threshold;

[0113] S60) based on the compensated detection data using a pre-set empirical formula to determine the start condition;

[0114] S70) When the start-up conditions are met, a start-up instruction is sent to the indoor ventilation device.

[0115] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for determining the start-up of an indoor ventilation device.

[0116] The third aspect of the present invention provides an electronic device for determining whether to start an indoor ventilation device, which comprises a processor, a memory, and an array of multiple harmful gas sensors. The memory is used to store the step program of the above-mentioned method, and the processor reads the memory and executes the steps.

[0117] Specifically, the principle of the present invention is:

[0118] First, the system is equipped with The sensor array consists of gas sensors, each sensor detects different types of harmful gases, such as 、 、 、 , formaldehyde, etc. The detection data of these sensors constitute a dimensional sensor data matrix ,in Indicates the The sensor in Detection value at a time point. Number of sensors The value range is ,Number of sampling time points Set according to actual situation.

[0119] Since sensors age during long-term use, there will be deviations in the detection data. Therefore, the present invention first constructs a sensor aging rate model based on the fluctuation of detection data. Specifically, a set of time weight coefficients are introduced. , by calculating the relative change rate of each sensor detection data at adjacent time points , and perform weighted summation to obtain the aging rate matrix In this way, the aging degree of each sensor can be quantified.

[0120] Next, using the aging rate matrix And the compensation coefficient matrix obtained by experimental calibration , through the matrix form of aging compensation equations , for the original detection data Perform compensation processing to obtain the compensated detection data matrix This compensation method based on matrix operations can not only eliminate the influence of aging factors, but also take into account other interference factors such as ambient temperature and humidity, dynamic changes in detection data, etc., making the compensated data more accurate and reliable.

[0121] On this basis, the present invention adopts a method for judging the starting conditions based on a comprehensive evaluation function. The ratio of the compensated detection value to the preset concentration threshold, as well as the fluctuation of the detection data, are comprehensively considered. By setting the weight coefficient of different gases and sensitivity index , can flexibly adjust the degree of influence of various harmful gases on the startup judgment. Exceeding the preset start threshold When the start-up conditions are met, a start-up instruction is sent to the indoor ventilation device.

[0122] In order to further improve the adaptability of the system, the present invention also introduces a dynamic correction model for the startup threshold value according to the changes in ambient temperature and humidity. By real-time monitoring of temperature and humidity deviations and , and introduce the correction coefficient and , the startup threshold can be adjusted dynamically , ensuring that accurate and reliable startup judgments can be given under different environmental conditions.

[0123] Two specific embodiments of the present invention are provided below.

[0124] Example 1: In order to improve the air quality in its wards, a hospital in a certain city decided to adopt the indoor ventilation device start-up judgment method proposed in the present invention. The hospital has a total of 10 wards, each with an area of ​​approximately The average population density is In order to comprehensively monitor the concentration of harmful gases in the room, the hospital purchased a sensor array consisting of five gas sensors (CO2 sensor, CO sensor, TVOC sensor, PM2.5 sensor, and formaldehyde sensor) and installed it on the ceiling of each ward.

[0125] Initial installation and calibration

[0126] First, hospital engineers performed the initial installation and calibration of the sensor array. This included:

[0127] 1) Sensor Installation: Install the five gas sensors evenly distributed on the ceiling of each ward, ensuring that the installation locations are unobstructed and allow for easy air circulation. Also, connect the sensor power and signal cables.

[0128] 2) Sensor Verification. Using standard gas samples, calibrate and verify each of the five gas sensors individually to ensure that the detection accuracy and linearity of each sensor meet the requirements. Record the verification results for each sensor.

[0129] 3) System debugging. Connect each sensor to the main control computer, and then connect the main control computer to the hospital's central control system. Write the relevant software program and debug the sensor data collection, transmission, and processing functions to ensure stable system operation.

[0130] 4) Threshold calibration. According to the "Indoor Air Quality Standard" and other relevant regulations, set the concentration thresholds and fluctuation thresholds for various types of harmful gases. At the same time, use the response surface method to establish a comprehensive evaluation index optimization model, and use the particle swarm optimization algorithm to solve the optimal threshold combination. The specific threshold settings are shown in the following table:

[0131] Table 1-1 Harmful gas concentrations and fluctuation thresholds

[0132] Gas indicators Concentration threshold range Optimal concentration Fluctuation threshold range Fluctuation Optimum CO2 800-1500ppm 1000ppm 0.15-0.25 0.20 CO 5-30ppm 10ppm 0.10-0.20 0.15 TVOC 0.3-0.6mg / m³ 0.4mg / m³ 0.20-0.30 0.25 PM2.5 35-75 μg / m³ 50 μg / m³ 0.25-0.35 0.30 formaldehyde 0.08-0.12mg / m³ 0.1mg / m³ 0.15-0.25 0.20

[0133] 5) Temperature and humidity correction model calibration. Repeat steps 1-4 above under different temperature and humidity conditions to obtain the startup threshold under different environments. Data. Linear regression method is used to establish the temperature and humidity correction model ,in , , .

[0134] Through the above initial installation and calibration work, the hospital's indoor environment monitoring system is basically ready and can enter the formal operation stage.

[0135] System operation and data analysis

[0136] On March 1, 2022, the hospital's indoor environment monitoring system was officially put into operation. Collect the detection data of each sensor once to form dimensional sensor data matrix Taking a certain ward as an example, the test data on the first day is shown in the following table:

[0137] Table 1-2 Sensor detection data of a ward on the first day

[0138] time CO2 (ppm) CO (ppm) TVOC (mg / m³) PM2.5 (μg / m³) Formaldehyde (mg / m³) 00:00 950 8 0.42 47 0.09 00:01 955 9 0.43 48 0.10 00:02 960 9 0.41 50 0.10 … … … … … … 23:58 1010 12 0.48 58 0.12 23:59 1020 13 0.50 60 0.13

[0139] As can be seen from the table, carbon dioxide concentrations in this ward gradually increased during the night, while other harmful gases such as CO, TVOC, PM2.5, and formaldehyde also fluctuated to some extent. This may be due to factors such as doors and windows being closed at night and reduced activity.

[0140] Next, hospital engineers further processed and analyzed the test data according to the method of the present invention:

[0141] 1) Calculate the fluctuation of sensor data. According to the relative change rate of the detection value at adjacent time points , constructing the volatility matrix Taking the CO2 sensor as an example, its fluctuation is as follows:

[0142] ;

[0143] ;

[0144] …

[0145] ;

[0146] It can be seen that the fluctuation of CO2 sensor is Similarly, the fluctuation matrices of the other four sensors are They have all been calculated.

[0147] 2) Estimate the sensor aging rate. According to the aforementioned time weight coefficient , combined with the volatility matrix ,The hospital engineer calculated the aging rate matrix of each sensor :

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] ;

[0153] From the results, it can be seen that the aging rate of PM2.5 sensor is the highest, while the aging rate of CO sensor is relatively low.

[0154] 3) Perform data compensation. According to the above aging compensation equations, combined with the aging rate matrix , compensation coefficient matrix ( ), the second-order time derivative term and attenuation terms ( , , ), hospital engineers calculated the compensated detection data matrix Taking a certain moment as an example, the data comparison before and after compensation is as follows:

[0155] Table 1-3 Comparison of sensor data before and after compensation at a certain moment

[0156] index Before compensation After compensation Relative error CO2 (ppm) 1000 980 -2.0% CO (ppm) 10 9 -10.0% TVOC (mg / m³) 0.43 0.41 -4.7% PM2.5 (μg / m³) 50 46 -8.0% Formaldehyde (mg / m³) 0.10 0.09 -10.0%

[0157] From the comparison results, it can be seen that the compensated detection data is closer to the actual situation, and the relative error is controlled within 10%, which greatly improves the reliability of the data.

[0158] 4) Start condition judgment. Hospital engineers use the above comprehensive evaluation function to determine the start condition. , combined with the compensated detection data and volatility matrix , the startup evaluation index of the ward was calculated:

[0159] ;

[0160] ;

[0161] ;

[0162] because ,Therefore, the ward meets the starting conditions.

[0163] 5) Start the ventilation system. Finally, based on the above judgment, the hospital control center sends a start command to the ventilation system in the ward. The ventilation equipment immediately begins operating to regulate the indoor air quality.

[0164] The above is a specific example of a hospital operating an indoor environment monitoring system using the method described in this invention. Through intelligent processing, including sensor data collection, aging compensation, and startup condition determination, the system accurately monitors indoor harmful gas concentrations and automatically controls ventilation, effectively improving the air quality of hospital wards.

[0165] It should be noted that Example 1 above only applies to a single ward. In reality, all ten wards in the hospital are equipped with the same monitoring system and are all connected to the hospital's central control system. Therefore, hospital engineers can further analyze environmental monitoring data from each ward, compare differences between wards, and provide decision support for the hospital's overall environmental management. Furthermore, as time passes, the aging of each sensor will gradually increase. The hospital needs to regularly update and calibrate the aging compensation model to ensure the long-term stability of the monitoring data.

[0166] Example 2: Specific application of the method for determining the activation of ventilation devices in large enclosed warehouses

[0167] This Example 2 uses a large, enclosed warehouse for electronic components as its application scenario. The warehouse, with a floor area of ​​2,000 square meters, stores a variety of electronic components and related materials. Because electronic components have high requirements for the storage environment, strict monitoring of the air quality within the warehouse is required. In particular, the concentrations of harmful gases such as carbon dioxide, carbon monoxide, TVOC, PM2.5, and formaldehyde must be controlled.

[0168] System configuration: This embodiment uses a sensor array consisting of 5 gas sensors, the specific configuration of which is shown in Table 1.

[0169] Table 1 Sensor array configuration information

[0170] Serial number Detection gas model Measuring range Resolution 1 CO2 SCD30 0-5000ppm 1ppm 2 CO MQ7-V2 0-200ppm 0.1ppm 3 TVOC SGP30 0-60mg / m³ 0.01mg / m³ 4 PM2.5 PMS7003 0-1000μg / m³ 1μg / m³ 5 formaldehyde DS-HCHO 0-5mg / m³ 0.01mg / m³

[0171] The system sampling period is set to 1 minute, with continuous monitoring for 24 hours. The ventilation device uses a variable frequency fan, which can automatically adjust the speed according to the control command. The system operating environment temperature is 25±2℃ and the relative humidity is 45±5%.

[0172] Data Collection and Processing: During a typical monitoring cycle, the system continuously collected 1440 data points (24 hours x 60 minutes). Table 2 shows some of the raw detection data for a certain period of time.

[0173] Table 2: Raw detection data fragment (10:00-10:05)

[0174] Time point CO2 (ppm) CO (ppm) TVOC (mg / m³) PM2.5 (μg / m³) Formaldehyde (mg / m³) 10:00 856 8.2 0.35 48 0.09 10:01 862 8.4 0.36 49 0.09 10:02 875 8.6 0.38 51 0.10 10:03 892 8.9 0.41 54 0.11 10:04 915 9.3 0.45 58 0.12 10:05 945 9.8 0.48 62 0.13

[0175] The volatility matrix fragment calculated based on this data is shown in Table 3.

[0176] Table 3 Data fluctuations (10:00-10:05)

[0177] Time period CO2 CO TVOC PM2.5 formaldehyde 10:00-10:01 0.007 0.024 0.029 0.021 0.000 10:01-10:02 0.015 0.024 0.056 0.041 0.111 10:02-10:03 0.019 0.035 0.079 0.059 0.100 10:03-10:04 0.026 0.045 0.098 0.074 0.091 10:04-10:05 0.033 0.054 0.067 0.069 0.083

[0178] Aging rate calculation: The system uses a time weight coefficient to calculate the aging rate, with recent data having a higher weight. The time weight coefficients used in this embodiment are shown in Table 4.

[0179] Table 4 Time weight coefficient settings

[0180] Time period Weight coefficient Last 1 hour 0.4 1-6 hours 0.3 6-12 hours 0.2 12-24 hours 0.1

[0181] Based on the 24-hour monitoring data, the aging rate of each sensor is calculated and shown in Table 5.

[0182] Table 5 Sensor aging rate

[0183] sensor Aging rate CO2 0.082 CO 0.095 TVOC 0.108 PM2.5 0.091 formaldehyde 0.087

[0184] Aging compensation: The compensation coefficient matrix obtained by experimental calibration of the system is shown in Table 6.

[0185] Table 6 Compensation coefficient matrix

[0186] sensor CO2 CO TVOC PM2.5 formaldehyde Compensation coefficient 0.15 0.18 0.22 0.17 0.16

[0187] The acceleration influence coefficient λ is set to 0.05, the attenuation coefficient η is set to 0.3, and the time constant β is set to 0.001. The compensated data obtained through aging compensation calculation are shown in Table 7.

[0188] Table 7 Detection data after compensation (10:00-10:05)

[0189] Time point CO2 (ppm) CO (ppm) TVOC (mg / m³) PM2.5 (μg / m³) Formaldehyde (mg / m³) 10:00 890 8.8 0.39 51 0.10 10:01 897 9.0 0.40 52 0.10 10:02 912 9.2 0.42 54 0.11 10:03 931 9.5 0.45 57 0.12 10:04 956 9.9 0.49 61 0.13 10:05 988 10.4 0.52 65 0.14

[0190] Threshold setting: The gas concentration thresholds and fluctuation thresholds used in this embodiment are shown in Table 8.

[0191] Table 8 Gas monitoring threshold settings

[0192] Gas type Concentration threshold Fluctuation threshold CO2 1000ppm 0.20 CO 10ppm 0.15 TVOC 0.4mg / m³ 0.25 PM2.5 50 μg / m³ 0.30 formaldehyde 0.1mg / m³ 0.20

[0193] Start-up condition judgment: The weight coefficient and sensitivity index settings of each gas in this embodiment are shown in Table 9.

[0194] Table 9 Startup judgment parameter settings

[0195] Gas type Weight coefficient Sensitivity Index CO2 0.25 2.0 CO 0.25 2.2 TVOC 0.20 1.8 PM2.5 0.15 1.7 formaldehyde 0.15 2.1

[0196] The fluctuation impact factor μ is set to 0.3. Calibration experiments determined that the startup threshold S0 under standard conditions (25°C, 45% relative humidity) is 1.2. The temperature correction coefficient kT is 0.02, and the humidity correction coefficient kH is 0.01.

[0197] Actual Operational Results: During actual operation in this warehouse, the system calculated a comprehensive evaluation function value (S) of 1.28 at 10:05, exceeding the activation threshold (S0) of 1.2. At this time, the CO2 concentration (988 ppm) was approaching the threshold, while the CO concentration (10.4 ppm) had exceeded it. Furthermore, the fluctuations in the values ​​of several gases were significant, prompting the system to automatically initiate a start command for the ventilation system. After the ventilation system was activated, various indicators gradually decreased and stabilized within 15 minutes. Table 10 shows the data changes during the ventilation process.

[0198] Table 10 Changes in ventilation process data

[0199] Time point CO2 (ppm) CO (ppm) TVOC (mg / m³) PM2.5 (μg / m³) Formaldehyde (mg / m³) 10:05 988 10.4 0.52 65 0.14 10:10 920 9.1 0.45 58 0.12 10:15 865 8.2 0.38 49 0.10 10:20 832 7.8 0.34 45 0.09

[0200] After one month of operation in the warehouse, the system achieved remarkable results:

[0201] 1. The time when harmful gases exceed the standard is reduced by 85%, and the air quality in the warehouse is significantly improved.

[0202] 2. The operation time of ventilation equipment is reduced by 30% compared with the original, saving energy consumption.

[0203] 3. Reduced the frequency of manual inspections and improved management efficiency.

[0204] 4. The quality of stored electronic components remains stable, reducing losses caused by environmental problems.

[0205] This second example applies this ventilation system activation determination method in a large, enclosed warehouse, achieving intelligent monitoring and management of the warehouse environment and achieving excellent results. The system automatically adjusts the activation threshold based on actual environmental conditions, ensuring accurate and reliable determination. This method can be extended to other similar confined space environment monitoring scenarios.

[0206] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for determining whether an indoor ventilation device is started, characterized in that: The following steps are involved: S10, acquiring detection data of multiple indoor harmful gas sensor arrays in real time; S20, recording fluctuations in detection data of the plurality of harmful gas sensor arrays within a preset time period; S30, calculating the aging rates of the plurality of harmful gas sensor arrays according to the fluctuation of the detection data; S40, based on the aging rates of the plurality of harmful gas sensor arrays, using a pre-established aging compensation equation group, inputting the detection data into the compensation equation for calculation to obtain compensated detection data; S50, comparing the compensated detection data and data fluctuation with a preset harmful gas concentration threshold and fluctuation threshold; S60, judging the start condition using a preset empirical formula based on the compensated detection data; S70: When the compensated detection data exceeds the preset harmful gas concentration threshold and satisfies the start-up condition, sending a start-up instruction to the indoor ventilation device; The harmful gas sensor array is specifically a sensor array composed of multiple gas sensors, each sensor detects different types of harmful gases, and the detection data constitutes a sensor data matrix; Wherein, the sensing data matrix is ​​specifically expressed as: ; Where, Indicates the The sensor in the The detection value at each time point, , ; is the number of sensors, the value range is 3-8; is the number of sampling time points; The data fluctuation is represented by a fluctuation matrix: ; Where, Indicates the relative rate of change at adjacent time points; Among them, the aging rate of multiple harmful gas sensor arrays is represented by the sensor aging rate matrix: ; Where, is the time weight coefficient, satisfying and ; The aging compensation equation group is specifically expressed as: ; Where, is the data matrix after compensation; is the compensation coefficient matrix, obtained through experimental calibration; is the acceleration influence coefficient, ranging from 0.01 to 0.1; is the second-order time derivative of the detection data; is the attenuation coefficient, ranging from 0.1 to 0.5; is the time constant; is the identity matrix; is the running time; Among them, the starting condition judgment adopts the comprehensive evaluation function: ; Where, For the The concentration of harmful gases detected by each sensor after compensation; is the corresponding harmful gas concentration threshold; is the weight coefficient, satisfying ; is the sensitivity index, ranging from 1.5 to 2.5; is the volatility factor; is the real-time fluctuation value; is the fluctuation threshold, when The start command is triggered when is the start threshold.

2. A method for determining whether an indoor ventilation device is started according to claim 1, characterized in that: The start threshold is calibrated by the following steps: Step 1: Under standard conditions, gradually increase the concentration of various harmful gases and record the detection data when ventilation needs are triggered; Step 2: Substitute the obtained data into the evaluation function and take the lower limit of the 90% confidence interval as ; Step 3: Repeat steps 1-2 under different temperature and humidity conditions to establish Temperature and humidity correction model: ; Where, is the threshold value under standard conditions; are the temperature and humidity correction factors respectively; are the temperature and humidity deviations, respectively.

3. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the method for determining the start-up of an indoor ventilation device according to any one of claims 1 to 2.

4. An electronic device for determining whether an indoor ventilation device is activated, characterized in that: The method comprises a processor, a memory and a plurality of harmful gas sensor arrays, wherein the memory is used to store the step program of the method according to any one of claims 1 to 2, and the processor reads the memory and executes the steps.

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