Real-time dust monitoring method based on Internet of Things

Abstract

The invention discloses a real-time dust monitoring method based on the Internet of Things, and relates to the technical field of dust monitoring, and the method comprises the following steps: when dust monitoring is carried out, dust data are obtained, and the dust data comprise dust particle concentration, temperature and humidity; preprocessing the dust data, and drawing a dust data change curve graph; obtaining a dust concentration early warning value; the dust data change curve graph is judged based on the dust concentration early warning value, and an early warning signal is sent based on a judgment result; after the early warning signal is received, a fan valve is started to clean indoor dust; acquiring cleaning data in real time, judging the cleaning data based on the dust concentration early warning value, and sending a cleaning stop instruction based on a judgment result; the invention is used for solving the problem that the existing dust monitoring technology needs manual assistance and cannot accurately detect and treat dust in a detection area in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of dust monitoring, and in particular to a real-time dust monitoring method based on the Internet of Things. Background Art

[0002] According to national standards, solid suspended matter with a particle size of less than 75μm is defined as dust. In traditional production processes, dust is generated due to the preparation of raw materials. Even if personnel receive safety training before operation, safety and health are affected in traditional production due to the factory's dust detection and personnel's failure to correctly wear safety protection. In the production process of medium-borosilicate pharmaceutical glass, batching personnel need to prepare raw materials. Due to various force majeure factors, the room may contain silica dust (non-polar powder with a crystalline free silicon dioxide content of more than 10%) and toxic chemicals such as zinc oxide and arsenic oxide.

[0003] Existing indoor dust detection instruments are often manually checked by hour or shift, and then the results are summarized in a form. They are unable to accurately detect and process the dust in the detection area in real time. Therefore, a real-time dust monitoring method based on the Internet of Things is proposed to perform real-time detection of dust in the batching room, and to issue an early warning if the detection exceeds the standard. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent. The present invention obtains dust data in a detection area in real time, pre-processes the dust data, obtains a dust data change curve graph, judges the dust data change curve graph based on a dust concentration warning value, and performs a cleaning operation in the area based on the judgment result, so as to solve the problem that the existing dust monitoring technology requires manual assistance and cannot accurately detect and process the dust in the detection area in real time.

[0005] To achieve the above objectives, in a first aspect, the present application provides a real-time dust monitoring method based on the Internet of Things, comprising the following steps:

[0006] When performing dust monitoring, dust data is obtained, the dust data including dust particle concentration, temperature and humidity;

[0007] Pre-process the dust data and draw a dust data change curve;

[0008] Get dust concentration warning value;

[0009] The dust data change curve is judged based on the dust concentration warning value, and a warning signal is issued based on the judgment result;

[0010] After receiving the early warning signal, the fan valve is started to clean the indoor dust;

[0011] Acquire cleaning data in real time, judge the cleaning data based on the dust concentration warning value, and issue a stop cleaning instruction based on the judgment result.

[0012] Furthermore, when dust monitoring is performed, obtaining dust data, which includes dust particle concentration, temperature and humidity, includes the following sub-steps:

[0013] A first laser dust sensor, a second laser dust sensor, a third laser dust sensor, a temperature sensor and a humidity sensor are arranged in the monitoring area, the first laser dust sensor is arranged at the lowest point of the geometric center of the plane diagram of the monitoring area, the second laser dust sensor is arranged at the highest point of the geometric center of the plane diagram of the monitoring area, the third laser dust sensor is arranged at the exhaust port in the monitoring area, and the temperature sensor and the humidity sensor are arranged on the wall on any side of the monitoring area; when performing dust monitoring, the first laser dust sensor, the second laser dust sensor, the third laser dust sensor, the temperature sensor and the humidity sensor are started;

[0014] The monitoring data of the first laser dust sensor, the second laser dust sensor, the third laser dust sensor, the temperature sensor and the humidity sensor are acquired in real time and recorded as first dust particle concentration data, second dust particle concentration data, third dust particle concentration data, temperature data and humidity data.

[0015] Furthermore, the preprocessing of dust data and drawing of dust data change curve diagram includes the following sub-steps:

[0016] When the dust data is obtained, the first dust particle concentration data, the second dust particle concentration data, and the third dust particle concentration data are preprocessed to obtain regional dust particle concentration data and regional dust concentration differences;

[0017] Establish a plane rectangular coordinate system, with time as the horizontal coordinate and dust particle concentration as the vertical coordinate, fill the dust particle concentration data of the area into the coordinate system, and obtain a dust particle concentration change curve;

[0018] Establish a plane rectangular coordinate system, with time as the horizontal coordinate and dust particle concentration as the vertical coordinate, fill the regional dust concentration difference into the coordinate system, and obtain a regional dust concentration difference change curve;

[0019] Establish a plane rectangular coordinate system, with time as the horizontal coordinate and temperature as the vertical coordinate, fill the temperature data into the coordinate system, and obtain a temperature change curve graph;

[0020] A plane rectangular coordinate system is established, with time as the horizontal coordinate and humidity as the vertical coordinate, and the humidity data is filled into the coordinate system to obtain a humidity change curve diagram.

[0021] Furthermore, the preprocessing comprises the following sub-steps:

[0022] Calculate the average value of the first dust particle concentration data and the second dust particle concentration data, and set it as regional dust particle concentration data;

[0023] The absolute value of the difference between the regional dust particle concentration data and the third dust particle concentration data at the same time point is set as the regional dust concentration difference.

[0024] Furthermore, the step of obtaining the dust concentration warning value includes the following sub-steps:

[0025] Obtain the standard value of dust concentration in the workplace from the database;

[0026] Substitute the regional dust particle concentration data and the regional dust concentration difference into the dust concentration warning value formula Y = A 1 ×C q +A 2 ×C p The dust concentration warning value is calculated; where Y is the dust concentration warning value, C q is the regional dust particle concentration data, C p is the regional dust concentration difference, A 1 is the weight value of regional dust particle concentration, A 2 is the weight value of regional dust concentration difference.

[0027] Further, judging the dust data change curve based on the dust concentration warning value, issuing a warning signal based on the judgment result, and starting the fan valve to clean the indoor dust after receiving the warning signal includes the following sub-steps:

[0028] Obtain the regional dust concentration difference, temperature and humidity in real time from the regional dust concentration difference change curve, temperature change curve and humidity change curve, substitute them into the primary judgment formula, and calculate the primary judgment value;

[0029] When the first-level judgment value is greater than or equal to the dust concentration standard value, an early warning signal is issued;

[0030] When the first-level judgment value is less than the dust concentration standard value, the regional dust particle concentration at this time is obtained from the regional dust particle concentration change curve diagram;

[0031] When the dust particle concentration in the area is lower than the dust concentration warning value, a normal signal is issued;

[0032] When the dust particle concentration in the area is greater than or equal to the dust concentration warning value, a warning signal is issued;

[0033] After receiving the early warning signal, the fan valve is started to clean the dust in the room.

[0034] Furthermore, the first-level judgment formula is: Among them, P is the first-level judgment value, C p is the regional dust concentration difference, T is the temperature, ρv is the humidity, B 1 is the temperature weight value, B 2 is the humidity weight value.

[0035] Furthermore, the real-time acquisition of cleaning data, judging the cleaning data based on the dust concentration warning value, and issuing a stop cleaning instruction based on the judgment result include the following sub-steps:

[0036] Get the time point for executing the cleanup operation and set it as the cleanup point;

[0037] Set the data after the cleaning point in the dust data change curve as the cleaning data;

[0038] Substitute the cleaned data into the first-level judgment formula to calculate the first-level judgment value;

[0039] When the first-level judgment value is greater than or equal to the dust concentration standard value, a signal to continue cleaning is issued;

[0040] When the first-level judgment value is less than the dust concentration standard value, the regional dust particle concentration at this time is obtained from the regional dust particle concentration change curve diagram;

[0041] When the dust particle concentration in the area is lower than the dust concentration warning value, a signal to stop cleaning is issued;

[0042] When the dust particle concentration in the area is greater than or equal to the dust concentration warning value, a signal to continue cleaning is issued;

[0043] After receiving the signal to continue cleaning, close the fan valve.

[0044] Beneficial effects of the present invention: The present invention firstly performs comprehensive and multi-point monitoring of dust concentration, temperature and humidity in the monitoring area by setting a plurality of laser dust sensors, temperature sensors and humidity sensors, thereby ensuring the accuracy of the acquired data;

[0045] The present invention pre-processes the dust data, draws a dust data change curve based on the pre-processed data, and displays the real-time change of the dust data through the dust data change curve, so that the change of dust concentration is clear at a glance, which is convenient for subsequent real-time monitoring of dust concentration;

[0046] The present invention also obtains a dust concentration warning value; makes a real-time judgment on a dust data change curve based on the dust concentration warning value, and issues a warning signal based on the judgment result; after receiving the warning signal, starts a fan valve to clean the indoor dust; at the same time, obtains cleaning data in real time, judges the cleaning data based on the dust concentration warning value, and issues a stop cleaning instruction based on the judgment result, thereby completing a monitoring closed loop, reducing labor costs, and having high safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of the method steps of the present invention;

[0048] Figure 2 It is a dust particle concentration variation curve diagram of the present invention;

[0049] Figure 3 It is a dust concentration difference variation curve diagram of the present invention;

[0050] Figure 4 It is the temperature variation curve diagram of the present invention;

[0051] Figure 5 It is a humidity change curve diagram of the present invention;

[0052] Figure 6 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1, please refer to Figure 1 As shown, a real-time dust monitoring method based on the Internet of Things includes the following steps:

[0055] Step S1: when performing dust monitoring, dust data is obtained, and the dust data includes dust particle concentration, temperature and humidity; Step S1 includes the following sub-steps:

[0056] Step S101: a first laser dust sensor, a second laser dust sensor, a third laser dust sensor, a temperature sensor and a humidity sensor are arranged in the monitoring area, the first laser dust sensor is arranged at the lowest point of the geometric center of the plane diagram of the monitoring area, the second laser dust sensor is arranged at the highest point of the geometric center of the plane diagram of the monitoring area, the third laser dust sensor is arranged at the exhaust port in the monitoring area, and the temperature sensor and the humidity sensor are arranged on the wall on any side of the monitoring area; in the specific implementation process, the dust distribution in the monitoring area is generally irregular, and the dust near the exhaust port is often smaller than that in other areas, so only performing dust detection on a part of the detection area will lead to the detection result not being universal;

[0057] When performing dust monitoring, the first laser dust sensor, the second laser dust sensor, the third laser dust sensor, the temperature sensor and the humidity sensor are started;

[0058] Step S102: acquiring monitoring data of the first laser dust sensor, the second laser dust sensor, the third laser dust sensor, the temperature sensor, and the humidity sensor in real time, and recording the data as first dust particle concentration data, second dust particle concentration data, third dust particle concentration data, temperature data, and humidity data;

[0059] Step S2: pre-process the dust data and draw a dust data change curve; Step S2 includes the following sub-steps:

[0060] Step S201: when the dust data is acquired, pre-processing the first dust particle concentration data, the second dust particle concentration data, and the third dust particle concentration data is performed to obtain regional dust particle concentration data and regional dust concentration difference;

[0061] Step S201 includes the following sub-steps:

[0062] Step S20101: Calculate the average value of the first dust particle concentration data and the second dust particle concentration data, and set it as the regional dust particle concentration data; in the specific implementation process, some dust will be unevenly distributed in the monitoring area due to the influence of gravity, so it is necessary to collect dust data at the bottom and top at the same time, and use the averaging method to reduce errors;

[0063] Step S20102: setting the absolute value of the difference between the regional dust particle concentration data and the third dust particle concentration data at the same time point as the regional dust concentration difference;

[0064] See also Figure 2 , Figure 3 , Figure 4 and Figure 5As shown, step S202: establish a plane rectangular coordinate system, with time as the horizontal coordinate and dust particle concentration as the vertical coordinate, fill the regional dust particle concentration data into the coordinate system, and obtain a dust particle concentration change curve;

[0065] Step S203: Establish a plane rectangular coordinate system, with time as the horizontal coordinate and dust particle concentration as the vertical coordinate, fill the regional dust concentration difference into the coordinate system, and obtain a regional dust concentration difference change curve;

[0066] Step S204: Establish a plane rectangular coordinate system, with time as the horizontal coordinate and temperature as the vertical coordinate, fill the temperature data into the coordinate system, and obtain a temperature change curve graph;

[0067] Step S205: Establish a plane rectangular coordinate system, with time as the horizontal coordinate and humidity as the vertical coordinate, fill the humidity data into the coordinate system, and obtain a humidity change curve graph;

[0068] Step S3: Obtain dust concentration warning value; Step S3 includes the following sub-steps:

[0069] Step S301: Obtain the dust concentration standard value of the workplace from the database; in the specific implementation process, the dust concentration standard value will change due to changes in different working scenes. Generally, the dust concentration in the air is required not to exceed 10 mg / m 3 ;

[0070] Step S302: Substitute the regional dust particle concentration data, the dust concentration standard value and the regional dust concentration difference into the dust concentration warning value formula Y=A 1 ×C q +A 2 ×C p The dust concentration warning value is calculated; where Y is the dust concentration warning value, C q is the regional dust particle concentration data, C p is the regional dust concentration difference, A 1 is the weight value of regional dust particle concentration, A 2 is the weight value of regional dust concentration difference; in the specific implementation process, the weight value of regional dust particle concentration and the weight value of regional dust concentration difference are obtained by experiments. For example, in this embodiment, the weight value of regional dust particle concentration is 0.8, the weight value of regional dust concentration difference is 0.2, and the regional dust particle concentration data at a certain moment obtained from the dust data change curve is 12 mg / m 3 , the regional dust concentration difference is 6mg / m 3 The dust concentration warning value at this time is 10.8 mg / m 3 .

[0071] Step S4: judging the dust data change curve based on the dust concentration warning value, and issuing a warning signal based on the judgment result; after receiving the warning signal, starting the fan valve to clean the indoor dust; Step S4 includes the following sub-steps:

[0072] Step S401: Obtain the regional dust concentration difference, temperature and humidity in real time from the regional dust concentration difference change curve, temperature change curve and humidity change curve, substitute them into the primary judgment formula, and calculate the primary judgment value; the primary judgment formula is Among them, P is the first-level judgment value, C p is the regional dust concentration difference, T is the temperature, ρv is the humidity, B 1 is the temperature weight value, B 2 is the humidity weight value; in the specific implementation process, due to the different dust components, the effects of temperature and humidity on dust concentration will also be different, so the humidity weight value and temperature weight value are obtained by experiment. For example, the humidity weight value obtained in this embodiment is 12, the temperature weight value is 0.05, and the regional dust concentration difference at a certain moment obtained in the dust data change curve is 6 mg / m 3 , the temperature is 30℃, the humidity is 60%, and the first-level judgment value is 4.8;

[0073] Step S402: When the first-level judgment value is greater than or equal to the dust concentration standard value, an early warning signal is issued; in the specific implementation process, the first-level judgment value represents the normal value of the dust concentration difference in the area after being affected by temperature and humidity. When the first-level judgment value is greater than or equal to the dust concentration standard value, it indicates that the dust concentration in the central part of the detection area is much greater than the dust concentration at the exhaust port. It may be due to the rapid increase in dust concentration in the central area under working conditions, so it is necessary to issue an early warning for the working environment;

[0074] When the first-level judgment value is less than the dust concentration standard value, the regional dust particle concentration at this time is obtained from the regional dust particle concentration change curve diagram; when the regional dust particle concentration is less than the dust concentration warning value, a normal signal is issued;

[0075] When the dust particle concentration in the area is greater than or equal to the dust concentration warning value, a warning signal is issued;

[0076] Step S403: after receiving the warning signal, start the fan valve to clean the indoor dust;

[0077] Step S5: Acquire cleaning data in real time, judge the cleaning data based on the dust concentration warning value, and issue a stop cleaning instruction based on the judgment result.

[0078] Step S5 includes the following sub-steps:

[0079] Step S501: Obtain the time point at which the cleaning operation is performed and set it as the cleaning point;

[0080] Step S502: setting the data after the cleaning point in the dust data change curve graph as the cleaning data;

[0081] Step S503: Substitute the cleaned data into the first-level judgment formula to calculate and obtain the first-level judgment value;

[0082] When the first-level judgment value is greater than or equal to the dust concentration standard value, a signal to continue cleaning is issued;

[0083] When the first-level judgment value is less than the dust concentration standard value, the regional dust particle concentration at this time is obtained from the regional dust particle concentration change curve diagram;

[0084] When the dust particle concentration in the area is lower than the dust concentration warning value, a signal to stop cleaning is issued;

[0085] When the dust particle concentration in the area is greater than or equal to the dust concentration warning value, a signal to continue cleaning is issued;

[0086] Step S504: After receiving the continue cleaning signal, close the fan valve; in the specific implementation process, continuous monitoring of the dust concentration during the cleaning process can prevent the cleaning operation from being too long, resulting in excessive energy consumption. Therefore, when the regional dust particle concentration or the first-level judgment value is within the threshold, the cleaning operation is terminated.

[0087] Example 2, please refer to Figure 6 As shown, Figure 6 The schematic diagram of the structure of an electronic device is illustrated, and the electronic device may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a real-time dust monitoring method based on the Internet of Things are executed to achieve the following functions: by real-time acquisition of dust data in the detection area, after pre-processing the dust data, a dust data change curve is obtained, the dust data change curve is judged based on the dust concentration warning value, a cleaning operation is performed in the area based on the judgment result, and the cleaning data is monitored to control the cleaning time.

[0088] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0089] Embodiment 4. The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a real-time dust monitoring method based on the Internet of Things provided by the above-mentioned methods. The method includes: acquiring dust data in a detection area in real time, preprocessing the dust data, obtaining a dust data change curve graph, judging the dust data change curve graph based on a dust concentration warning value, performing cleaning operations in the area based on the judgment result, and monitoring the cleaning data to control the cleaning time.

[0090] Embodiment 5, the present application also provides a computer-readable storage medium, the present application provides a storage medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps in the above-mentioned real-time dust monitoring method based on the Internet of Things are executed. Through the above technical scheme, when the computer program is executed by the processor, the method in any optional implementation of the above-mentioned embodiment is executed to achieve the following functions: by real-time acquisition of dust data in the detection area, after pre-processing the dust data, a dust data change curve chart is obtained, the dust data change curve chart is judged based on the dust concentration warning value, the cleaning operation is performed in the area based on the judgment result, and the cleaning data is monitored to control the cleaning time.

[0091] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0092] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A real-time dust monitoring method based on the Internet of Things, characterized in that: The steps include: When performing dust monitoring, dust data is obtained, the dust data including dust particle concentration, temperature and humidity; Pre-process the dust data and draw a dust data change curve; Get dust concentration warning value; The dust data change curve is judged based on the dust concentration warning value, and a warning signal is issued based on the judgment result; after receiving the warning signal, the fan valve is started to clean the indoor dust; Acquire cleaning data in real time, judge the cleaning data based on the dust concentration warning value, and issue a stop cleaning instruction based on the judgment result.

2. The real-time dust monitoring method based on the Internet of Things according to claim 1 is characterized in that: When performing dust monitoring, obtaining dust data, which includes dust particle concentration, temperature and humidity, includes the following sub-steps: A first laser dust sensor, a second laser dust sensor, a third laser dust sensor, a temperature sensor and a humidity sensor are arranged in the monitoring area, the first laser dust sensor is arranged at the lowest point of the geometric center of the plane diagram of the monitoring area, the second laser dust sensor is arranged at the highest point of the geometric center of the plane diagram of the monitoring area, the third laser dust sensor is arranged at the exhaust port in the monitoring area, and the temperature sensor and the humidity sensor are arranged on the wall on any side of the monitoring area; when performing dust monitoring, the first laser dust sensor, the second laser dust sensor, the third laser dust sensor, the temperature sensor and the humidity sensor are started; The monitoring data of the first laser dust sensor, the second laser dust sensor, the third laser dust sensor, the temperature sensor and the humidity sensor are acquired in real time and recorded as first dust particle concentration data, second dust particle concentration data, third dust particle concentration data, temperature data and humidity data.

3. The real-time dust monitoring method based on the Internet of Things according to claim 2 is characterized in that: The preprocessing of dust data and drawing of dust data change curve diagram comprises the following sub-steps: When the dust data is obtained, the first dust particle concentration data, the second dust particle concentration data, and the third dust particle concentration data are preprocessed to obtain regional dust particle concentration data and regional dust concentration differences; Establish a plane rectangular coordinate system, with time as the horizontal coordinate and dust particle concentration as the vertical coordinate, fill the dust particle concentration data of the area into the coordinate system, and obtain a dust particle concentration change curve; Establish a plane rectangular coordinate system, with time as the horizontal coordinate and dust particle concentration as the vertical coordinate, fill the regional dust concentration difference into the coordinate system, and obtain a regional dust concentration difference change curve; Establish a plane rectangular coordinate system, with time as the horizontal coordinate and temperature as the vertical coordinate, fill the temperature data into the coordinate system, and obtain a temperature change curve graph; A plane rectangular coordinate system is established, with time as the horizontal coordinate and humidity as the vertical coordinate, and the humidity data is filled into the coordinate system to obtain a humidity change curve diagram.

4. The real-time dust monitoring method based on the Internet of Things according to claim 3 is characterized in that: The pre-processing comprises the following sub-steps: Calculate the average value of the first dust particle concentration data and the second dust particle concentration data, and set it as regional dust particle concentration data; The absolute value of the difference between the regional dust particle concentration data and the third dust particle concentration data at the same time point is set as the regional dust concentration difference.

5. The real-time dust monitoring method based on the Internet of Things according to claim 4 is characterized in that: The step of obtaining the dust concentration warning value includes the following sub-steps: Obtain the standard value of dust concentration in the workplace from the database; Substitute the regional dust particle concentration data and the regional dust concentration difference into the dust concentration warning value formula Y = A1 × C q +A2×C p The dust concentration warning value is calculated; where Y is the dust concentration warning value, C q is the regional dust particle concentration data, C p is the regional dust concentration difference, A1 is the regional dust particle concentration weight value, and A2 is the regional dust concentration difference weight value.

6. The real-time dust monitoring method based on the Internet of Things according to claim 5 is characterized in that: The dust data change curve is judged based on the dust concentration warning value, a warning signal is issued based on the judgment result, and after receiving the warning signal, the fan valve is started to clean the indoor dust, which includes the following sub-steps: Obtain the regional dust concentration difference, temperature and humidity in real time from the regional dust concentration difference change curve, temperature change curve and humidity change curve, substitute them into the primary judgment formula, and calculate the primary judgment value; When the first-level judgment value is greater than or equal to the dust concentration standard value, an early warning signal is issued; When the first-level judgment value is less than the dust concentration standard value, the regional dust particle concentration at this time is obtained from the regional dust particle concentration change curve diagram; When the dust particle concentration in the area is lower than the dust concentration warning value, a normal signal is issued; When the dust particle concentration in the area is greater than or equal to the dust concentration warning value, a warning signal is issued; After receiving the early warning signal, the fan valve is started to clean the dust in the room.

7. The real-time dust monitoring method based on the Internet of Things according to claim 6 is characterized in that: The first-level judgment formula is: Among them, P is the first-level judgment value, C p is the regional dust concentration difference, T is the temperature, ρv is the humidity, B1 is the temperature weight value, and B2 is the humidity weight value.

8. The real-time dust monitoring method based on the Internet of Things according to claim 7 is characterized in that: The real-time acquisition of cleaning data, judging the cleaning data based on the dust concentration warning value, and issuing a stop cleaning instruction based on the judgment result include the following sub-steps: Get the time point for executing the cleanup operation and set it as the cleanup point; Set the data after the cleaning point in the dust data change curve as the cleaning data; Substitute the cleaned data into the first-level judgment formula to calculate the first-level judgment value; When the first-level judgment value is greater than or equal to the dust concentration standard value, a signal to continue cleaning is issued; When the first-level judgment value is less than the dust concentration standard value, the regional dust particle concentration at this time is obtained from the regional dust particle concentration change curve diagram; When the dust particle concentration in the area is lower than the dust concentration warning value, a signal to stop cleaning is issued; When the dust particle concentration in the area is greater than or equal to the dust concentration warning value, a signal to continue cleaning is issued; After receiving the signal to continue cleaning, close the fan valve.

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