Fan rotating speed adjusting system based on PLC control technology

Abstract

The invention discloses a fan rotating speed adjusting system based on a PLC control technology, relates to the technical field of fan rotating speed adjustment, and solves the technical problem that in the prior art, the actual adjusting effect is not accurate enough due to the fact that air pressure changes caused by temperature rise during operation of equipment is not considered. According to the method, the equipment working condition information and the external environment information of the equipment are collected through the sensor, and the equipment working condition information is compared with the operation range extracted from the database to obtain the equipment state; based on the equipment working condition information and the external environment information, the fan required rotating speed of the fan is obtained through calculation; the required rotating speed of the draught fan is adjusted according to the equipment state, the corresponding draught fan adjusting rotating speed is obtained for different working states, it is ensured that the equipment runs in the most energy-saving state under different working conditions, and unnecessary energy waste is reduced; and the adjustment working condition information of the equipment is collected again, and the adjustment working condition information is compared with the operation range to obtain an adjustment effect.

Description

Technical Field

[0001] The invention belongs to the field of fan speed regulation, and in particular is a fan speed regulation system based on PLC control technology. Background Art

[0002] With the rapid development of industrial automation, more and more equipment requires precise control and management. As a key component in many industrial equipment, the precise control of the fan speed is of great significance to improving the energy efficiency of the equipment, extending the life of the equipment and ensuring the safe operation of the equipment.

[0003] The invention patent with application number CN2018110573135 discloses an automatic ventilation system and ventilation adjustment method for polar ship machinery spaces. The invention detects the average temperature of the machine through a temperature sensor, transmits the temperature signal to the PLC control system, collects the internal pressure and external pressure of the machine through a pressure sensor, and also sends the pressure difference signal to the PLC control system. The blower inverter and the exhaust fan inverter are controlled according to the temperature signal and the pressure difference signal of the machine to adjust the fan speed; the invention adjusts the fan speed and air volume by comparing the temperature signal with the target temperature, and adjusts the exhaust fan air volume by comparing the pressure signal with the target pressure; when the equipment is running, the heat generated by it will cause the temperature to rise, and the temperature inside the equipment will also affect the change of the internal air pressure. If the impact of the temperature rise on the equipment is not taken into account, the final adjusted wind speed is not accurate enough for the actual situation, which will lead to a reduction in the cost-effectiveness of energy costs.

[0004] The present invention provides a fan speed adjustment system based on PLC control technology to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a fan speed adjustment system based on PLC control technology, which is used to solve the technical problem that the prior art does not take into account the temperature increase of the equipment during operation, causing the air pressure to change, which ultimately leads to the actual adjustment effect is not accurate enough.

[0006] To achieve the above-mentioned object, the first aspect of the present invention provides a fan speed adjustment system based on PLC control technology, comprising: a data collection module, a wind speed adjustment module and an effect detection module;

[0007] Data collection module: used to collect equipment operating information and external environment information;

[0008] Wind speed adjustment module: used to obtain the equipment status of the equipment according to the equipment working condition information; obtain the required fan speed based on the equipment working condition information and the external environment information, and adjust the fan required speed according to the equipment status to obtain the fan adjustment speed;

[0009] Effect detection module: used to detect the equipment working condition to obtain the adjustment working condition information, and obtain the adjustment effect based on the adjustment working condition information.

[0010] Preferably, obtaining the equipment status of the equipment according to the equipment operating condition information includes:

[0011] A1: Extract equipment operating information; the equipment operating information includes the equipment's operating status and operating data; the operating status includes the start-up status and the shutdown status; the operating data includes the equipment's real-time temperature and real-time pressure;

[0012] A2: Determine whether the device is in the power-on state; if yes, jump to A3; if no, jump to A4;

[0013] A3: Extract the startup operation range from the historical experiment database; determine whether the operation data of the device meets the startup operation range; if yes, mark the device working state of the corresponding device as normal startup state; if no, mark the device working state of the corresponding device as abnormal startup state, and jump to A5;

[0014] A4: Extract the shutdown operation range from the historical experiment database; determine whether the operation data of the equipment meets the shutdown operation range; if yes, mark the equipment working state of the corresponding equipment as normal shutdown state; if no, mark the equipment working state of the corresponding equipment as abnormal shutdown state, and jump to A5;

[0015] A5: Integrate the startup operation range and the shutdown operation range and mark them as the equipment operation range; determine whether the real-time temperature is greater than the corresponding equipment operation range; if yes, mark the temperature state of the corresponding equipment as overheating; if no, mark the temperature state of the corresponding equipment as low temperature;

[0016] A6: Determine whether the real-time pressure is greater than the corresponding device operating range; if yes, mark the pressure state of the corresponding device as overpressure; if no, mark the pressure state of the corresponding device as underpressure;

[0017] A7: Integrate the equipment working status, temperature status and pressure status to obtain the equipment status.

[0018] Preferably, obtaining the fan required speed based on the equipment operating condition information and the external environment information includes:

[0019] Extracting equipment operating condition information and external environment information; wherein the external environment information includes the external temperature, air quality and atmospheric pressure outside the equipment;

[0020] The difference between the real-time temperature of the equipment and the corresponding equipment operating range is marked as the equipment temperature difference, and the difference between the real-time pressure of the equipment and the corresponding equipment operating range is marked as the equipment pressure difference; the current fan speed is obtained by extracting from the database;

[0021] The fan demand speed XQV is calculated by the formula XQV=[1+(SCT-WBT)×α1 / JZT]+[1+(SCP-WBP)×α2 / JZP]×β×DQS; wherein SCT represents the equipment temperature difference, WBT represents the external temperature, α1 represents the temperature influence coefficient, SCP represents the equipment pressure difference, WBP represents the atmospheric pressure, α2 represents the pressure influence coefficient, DQS represents the current fan speed, JZT represents the temperature reference difference, JZP represents the pressure reference difference, the temperature reference difference is the unit of temperature change, the pressure reference difference is the unit of pressure change, β represents the air quality influence coefficient, and the temperature influence coefficient, pressure influence coefficient and air quality influence coefficient are set according to historical experimental data.

[0022] Preferably, the temperature influence coefficient, pressure influence coefficient and air quality influence coefficient are set according to historical experimental data, including:

[0023] Extract historical experimental data from the historical experiment database;

[0024] Mark the historical experimental data group in which only temperature changes in the historical experimental data as temperature change experimental data, mark the historical experimental data group in which only pressure changes in the historical experimental data as pressure change experimental data, and mark the historical experimental data in which only air quality changes in the historical experimental data as air quality change experimental data; and mark the corresponding changed temperature, pressure and air quality as change data;

[0025] The change category corresponding to the temperature change experimental data is marked as temperature change, the change category corresponding to the pressure change experimental data is marked as pressure change, and the change category corresponding to the air quality change experimental data is marked as air quality change; an initial neural network model is constructed, and the temperature change experimental data, the pressure change experimental data, the air quality change experimental data and the corresponding fan speed are used as training data and test data, the neural network model is trained using the training data, and the trained neural network model is tested using the test data, and the parameters of the neural network model are adjusted according to the test results to obtain an influence coefficient model in which the input data is the change data and the change category, and the output data is the corresponding influence coefficient;

[0026] The equipment temperature difference and change category are input into the influence coefficient model as temperature change to obtain the temperature change influence coefficient. The equipment pressure difference and change category are input into the influence coefficient model as pressure change to obtain the pressure change influence coefficient. The air quality and change category are input into the influence coefficient model as air quality change to obtain the air quality influence coefficient.

[0027] Preferably, the step of adjusting the fan demand speed according to the equipment state to obtain the fan adjustment speed includes:

[0028] S1: Extract device status;

[0029] S2: Determine whether the device is in the power-on state; if yes, jump to S3; if no, jump to S4;

[0030] S3: Set the heat dissipation coefficient; calculate the product of the real-time temperature and the heat dissipation coefficient and the product of the required wind speed to obtain the fan adjustment speed;

[0031] S4: Set the shutdown threshold; determine whether the fan demand speed is greater than the shutdown threshold; if yes, maintain the fan adjustment speed; if no, turn off the fan switch and mark the fan adjustment speed as zero.

[0032] Preferably, obtaining the adjustment effect based on the adjustment working condition information includes:

[0033] B1: extracting the adjustment working condition information; wherein the adjustment working condition information includes the working state of the adjustment device after adjustment, the adjustment temperature state and the adjustment pressure state;

[0034] B2: Determine whether the adjusted running state is the power-on state; if yes, jump to B3; if no, jump to B4;

[0035] B3: Determine whether the adjustment condition information meets the startup operation range; if yes, mark the adjustment effect as effective adjustment; if no, mark the adjustment effect as invalid adjustment;

[0036] B4: Determine whether the adjustment condition information meets the shutdown operation range; if yes, mark the adjustment effect as effective adjustment; if no, mark the adjustment effect as invalid adjustment.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. The present invention collects the equipment operating condition information and external environment information of the equipment through sensors, compares the equipment operating condition information with the operating range extracted from the database, obtains the equipment status, and can ensure real-time grasp of the equipment status; the fan demand speed of the fan is calculated based on the equipment operating condition information and the external environment information, and the wind speed is accurately controlled to effectively dissipate heat, prevent equipment from overheating, reduce mechanical wear, and extend the service life of the equipment; the fan demand speed is adjusted according to the equipment status, and the corresponding fan adjustment speed is obtained for different working states, ensuring that the equipment operates in the most energy-saving state under different working conditions, reducing unnecessary energy waste, and reasonably adjusting the fan speed can reduce the wear of mechanical parts, extend the service life of the equipment, and improve the stability and reliability of the equipment; the equipment adjustment operating condition information is re-collected, and the adjustment operating condition information is compared with the operating range to obtain the adjustment effect, which can verify the adjustment effect, continuously optimize the control strategy and equipment design, and improve the performance and reliability of the equipment.

[0039] 2. The present invention trains a neural network model through historical experimental data to obtain an influence coefficient model, inputs the equipment temperature difference, equipment pressure difference and air quality into the influence coefficient model to obtain the temperature influence coefficient, pressure influence coefficient and air quality influence coefficient, and combines the equipment operating condition information with the external environment information to calculate the required fan speed, which can make the calculation result more accurate, thereby achieving more precise control of the fan speed, ensuring that the equipment runs stably in the adjusted state, and improving the reliability and stability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 The following is a flowchart of an embodiment of the present invention.

[0042] Figure 2 It is a system composition diagram of the present invention.

[0043] Figure 3 The following is a complete flowchart for obtaining device status in one embodiment of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0045] See also Figure 1-Figure 3 , the first aspect of the present invention provides a fan speed adjustment system based on PLC control technology, including: a data collection module, a wind speed adjustment module and an effect detection module;

[0046] Data collection module: used to collect equipment operating condition information and external environment information.

[0047] Exemplarily, multiple temperature sensors and pressure sensors are arranged inside and outside the gas blower to collect real-time temperature and real-time pressure inside the gas blower and external environmental information outside the gas blower, and the operating status of the gas blower is obtained according to the management system.

[0048] Wind speed adjustment module: used to obtain the equipment status of the equipment according to the equipment working condition information; obtain the fan demand speed based on the equipment working condition information and the external environment information, and adjust the fan demand speed according to the equipment status to obtain the fan adjustment speed.

[0049] Exemplarily, determine whether the operating state of the equipment is the start-up state; in this embodiment, the operating state of the gas blower is the start-up state, and extract the start-up operating range from the historical experimental database, the start-up operating range specifically including the start-up temperature range and the start-up pressure range, determine whether the real-time temperature in the equipment operating data meets the start-up temperature range, and determine whether the real-time pressure in the equipment operating data meets the start-up pressure range. In this embodiment, it is determined that the real-time temperature of the gas blower does not meet the start-up temperature range, and the real-time pressure does not meet the start-up pressure range. The equipment working state of the gas blower is marked as an abnormal start-up state, and it is determined whether the real-time temperature of the gas blower is greater than the start-up temperature range. It is determined that the real-time temperature of the gas blower is greater than the maximum value of the start-up temperature range, and the temperature state of the gas blower is marked as an overheated state; it is determined whether the real-time pressure of the gas blower is greater than the start-up pressure range. It is determined that the real-time pressure of the gas blower is less than the minimum value of the start-up pressure range, and the pressure state of the gas blower is marked as a low pressure state.

[0050] In some other preferred embodiments, if the operating state of the equipment is a shutdown state, the shutdown operating range is extracted from the historical experimental database, the real-time temperature in the equipment operating data is compared with the shutdown temperature range in the shutdown operating range to obtain the temperature state, and the real-time pressure in the equipment operating data is compared with the shutdown pressure range in the shutdown operating range to obtain the pressure state.

[0051] It should be noted that the normal temperature range and normal pressure range corresponding to the device in the on state and the shut down state are different. In the on state, the heat generated by the device due to operation will cause the temperature of the device to rise. The device will also generate gas during operation, which will increase the pressure inside the device. The temperature rise inside the device will also increase the pressure inside the device. When the device is in the shut down state, it may also need to dissipate heat due to the high internal temperature. The device states corresponding to the on state and the shut down state are distinguished. When the device is running, the fan speed can be precisely controlled to ensure that the device runs in an efficient state to avoid energy waste. In the shut down state, the wind speed can be adjusted appropriately to maintain basic environmental conditions, such as temperature and humidity, to prevent the equipment from being damaged by environmental changes.

[0052] In this embodiment, the real-time temperature inside the gas blower is 200 degrees, the real-time pressure is 10 kPa, the collected external temperature outside the gas blower is 25 degrees, the air quality is good, and the atmospheric pressure is 100 kPa; in this embodiment, the startup temperature range is 100 degrees-150 degrees, and the startup pressure range is 120 kPa-180 kPa; the difference between the real-time temperature of the equipment and the startup temperature range is calculated to obtain a device temperature difference of 50 degrees, and the difference between the real-time pressure of the equipment and the startup pressure range is calculated to obtain a device pressure difference of 20 kPa. The current fan speed is extracted from the management system database and is 1000 revolutions per minute.

[0053] Extract a large amount of historical experimental data from the historical experimental database, mark the historical experimental data group in which only temperature changes as temperature change experimental data, mark the historical experimental data group in which only pressure changes as pressure change experimental data, and mark the historical experimental data in which only air quality changes as air quality change experimental data; and mark the corresponding changed temperature, pressure and air quality as change data;

[0054] The change category corresponding to the temperature change experimental data is marked as temperature change, the change category corresponding to the pressure change experimental data is marked as pressure change, and the change category corresponding to the air quality change experimental data is marked as air quality change; an initial neural network model is constructed, and the temperature change experimental data, the pressure change experimental data, the air quality change experimental data and the corresponding fan speed are used as training data and test data. The neural network model is trained using the training data, and the trained neural network model is tested using the test data. According to the test results, the parameters of the neural network model are adjusted to obtain an influence coefficient model with the input data being the change data and the change category and the output data being the corresponding influence coefficient.

[0055] The equipment temperature difference is 50 degrees, and the change category is temperature change input into the influence coefficient model to obtain a temperature change influence coefficient of 1.2. The equipment pressure difference is 20 kPa, and the change category is pressure change input into the influence coefficient model to obtain a pressure change influence coefficient of 1.1. The air quality is good, and the change category is air quality change input into the influence coefficient model to obtain an air quality influence coefficient of 1.0. In this embodiment, the temperature reference difference is 1 degree, and the pressure reference difference is kPa. The fan demand speed XQV = 1160 revolutions per minute is calculated through the formula XQV = [1 + (SCT-WBT) × α1 / JZT] + [1 + (SCP-WBP) × α2 / JZP] × β × DQS.

[0056] Determine whether the equipment working state of the gas blower is the on state. In this embodiment, the equipment working state of the gas blower is the on state. In the on state, the gas blower will continue to generate heat and increase the temperature, so it is necessary to adjust the fan speed to reduce the impact of subsequent temperature increase on the heat dissipation and energy saving effect; set the heat dissipation coefficient, calculate the product of the real-time temperature and the heat dissipation coefficient and the product of the fan demand speed to obtain the fan adjustment speed in the on state; in the shutdown state, the fan speed demand of the equipment is relatively small, and a shutdown threshold is set. If the fan demand speed is less than the shutdown threshold, it means that the equipment has a small demand for the fan speed at this time, and heat dissipation can be completed by natural cooling, which can reduce energy consumption, reduce operating costs, and also reduce mechanical wear and extend mechanical life; the heat dissipation coefficient is set according to the professional's assessment of the degree of influence of temperature growth on the fan speed; the shutdown threshold is set according to the professional's assessment of the fan speed that can be regarded as having no impact on the normal shutdown of the equipment.

[0057] Effect detection module: used to detect the equipment working condition to obtain the adjustment working condition information, and obtain the adjustment effect based on the adjustment working condition information.

[0058] Exemplarily, the fan speed regulation is applied to the gas-fired fan equipment through the PLC control system, and the working state, regulation temperature state and regulation pressure state of the regulated equipment are collected again through the temperature sensor and the pressure sensor; it is determined whether the regulated operating state is the power-on state; it is determined that the regulated operating state is the power-on state, and it is further determined whether the regulation temperature state and the regulation pressure state are in accordance with the power-on temperature range and the power-on pressure range in the power-on operating range; if they are in accordance, it means that the equipment can operate normally after the adjustment, and the regulation effect is marked as effective regulation; if they are not in accordance, it means that the regulation effect has not yet reached the target effect, and the regulation effect is marked as invalid regulation.

[0059] In some other preferred embodiments, if the adjusted operating state is a shutdown state, the shutdown operating range is called to compare with the adjustment condition information to obtain the adjustment effect.

[0060] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0061] Working principle of the present invention:

[0062] The present invention collects equipment operating condition information and external environment information of the equipment through sensors, obtains the equipment state of the equipment based on the comparison result of the equipment operating condition information and the operating range in the database, obtains the temperature influence coefficient, the pressure influence coefficient and the air quality influence coefficient through the neural network model obtained by training the historical experimental data, obtains the fan demand speed by combining the equipment operating condition information and the external environment information, adjusts the fan demand speed according to the equipment state and the preset heat dissipation coefficient and the shutdown threshold to obtain the fan adjustment speed; applies the fan adjustment speed through the PLC control system, and re-collects the adjusted operating state of the adjustment equipment through the sensor, and compares it with the operating range to obtain the adjustment effect.

[0063] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A fan speed adjustment system based on PLC control technology, characterized in that: include: Data collection module, wind speed adjustment module and effect detection module; Data collection module: used to collect equipment operating information and external environment information; Wind speed adjustment module: used to obtain the equipment status of the equipment according to the equipment working condition information; The fan demand speed is obtained based on the equipment operating condition information and the external environment information, and the fan demand speed is adjusted according to the equipment status to obtain the fan adjustment speed; Effect detection module: used to detect the equipment working condition to obtain the adjustment working condition information, and obtain the adjustment effect based on the adjustment working condition information.

2. A fan speed adjustment system based on PLC control technology according to claim 1, characterized in that: The obtaining of the equipment status of the equipment according to the equipment operating condition information includes: A1: Extract equipment operating information; the equipment operating information includes the equipment's operating status and operating data; the operating status includes the start-up status and the shutdown status; the operating data includes the equipment's real-time temperature and real-time pressure; A2: Determine whether the device is in the power-on state; if yes, jump to A3; if no, jump to A4; A3: Extract the startup operation range from the historical experiment database; determine whether the operation data of the device meets the startup operation range; if yes, mark the device working state of the corresponding device as normal startup state; if no, mark the device working state of the corresponding device as abnormal startup state, and jump to A5; A4: Extract the shutdown operation range from the historical experiment database; determine whether the operation data of the equipment meets the shutdown operation range; if yes, mark the equipment working state of the corresponding equipment as normal shutdown state; if no, mark the equipment working state of the corresponding equipment as abnormal shutdown state, and jump to A5; A5: Integrate the startup operation range and the shutdown operation range and mark them as the equipment operation range; determine whether the real-time temperature is greater than the corresponding equipment operation range; if yes, mark the temperature state of the corresponding equipment as overheating; if no, mark the temperature state of the corresponding equipment as low temperature; A6: Determine whether the real-time pressure is greater than the corresponding device operating range; if yes, mark the pressure state of the corresponding device as overpressure; if no, mark the pressure state of the corresponding device as underpressure; A7: Integrate the equipment working status, temperature status and pressure status to obtain the equipment status.

3. The fan speed adjustment system based on PLC control technology according to claim 1 is characterized in that: The step of obtaining the required fan speed based on the equipment operating condition information and the external environment information includes: Extracting equipment operating condition information and external environment information; wherein the external environment information includes the external temperature, air quality and atmospheric pressure outside the equipment; The difference between the real-time temperature of the equipment and the corresponding equipment operating range is marked as the equipment temperature difference, and the difference between the real-time pressure of the equipment and the corresponding equipment operating range is marked as the equipment pressure difference; the current fan speed is obtained by extracting from the database; The fan demand speed XQV is calculated by the formula XQV=[1+(SCT-WBT)×α1 / JZT]+[1+(SCP-WBP)×α2 / JZP]×β×DQS; wherein SCT represents the equipment temperature difference, WBT represents the external temperature, α1 represents the temperature influence coefficient, SCP represents the equipment pressure difference, WBP represents the atmospheric pressure, α2 represents the pressure influence coefficient, DQS represents the current fan speed, JZT represents the temperature reference difference, JZP represents the pressure reference difference, β represents the air quality influence coefficient, and the temperature influence coefficient, pressure influence coefficient and air quality influence coefficient are set according to historical experimental data.

4. The fan speed adjustment system based on PLC control technology according to claim 3 is characterized in that: The temperature influence coefficient, pressure influence coefficient and air quality influence coefficient are set according to historical experimental data, including: Extract historical experimental data from the historical experiment database; Mark the historical experimental data group in which only temperature changes in the historical experimental data as temperature change experimental data, mark the historical experimental data group in which only pressure changes in the historical experimental data as pressure change experimental data, and mark the historical experimental data in which only air quality changes in the historical experimental data as air quality change experimental data; and mark the corresponding changed temperature, pressure and air quality as change data; The change category corresponding to the temperature change experimental data is marked as temperature change, the change category corresponding to the pressure change experimental data is marked as pressure change, and the change category corresponding to the air quality change experimental data is marked as air quality change; an initial neural network model is constructed, and the temperature change experimental data, the pressure change experimental data, the air quality change experimental data and the corresponding fan speed are used as training data and test data, the neural network model is trained using the training data, and the trained neural network model is tested using the test data, and the parameters of the neural network model are adjusted according to the test results to obtain an influence coefficient model in which the input data is the change data and the change category, and the output data is the corresponding influence coefficient; The equipment temperature difference and change category are input into the influence coefficient model as temperature change to obtain the temperature change influence coefficient. The equipment pressure difference and change category are input into the influence coefficient model as pressure change to obtain the pressure change influence coefficient. The air quality and change category are input into the influence coefficient model as air quality change to obtain the air quality influence coefficient.

5. The fan speed adjustment system based on PLC control technology according to claim 1 is characterized in that: The step of adjusting the fan demand speed according to the equipment state to obtain the fan adjustment speed includes: S1: Extract device status; S2: Determine whether the device is in the power-on state; if yes, jump to S3; if no, jump to S4; S3: Set the heat dissipation coefficient; calculate the product of the real-time temperature and the heat dissipation coefficient and the product of the required wind speed to obtain the fan adjustment speed; S4: Set the shutdown threshold; determine whether the fan demand speed is greater than the shutdown threshold; if yes, maintain the fan adjustment speed; if no, turn off the fan switch and mark the fan adjustment speed as zero.

6. The fan speed adjustment system based on PLC control technology according to claim 1 is characterized in that: The obtaining of the adjustment effect based on the adjustment working condition information includes: B1: extracting the adjustment working condition information; wherein the adjustment working condition information includes the working state of the adjustment device after adjustment, the adjustment temperature state and the adjustment pressure state; B2: Determine whether the adjusted running state is the power-on state; if yes, jump to B3; if no, jump to B4; B3: Determine whether the adjustment condition information meets the startup operation range; if yes, mark the adjustment effect as effective adjustment; if no, mark the adjustment effect as invalid adjustment; B4: Determine whether the adjustment condition information meets the shutdown operation range; if yes, mark the adjustment effect as effective adjustment; if no, mark the adjustment effect as invalid adjustment.

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