Multi-working-condition fan adaptive control method and system and storage medium

Abstract

The invention discloses a multi-working-condition fan adaptive control method, a multi-working-condition fan adaptive control system and a storage medium, relates to the field of automatic control, and solves the technical problem that in fan regulation, adaptive regulation adapting to environment change is difficult to carry out on a fan of a fan. According to the method, the effective wind control range of each fan in a factory is obtained through the digital twinborn model; acquiring target data in the effective wind control range; performing feature value extraction on the target data to obtain feature target data; emitting an alarm signal based on the feature target data; determining a fan regulation and control factor based on the feature target data, and performing adaptive regulation on the fan based on the fan regulation and control factor; according to the invention, the adaptability and control capability of the fan to the environment can be improved, and the invalid energy consumption among multiple fans can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automation control, and in particular to a multi-working-condition fan self-adaptive control method and system and a storage medium. BACKGROUND

[0002] Fans play an important role in many fields such as industry, construction, and agriculture, and are used for ventilation, air conditioning, gas delivery, etc. In the industry and energy fields, fans are widely used in ventilation, air conditioning, metallurgy, chemical industry, etc. However, in actual operation, fans often face changing working conditions such as environmental changes and equipment aging, making it difficult for traditional control methods to operate efficiently and stably, and there are prominent problems such as high energy consumption and low efficiency.

[0003] At present, most of the multi-working-condition fan self-adaptive control methods are difficult to adaptively control the fan of the fan according to the change of the environment in the fan regulation, and only the speed is set according to the fixed parameters or manually to select the wind speed, which will cause the invalid energy consumption of the fan in the low harmful gas concentration and the delay of the blowing effect of the harmful gas in the high harmful gas concentration.

[0004] Therefore, the present application discloses a multi-working-condition fan self-adaptive control method, system and storage medium to solve the above technical problems. SUMMARY

[0005] The present application aims to at least solve one of the technical problems existing in the prior art; for this purpose, the present application proposes a multi-working-condition fan self-adaptive control method, system and storage medium to solve the technical problem that it is difficult to adaptively control the fan of the fan according to the change of the environment in the fan regulation. The present application obtains the effective wind control range of each fan in the factory through a digital twin model, obtains target data within the effective wind control range, extracts feature target data from the target data, determines a fan regulation factor based on the feature target data, and adaptively adjusts the fan based on the fan regulation factor to solve the above problems.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a multi-working-condition fan self-adaptive control method, comprising:

[0007] obtaining the effective wind control range of each fan in the factory through a digital twin model;

[0008] obtaining target data within the effective wind control range; wherein the target data includes carbon monoxide concentration, nitrogen monoxide concentration, nitrogen dioxide concentration, and sulfur dioxide concentration;

[0009] The target data is subjected to eigenvalue extraction to obtain characteristic target data; wherein the characteristic target data includes characteristic carbon monoxide concentration, characteristic nitrogen monoxide concentration, characteristic nitrogen dioxide concentration and characteristic sulfur dioxide concentration;

[0010] An alarm signal is issued based on the characteristic target data;

[0011] A fan control factor is determined based on the characteristic target data, and the fan is adaptively adjusted based on the fan control factor.

[0012] Preferably, the effective fan control range of each fan in the factory obtained through the digital twin model comprises:

[0013] A three-dimensional model is constructed through a three-dimensional map of the factory obtained through a three-dimensional scanning device; the three-dimensional model and fan device information are extracted; wherein the three-dimensional scanning device includes a high-resolution camera or a three-dimensional scanner, and the fan device information includes the appearance characteristics and physical characteristics of each fan participating in the ventilation control of the factory;

[0014] A device model of each fan is constructed based on the fan device information, a simulation model is constructed based on the three-dimensional model, and the device model and the simulation model are combined to generate a digital twin model;

[0015] A standard rotating speed and fan operating data corresponding to the standard rotating speed are input into the digital twin model to obtain the effective fan control range of each fan; wherein the standard rotating speed is obtained by manual setting, and the effective fan control range is the effective working range of the corresponding fan under the standard rotating speed.

[0016] Preferably, the target data in the effective fan control range is obtained, comprising:

[0017] Within the effective fan control range of each fan, carbon monoxide concentration is obtained through a plurality of carbon monoxide sensors, nitrogen monoxide concentration is obtained through a plurality of nitrogen monoxide sensors, nitrogen dioxide concentration is obtained through a plurality of nitrogen dioxide sensors, and sulfur dioxide concentration is obtained through a plurality of sulfur dioxide sensors.

[0018] Preferably, the eigenvalue extraction of the target data to obtain the characteristic target data comprises:

[0019] The carbon monoxide concentration, the nitrogen monoxide concentration, the nitrogen dioxide concentration and the sulfur dioxide concentration are integrated into A1 concentration group, A2 concentration group, A3 concentration group and A4 concentration group according to types;

[0020] Extracting each concentration group in turn, obtaining the variance of the concentration group, judging whether the variance is greater than the concentration variance threshold value; when the variance is not greater than the concentration variance threshold value, the data of the current concentration group is retained; when the variance is greater than the concentration variance threshold value, the concentration with the largest absolute value of the difference from the concentration average value in the current concentration group is removed, and the variance is re-judged until the variance of the current concentration group is less than the corresponding concentration variance threshold value, and the remaining data of the current concentration group is retained; wherein the concentration variance threshold value is obtained by experience setting;

[0021] The maximum value and the average value of the data retained in each concentration group are subjected to weight calculation to obtain the characteristic value of each concentration group; the characteristic value corresponding to the A1 concentration group is marked as the characteristic carbon monoxide concentration, the characteristic value corresponding to the A2 concentration group is marked as the characteristic nitrogen monoxide concentration, the characteristic value corresponding to the A3 concentration group is marked as the characteristic nitrogen dioxide concentration, and the characteristic value corresponding to the A4 concentration group is marked as the characteristic sulfur dioxide concentration.

[0022] Preferably, the alarm signal is issued based on the characteristic target data, comprising:

[0023] Extracting each concentration in the characteristic target data, when the characteristic carbon monoxide concentration N1 in the characteristic target data exceeds the carbon monoxide alarm threshold value, issuing an alarm signal for carbon monoxide exceeding the standard;

[0024] When the characteristic nitrogen monoxide concentration N2 in the characteristic target data exceeds the nitrogen monoxide alarm threshold value, an alarm signal for nitrogen monoxide exceeding the standard is issued;

[0025] When the characteristic nitrogen dioxide concentration N3 in the characteristic target data exceeds the nitrogen dioxide alarm threshold value, an alarm signal for nitrogen dioxide exceeding the standard is issued;

[0026] When the characteristic sulfur dioxide concentration N4 in the characteristic target data exceeds the sulfur dioxide alarm threshold value, an alarm signal for sulfur dioxide exceeding the standard is issued; wherein the carbon monoxide alarm threshold value, the nitrogen monoxide alarm threshold value, the nitrogen dioxide alarm threshold value and the sulfur dioxide alarm threshold value are determined according to the standard value set by experts and the natural ventilation condition within the effective control range of the current fan.

[0027] Preferably, the fan control factor is determined based on the characteristic target data, comprising:

[0028] Extracting feature target data in the concentration of carbon monoxide N1, feature concentration of nitrogen monoxide N2, feature concentration of nitrogen dioxide N3 and feature concentration of sulfur dioxide N4, judging whether the concentration of carbon monoxide N1, feature concentration of nitrogen monoxide N2, feature concentration of nitrogen dioxide N3 and feature concentration of sulfur dioxide N4 exceed the corresponding concentration determination value; if yes, the value of fan regulating factor FZ is set to 1; otherwise, the fan regulating factor FZ is determined based on the feature concentration of carbon monoxide N1, feature concentration of nitrogen monoxide N2, feature concentration of nitrogen dioxide N3 and feature concentration of sulfur dioxide N4; wherein the concentration determination value is obtained by experience;

[0029] The fan regulating factor FZ satisfies the calculation formula (1):

[0030]

[0031] Wherein, α1, α2, α3 and α4 are proportional adjustment coefficients determined according to the number of times that each concentration exceeds the corresponding concentration determination value; Y1 is the concentration determination value corresponding to the feature concentration of carbon monoxide N1, Y2 is the concentration determination value corresponding to the feature concentration of nitrogen monoxide N2, Y3 is the concentration determination value corresponding to the feature concentration of nitrogen dioxide N3, and Y4 is the concentration determination value corresponding to the feature concentration of sulfur dioxide N4.

[0032] Preferably, the α1, α2, α3 and α4 are determined according to the number of times that each concentration exceeds the corresponding concentration determination value, comprising:

[0033] Extracting the number of times DC that each concentration exceeds the corresponding concentration determination value in the feature target data recorded in the current factory in the n days before the current time i , determining the proportional adjustment coefficient αi corresponding to each concentration in the feature target data based on the number of times DC i ; wherein, i is the number corresponding to each concentration in the feature target data; n is obtained by artificial setting, generally taking the value of 180 days;

[0034] The proportional adjustment coefficient αi satisfies the calculation formula (2):

[0035]

[0036] Wherein, BDC is a number limit value set according to experience, which is used to reduce the influence of excessive number of times exceeding the concentration determination value on other proportional adjustment coefficients.

[0037] Preferably, the fan is adaptively adjusted based on the fan regulating factor, comprising:

[0038] Extracting the fan regulating factor FZ, determining the fan speed FV corresponding to the current fan based on the calculation formula (3), the calculation formula (3) is:

[0039] FV=max(FZ*DZ,AZ) (3);

[0040] Wherein, DZ is the maximum speed of the fan corresponding to the fan of the wind machine, and AZ is the minimum speed set by the artificial for the fan corresponding to the fan of the wind machine.

[0041] The second aspect of the application provides a multi-condition wind machine adaptive control system, comprising: an intelligent analysis module, and a data collection module and an adaptive control module connected with the intelligent analysis module.

[0042] The data collection module is used to obtain the effective wind control range of each wind machine in the factory through a digital twin model, and obtain target data in the effective wind control range; wherein the target data includes carbon monoxide concentration, nitrogen monoxide concentration, nitrogen dioxide concentration and sulfur dioxide concentration.

[0043] The intelligent analysis module is used to extract feature values of the target data to obtain feature target data; wherein the feature target data includes feature carbon monoxide concentration, feature nitrogen monoxide concentration, feature nitrogen dioxide concentration and feature sulfur dioxide concentration.

[0044] The adaptive control module is used to issue an alarm signal based on the feature target data, determine a wind machine control factor based on the feature target data, and adaptively adjust the fan based on the wind machine control factor.

[0045] The third aspect of the application provides a storage medium, characterized by being used for storing a computer program, and the computer program is executed to realize a multi-condition wind machine adaptive control method.

[0046] Compared with the prior art, the beneficial effects of the application are:

[0047] 1. The application obtains the effective wind control range of each wind machine in the factory through a digital twin model, obtains target data in the effective wind control range, extracts feature values of the target data to obtain feature target data, determines a wind machine control factor based on the feature target data, and adaptively adjusts the fan based on the wind machine control factor, thereby solving the technical problem that it is difficult to adaptively control the fan of the wind machine in the wind machine control; the application can improve the adaptability and control ability of the wind machine to the environment, and reduce the invalid energy consumption between multiple wind machines.

[0048] 2. The application constructs a three-dimensional model of the factory through digital twin technology and combines fan equipment information to accurately plan the effective control range of each fan, thereby significantly improving the efficiency and accuracy of factory ventilation control. The application can identify the effective working area of the fan in advance, avoid the invalid energy consumption caused by multiple fans working simultaneously due to excessive concentration at a certain position, thereby optimizing energy utilization and reducing operating costs. Through simulation analysis of the digital twin model, the working efficiency of the fan at the standard speed can be more accurately evaluated, providing a scientific basis for adaptive control of the fan and improving system flexibility and response speed. In addition, the application constructs a high-precision digital twin model through three-dimensional scanning technology and extraction of fan equipment information, which can simulate the running state of the fan under different working conditions, providing data support for optimizing fan layout and parameter setting, thereby further improving the overall ventilation effect of the factory and the safety of the production environment. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, a brief introduction will be given below to the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0050] Figure 1 The operation steps of the present application are shown in the figure.

[0051] Figure 2 The operation steps of the present application are shown in the figure.

[0052] Figure 3 The system module of the present application is shown in the figure. DETAILED DESCRIPTION

[0053] The technical solutions of the present application will be described below in conjunction with the embodiments, obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] Please refer to Figure 1 The first aspect embodiment of the present application provides a multi-working-condition fan adaptive control method, comprising:

[0055] Obtain the effective control range of each fan in the factory through a digital twin model;

[0056] Obtaining target data in the effective air control range; wherein, the target data includes carbon monoxide concentration, nitrogen monoxide concentration, nitrogen dioxide concentration and sulfur dioxide concentration;

[0057] Performing eigenvalue extraction on the target data to obtain characteristic target data; wherein, the characteristic target data includes characteristic carbon monoxide concentration, characteristic nitrogen monoxide concentration, characteristic nitrogen dioxide concentration and characteristic sulfur dioxide concentration;

[0058] Issuing an alarm signal based on the characteristic target data;

[0059] Determining a fan control factor based on the characteristic target data, and adaptively adjusting the fan based on the fan control factor.

[0060] In the present application, the effective air control range of each fan in the factory is obtained through a digital twin model, including:

[0061] Obtaining a three-dimensional map of the factory through a three-dimensional scanning device, and constructing a three-dimensional model based on the three-dimensional map of the factory; extracting the three-dimensional model and fan equipment information; wherein, the three-dimensional scanning device includes a high-resolution camera or a three-dimensional scanner, and the fan equipment information includes the appearance characteristics and physical characteristics of each fan participating in the ventilation control of the factory;

[0062] Constructing an equipment model of each fan based on the fan equipment information, constructing a simulation model based on the three-dimensional model, and combining the equipment model and the simulation model to generate a digital twin model;

[0063] Inputting a standard rotating speed and fan operating data corresponding to the standard rotating speed into the digital twin model to obtain the effective air control range of each fan; wherein, the standard rotating speed is obtained by manual setting, and the effective air control range is the effective working range of the corresponding fan under the standard rotating speed.

[0064] Notably, the application constructs a three-dimensional model of the factory through digital twinning technology, and combines fan equipment information to accurately plan the effective control range of each fan, thereby significantly improving the efficiency and accuracy of factory ventilation regulation. The application can identify the effective working area of the fan in advance, avoid the invalid energy consumption caused by multiple fans working simultaneously due to excessive concentration at a certain position, thereby optimizing energy utilization and reducing operating costs. Through simulation analysis of the digital twinning model, the working efficiency of the fan at the standard speed can be more accurately evaluated, providing a scientific basis for adaptive control of the fan and improving system flexibility and response speed. In addition, the application constructs a high-precision digital twinning model through three-dimensional scanning technology and extraction of fan equipment information, which can simulate the running state of the fan under different working conditions, providing data support for optimizing fan layout and parameter setting, thereby further improving the overall ventilation effect of the factory and the safety of the production environment. In summary, the application not only improves the intelligent level of fan adaptive control, but also provides strong technical support for efficient operation and sustainable development of the factory.

[0065] It should be noted that the physical characteristics include engine parameters of the fan, energy consumption corresponding to the fan blade speed, etc.

[0066] It should be noted that when obtaining the effective working range, if there is a factory area that is not marked as an effective working range, such factory area is included in the effective working range of the nearest fan.

[0067] In the application, the target data in the effective control range of the fan is obtained, including:

[0068] In the effective control range of each fan, the carbon monoxide concentration is obtained through a plurality of carbon monoxide sensors, the nitrogen monoxide concentration is obtained through a plurality of nitrogen monoxide sensors, the nitrogen dioxide concentration is obtained through a plurality of nitrogen dioxide sensors, and the sulfur dioxide concentration is obtained through a plurality of sulfur dioxide sensors.

[0069] Please refer to Figure 2 In the application, the feature value of the target data is extracted to obtain the characteristic target data, including:

[0070] The carbon monoxide concentration, nitrogen monoxide concentration, nitrogen dioxide concentration and sulfur dioxide concentration are integrated according to the type as A1 concentration group, A2 concentration group, A3 concentration group and A4 concentration group;

[0071] Extract each concentration group in turn, obtain the variance of the concentration group, and judge whether the variance is greater than the concentration variance threshold; when the variance is not greater than the concentration variance threshold, the data of the current concentration group is retained; when the variance is greater than the concentration variance threshold, the concentration with the largest absolute value of the difference from the concentration average in the current concentration group is removed, and the variance is re-judged until the variance of the current concentration group is less than the corresponding concentration variance threshold, and the remaining data of the current concentration group is retained; wherein the concentration variance threshold is obtained by experience setting;

[0072] The maximum value and the average value of the data retained in each concentration group are subjected to weight calculation to obtain the characteristic value of each concentration group; the characteristic value corresponding to the A1 concentration group is marked as the characteristic carbon monoxide concentration, the characteristic value corresponding to the A2 concentration group is marked as the characteristic nitrogen monoxide concentration, the characteristic value corresponding to the A3 concentration group is marked as the characteristic nitrogen dioxide concentration, and the characteristic value corresponding to the A4 concentration group is marked as the characteristic sulfur dioxide concentration.

[0073] Notably, the present application extracts the characteristic values of the target data, integrates the carbon monoxide, nitrogen monoxide, nitrogen dioxide and sulfur dioxide concentrations into four concentration groups respectively, and removes the outliers through variance judgment, ensuring the stability and reliability of the data. By retaining the data that meets the variance requirement and calculating the weight of the maximum value and the average value, the representative characteristic values are extracted, thereby providing accurate data support for subsequent fan adaptive control. This method not only improves the efficiency of data processing, but also ensures the accuracy and reliability of the characteristic values, providing strong technical support for the intelligentization and refinement of factory ventilation regulation. At the same time, by setting the concentration variance threshold through experience, the data processing process is more flexible and applicable, which can adapt to the actual needs of different factories, further improving the practicality and scalability of the system.

[0074] It should be noted that in the weight calculation of the maximum value and the average value of the data retained in each concentration group to obtain the characteristic value of each concentration group, the weight coefficients of the maximum value and the average value are obtained by manual setting, for example, the weight coefficient of the maximum value is 0.7, and the weight coefficient of the average value is 0.3.

[0075] It should be noted that in the removal of the concentration with the largest absolute value of the difference from the concentration average in the current concentration group, the concentration average is the average value of the concentrations retained in the current variance judgment step.

[0076] It should be noted that in the removal of the concentration with the largest absolute value of the difference from the concentration average in the current concentration group, if the concentration with the largest absolute value of the difference from the concentration average in the current concentration group has a maximum concentration and a minimum concentration, the minimum concentration is removed first.

[0077] It should be noted that if the variance of the remaining concentration is still not less than the concentration variance threshold after removing 90% of the concentration data, the average value of the original concentration of the current concentration group is used as the characteristic value.

[0078] In the present application, an alarm signal is issued based on the characteristic target data, including:

[0079] Extract each concentration in the characteristic target data, and when the characteristic carbon monoxide concentration N1 in the characteristic target data exceeds the carbon monoxide alarm threshold, an alarm signal is issued for carbon monoxide exceeding the standard;

[0080] When the characteristic nitrogen monoxide concentration N2 in the characteristic target data exceeds the nitrogen monoxide alarm threshold, an alarm signal is issued for nitrogen monoxide exceeding the standard;

[0081] When the characteristic nitrogen dioxide concentration N3 in the characteristic target data exceeds the nitrogen dioxide alarm threshold, an alarm signal is issued for nitrogen dioxide exceeding the standard;

[0082] When the characteristic sulfur dioxide concentration N4 in the characteristic target data exceeds the sulfur dioxide alarm threshold, an alarm signal is issued for sulfur dioxide exceeding the standard; wherein the carbon monoxide alarm threshold, the nitrogen monoxide alarm threshold, the nitrogen dioxide alarm threshold and the sulfur dioxide alarm threshold are determined according to the standard value set by experts and the natural ventilation condition within the effective control range of the current fan.

[0083] It should be noted that the natural ventilation condition within the effective control range of the current fan is the natural wind speed data within the effective control range of the current fan without fan ventilation, and each alarm threshold is inversely proportional to the natural wind speed data value, because the more gas dispersed by the natural wind speed data, the lower the concentration monitored, and at this time the gas leakage condition is more serious than the monitored condition, so the alarm threshold needs to be lowered to adapt to the influence of natural wind speed on gas concentration.

[0084] In the present application, a fan control factor is determined based on the characteristic target data, including:

[0085] Extract the characteristic carbon monoxide concentration N1, the characteristic nitrogen monoxide concentration N2, the characteristic nitrogen dioxide concentration N3 and the characteristic sulfur dioxide concentration N4 in the characteristic target data, and determine whether there is a concentration exceeding the corresponding concentration determination value in the characteristic carbon monoxide concentration N1, the characteristic nitrogen monoxide concentration N2, the characteristic nitrogen dioxide concentration N3 and the characteristic sulfur dioxide concentration N4; Yes, set the value of the fan control factor FZ to 1; No, determine the fan control factor FZ based on the characteristic carbon monoxide concentration N1, the characteristic nitrogen monoxide concentration N2, the characteristic nitrogen dioxide concentration N3 and the characteristic sulfur dioxide concentration N4; wherein the concentration determination value is obtained by experience;

[0086] The fan regulation factor FZ satisfies the calculation formula (1):

[0087]

[0088] wherein a1, a2, a3 and a4 are proportional adjustment coefficients determined according to the number of times each concentration exceeds the corresponding concentration determination value; Y1 is the concentration determination value corresponding to the characteristic carbon monoxide concentration N1, Y2 is the concentration determination value corresponding to the characteristic nitrogen monoxide concentration N2, Y3 is the concentration determination value corresponding to the characteristic nitrogen dioxide concentration N3, and Y4 is the concentration determination value corresponding to the characteristic sulfur dioxide concentration N4.

[0089] Notably, the method uses empirically obtained concentration determination values, combined with data and experience in actual application, and has strong practicality and operability. By judging whether the characteristic gas concentration exceeds the determination value, it can quickly and intuitively determine whether the fan regulation measure needs to be started. When any characteristic gas concentration exceeds the determination value, the fan regulation factor FZ is directly set to 1, ensuring that it can respond quickly in an emergency and avoiding potential safety risks. In the case where the determination value is not exceeded, the relative deviations of multiple gas concentrations are considered comprehensively through calculation formula (1), which can more finely adjust the operating parameters of the fan and achieve precise control of environmental quality.

[0090] In addition, proportional adjustment coefficients a1, a2, a3 and a4 are introduced in calculation formula (1), which are determined according to the number of times each concentration exceeds the corresponding concentration determination value, reflecting the differentiated treatment of different gas concentration exceedances. This design makes the calculation of the fan regulation factor FZ more flexible and scientific, and can dynamically adjust the adjustment coefficients according to the historical data of each gas concentration exceeding in actual operation, thereby optimizing the regulation effect of the fan. At the same time, FZ is calculated in the form of an exponential function, making the change of the regulation factor more smooth and continuous, avoiding the system fluctuations that may be caused by sudden regulation, and improving the stability and reliability of the system.

[0091] The implementation of the method can effectively improve the intelligent level of fan regulation, reduce the need for human intervention, reduce operating costs, and improve the efficiency of environmental quality monitoring and control. By comprehensively considering the dynamic changes of multiple gas concentrations, the method not only can timely discover and respond to environmental quality problems, but also can optimize the fan operating parameters to achieve the goal of energy saving and emission reduction.

[0092] It should be noted that, in judging whether the concentrations of the characteristic carbon monoxide concentration N1, the characteristic nitrogen monoxide concentration N2, the characteristic nitrogen dioxide concentration N3 and the characteristic sulfur dioxide concentration N4 exceed the corresponding concentration determination values, the judgment is made in the order of N1, N2, N3 and N4. If any of the concentrations exceeds the corresponding concentration determination value, the fan regulation factor FZ is immediately set to 1, and further calculation is stopped.

[0093] In the present application, a1, a2, a3 and a4 are determined according to the number of times each concentration exceeds the corresponding concentration determination value, including:

[0094] Extract the number of times each concentration exceeds the corresponding concentration determination value DC in the characteristic target data recorded in the current factory in the n days before the current time i Determine the proportional adjustment coefficient ai corresponding to each concentration in the characteristic target data based on the number of times DC i ; wherein i is the number corresponding to each concentration in the characteristic target data; n is obtained by artificial setting, generally taking the value of 180 days;

[0095] The proportional adjustment coefficient ai satisfies the calculation formula (2):

[0096]

[0097] Wherein, BDC is a number limit value set according to experience, which is used to reduce the influence of excessive number of times exceeding the concentration determination value on other proportional adjustment coefficients.

[0098] It is worth noting that, by extracting the number of times each concentration exceeds the corresponding concentration determination value in the characteristic target data recorded in the current factory in the n days before the current time, and determining the proportional adjustment coefficient ai based on these numbers, the present application can dynamically reflect the historical rules and trends of different pollutant concentration exceedances. This method fully considers the statistical characteristics of historical data, making the determination of proportional adjustment coefficient ai more scientific and reasonable, and avoiding the regulation bias caused by fixed coefficients. By introducing the number limit value BDC, the influence of the number of times each pollutant concentration exceeds the concentration determination value on the proportional adjustment coefficient can be effectively balanced, preventing excessive number of times of a certain pollutant concentration exceeding the concentration determination value from excessively suppressing the proportional adjustment coefficients of other pollutants, so as to ensure that the weight distribution of each pollutant in the fan regulation is more fair and reasonable;

[0099] More importantly, by calculating the proportional adjustment coefficient ai according to formula (2), the relative contribution of the number of times each pollutant concentration exceeds the concentration determination value can be quantified as a specific weight value, so that in the subsequent calculation of the fan regulation factor, the differentiation of different pollutant concentration exceedances can be realized. This method not only improves the intelligent level of fan regulation, but also dynamically adjusts the operating parameters of the fan according to the changes of the actual environmental conditions, so as to realize the precise control of environmental quality;

[0100] In summary, the present application can effectively balance the influence of the concentration of each pollutant exceeding the standard on the fan regulation through historical data statistics and dynamic weight distribution, ensuring the scientificity and accuracy of fan regulation, and improving the intelligent level of the system and the efficiency of environmental quality control. This method not only adapts to the actual environmental conditions of different factories, but also dynamically adjusts the control strategy according to the changes in historical data, thereby achieving comprehensive, accurate and efficient control of environmental quality.

[0101] It should be noted that the number of times DC i Specifically: the number of times DC1 that the characteristic carbon monoxide concentration exceeds the concentration determination value Y1, the number of times DC2 that the characteristic nitrogen dioxide concentration N2 exceeds the concentration determination value Y2, the number of times DC3 that the characteristic nitrogen dioxide concentration N3 exceeds the concentration determination value Y3, and the number of times DC4 that the characteristic sulfur dioxide concentration N4 exceeds the concentration determination value Y4.

[0102] In the present application, the fan is adaptively adjusted based on the fan regulation factor, including:

[0103] The fan regulation factor FZ is extracted, and the fan speed FV corresponding to the current fan is determined based on calculation formula (3), and the calculation formula (3) is:

[0104] FV = max (FZ·DZ, AZ) (3);

[0105] Wherein, DZ is the maximum speed of the fan corresponding to the fan, and AZ is the minimum speed set by the artificial fan corresponding to the fan.

[0106] Please refer to Figure 3 The second aspect of the present application provides a multi-working-condition fan adaptive control system, comprising: an intelligent analysis module, and a data collection module and an adaptive control module connected to the intelligent analysis module;

[0107] The data collection module is used to obtain the effective wind control range of each fan in the factory through the digital twin model, and to obtain the target data in the effective wind control range; wherein the target data includes carbon monoxide concentration, nitrogen dioxide concentration, nitrogen dioxide concentration and sulfur dioxide concentration

[0108] The intelligent analysis module is used to extract the characteristic values of the target data to obtain the characteristic target data; wherein the characteristic target data includes characteristic carbon monoxide concentration, characteristic nitrogen dioxide concentration, characteristic nitrogen dioxide concentration and characteristic sulfur dioxide concentration;

[0109] The adaptive control module is used to issue an alarm signal based on the characteristic target data, determine the fan regulation factor based on the characteristic target data, and adaptively adjust the fan based on the fan regulation factor.

[0110] The third aspect of the present application provides a storage medium characterized by being used for storing a computer program, the computer program being executed to implement a multi-working-condition fan adaptive control method.

[0111] Part of the data in the above formula is calculated by removing the dimension, and the formula is obtained by software simulation of a large amount of collected data to obtain a formula closest to the real situation; the preset parameters and the preset threshold in the formula are set by the person skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0112] The working principle of the present application is as follows:

[0113] The present application first obtains the effective control range of each fan in the factory through the digital twin model, which can divide the working range of the fan and reduce the situation of one place exceeding the standard and multiple fans running; then the target data in the effective control range is obtained, and the characteristic target data is obtained by extracting the eigenvalue of the target data; this step standardizes the analysis of the target data, and provides accurate data for the subsequent fan regulation; finally, an alarm signal is issued based on the characteristic target data, a fan regulation factor is determined based on the characteristic target data, and the fan is adaptively adjusted based on the fan regulation factor; this step analyzes the speed of the fan through the adaptation of the environment, so that the speed of the fan can match the needs of the environment, and the automatic control level is improved.

[0114] The above embodiments are only used to illustrate the technical method of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A multi-condition fan adaptive control method, characterized in that, The method comprises the following steps: obtaining the effective air control range of each fan in the factory through a digital twin model; obtaining target data in the effective air control range; wherein the target data comprises carbon monoxide concentration, nitric oxide concentration, nitrogen dioxide concentration and sulfur dioxide concentration; extracting feature values from the target data to obtain feature target data; wherein the feature target data comprises feature carbon monoxide concentration, feature nitric oxide concentration, feature nitrogen dioxide concentration and feature sulfur dioxide concentration; issuing an alarm signal based on the feature target data; determining a fan control factor based on the feature target data, and adaptively adjusting the fan based on the fan control factor.

2. The adaptive control method of a multi-condition fan according to claim 1, characterized in that, The method of obtaining the effective air control range of each fan in the factory through a digital twin model comprises the following steps: obtaining a three-dimensional map of the factory through a three-dimensional scanning device, and constructing a three-dimensional model based on the three-dimensional map of the factory; extracting three-dimensional model and fan equipment information; wherein the three-dimensional scanning device comprises a high-resolution camera or a three-dimensional scanner, and the fan equipment information comprises the appearance characteristics and physical characteristics of each fan participating in the factory ventilation control; constructing a device model of each fan based on the fan equipment information, constructing a simulation model based on the three-dimensional model, and combining the device model and the simulation model to generate a digital twin model; inputting a standard rotating speed and fan operating data corresponding to the standard rotating speed into the digital twin model to obtain the effective air control range of each fan; wherein the effective air control range is the effective working range of the corresponding fan under the standard rotating speed.

3. The adaptive control method of a multi-condition fan according to claim 1, characterized in that, The method of obtaining target data in the effective air control range comprises the following steps: within the effective air control range of each fan, obtaining carbon monoxide concentration through a plurality of carbon monoxide sensors, obtaining nitric oxide concentration through a plurality of nitric oxide sensors, obtaining nitrogen dioxide concentration through a plurality of nitrogen dioxide sensors, and obtaining sulfur dioxide concentration through a plurality of sulfur dioxide sensors.

4. The adaptive control method of a multi-condition fan according to claim 1, characterized in that, The method of extracting feature values from the target data to obtain feature target data comprises the following steps: integrating carbon monoxide concentration, nitric oxide concentration, nitrogen dioxide concentration and sulfur dioxide concentration into A1 concentration group, A2 concentration group, A3 concentration group and A4 concentration group according to types; extracting each concentration group in turn, obtaining the variance of the concentration group, and determining whether the variance is greater than the concentration variance threshold; when the variance is not greater than the concentration variance threshold, the data of the current concentration group is retained; when the variance is greater than the concentration variance threshold, the concentration with the maximum absolute value difference from the concentration average value in the current concentration group is removed, and the variance is re-determined until the variance of the current concentration group is less than the corresponding concentration variance threshold, and the remaining data of the current concentration group is retained; performing weight calculation on the maximum value and the average value of the retained data of each concentration group to obtain the feature values of each concentration group; the feature values corresponding to the A1 concentration group are marked as feature carbon monoxide concentration, the feature values corresponding to the A2 concentration group are marked as feature nitric oxide concentration, the feature values corresponding to the A3 concentration group are marked as feature nitrogen dioxide concentration, and the feature values corresponding to the A4 concentration group are marked as feature sulfur dioxide concentration.

5. The adaptive control method of a multi-condition fan according to claim 1, characterized in that, The method of issuing an alarm signal based on the feature target data comprises the following steps: extracting each concentration in the characteristic target data, when the characteristic carbon monoxide concentration N1 in the characteristic target data exceeds a carbon monoxide alarm threshold, issuing an alarm signal of carbon monoxide exceeding the standard; when the characteristic nitrogen monoxide concentration N2 in the characteristic target data exceeds a nitrogen monoxide alarm threshold, issuing an alarm signal of nitrogen monoxide exceeding the standard; when the characteristic nitrogen dioxide concentration N3 in the characteristic target data exceeds a nitrogen dioxide alarm threshold, issuing an alarm signal of nitrogen dioxide exceeding the standard; when the characteristic sulfur dioxide concentration N4 in the characteristic target data exceeds a sulfur dioxide alarm threshold, issuing an alarm signal of sulfur dioxide exceeding the standard; wherein the carbon monoxide alarm threshold, the nitrogen monoxide alarm threshold, the nitrogen dioxide alarm threshold and the sulfur dioxide alarm threshold are determined according to a standard value set by an expert and the natural ventilation condition within the effective wind control range of the current fan.

6. The adaptive control method of a multi-condition fan according to claim 1, wherein, The fan regulation factor is determined based on the characteristic target data, comprising: extracting the characteristic carbon monoxide concentration N1, the characteristic nitrogen monoxide concentration N2, the characteristic nitrogen dioxide concentration N3 and the characteristic sulfur dioxide concentration N4 in the characteristic target data, and determining whether there is a concentration exceeding the corresponding concentration determination value in the characteristic carbon monoxide concentration N1, the characteristic nitrogen monoxide concentration N2, the characteristic nitrogen dioxide concentration N3 and the characteristic sulfur dioxide concentration N4; if yes, setting the value of the fan regulation factor FZ to 1; otherwise, determining the fan regulation factor FZ based on the characteristic carbon monoxide concentration N1, the characteristic nitrogen monoxide concentration N2, the characteristic nitrogen dioxide concentration N3 and the characteristic sulfur dioxide concentration N4; The fan regulation factor FZ satisfies the calculation formula (1): wherein α1, α2, α3 and α4 are proportional adjustment coefficients determined according to the number of times that each concentration exceeds the corresponding concentration determination value; Y1 is the concentration determination value corresponding to the characteristic carbon monoxide concentration N1, Y2 is the concentration determination value corresponding to the characteristic nitrogen monoxide concentration N2, Y3 is the concentration determination value corresponding to the characteristic nitrogen dioxide concentration N3, and Y4 is the concentration determination value corresponding to the characteristic sulfur dioxide concentration N4.

7. The adaptive control method of a multi-condition fan according to claim 6, wherein, The α1, α2, α3 and α4 are determined according to the number of times that each concentration exceeds the corresponding concentration determination value, comprising: Extract the number of times (DC) each concentration exceeds its corresponding concentration threshold from the feature target data recorded by the current factory in the previous n days. i Based on the number of DC i Determine the proportional adjustment coefficient αi corresponding to each concentration in the feature target data; where i is the number corresponding to each concentration in the feature target data; The proportional adjustment coefficient αi satisfies the calculation formula (2): wherein BDC is the number limit value.

8. The adaptive control method of a multi-condition fan according to claim 6, wherein, The fan is adaptively adjusted based on the fan regulation factor, comprising: extracting the fan regulation factor FZ, and determining the fan speed FV corresponding to the current fan based on the calculation formula (3): FV = max (FZ·DZ, AZ) (3); wherein DZ is the maximum speed of the fan corresponding fan, and AZ is the minimum speed of the fan corresponding fan.

9. A multi-condition fan adaptive control system for operating a multi-condition fan adaptive control method according to any one of claims 1 to 8, characterized in that, Comprising: an intelligent analysis module, and a data collection module and an adaptive control module connected with the intelligent analysis module; The data collection module is used to obtain the effective wind control range of each fan in the factory through a digital twin model, and acquire target data within the effective wind control range; wherein the target data includes carbon monoxide concentration, nitrogen monoxide concentration, nitrogen dioxide concentration and sulfur dioxide concentration The intelligent analysis module is configured to extract feature values of the target data to obtain feature target data, wherein the feature target data comprises feature carbon monoxide concentration, feature nitrogen monoxide concentration, feature nitrogen dioxide concentration and feature sulfur dioxide concentration. The adaptive control module is configured to issue an alarm signal based on the feature target data, determine a fan regulation factor based on the feature target data, and adaptively regulate the fan based on the fan regulation factor.

10. A storage medium, characterized by A computer program is stored, and the computer program is executed to implement the adaptive control method for the multi-working-condition fan according to any one of claims 1 to 8.

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