PLC-based remote frequency conversion control system for frequency conversion cabinets

Abstract

The present invention discloses a PLC-based remote frequency conversion control system for a frequency conversion cabinet, which relates to the technical field of remote frequency conversion control of frequency conversion cabinets, and includes a daily data monitoring module, a fan operation analysis module, a fan operation prediction module and a fan frequency control module; the daily data monitoring module is used to monitor daily data during fan operation; the fan operation analysis module is used to analyze the operation law of the fan based on daily data; the fan operation prediction module is used to predict the fan frequency of the fan based on daily data and the operation law; the fan frequency control module is used to regulate the fan frequency based on the predicted fan frequency; the present invention is used to solve the problem that the existing remote frequency conversion control technology of the frequency conversion cabinet is not accurate and reasonable enough when used for frequency conversion control of the fan, which leads to waste of power resources and poor indoor air environment.

Description

Technical Field

[0001] The present invention relates to the technical field of remote frequency conversion control of frequency conversion cabinets, in particular to a PLC-based remote frequency conversion control system for frequency conversion cabinets. Background Art

[0002] Remote frequency conversion control technology for frequency conversion cabinets refers to the use of remote communication technology to remotely monitor, operate and control the frequency converter in the frequency conversion cabinet through the network or other communication means. Through this technology, users can remotely monitor the operating status, parameters and fault information of the frequency converter in real time, and realize remote power on and off, speed regulation and parameter setting of the frequency converter, thereby realizing remote control and management of the equipment. This technology has important application significance in the field of industrial automation and intelligent control, and can improve production efficiency and reduce operating costs. It also facilitates remote maintenance and management of equipment.

[0003] When the existing frequency conversion cabinet remote frequency conversion control technology is used to control the fan to purify the indoor air environment, multiple gears are usually set to control the fan, or manual adjustment is performed, which cannot achieve precise adjustment. The frequency of the fan is usually increased after the indoor air environment is found to be poor. It is difficult to predict the changes in the indoor air environment and thus perform frequency conversion control of the fan in advance to keep the indoor air environment within a good range. For example, in the Chinese patent application publication number: CN115388500A, an intelligent control purification fan and its operation method are disclosed. The air volume of each room in this solution is a fixed value set according to its area, and there is no adjustment based on the indoor air environment. If the fan operates at a higher frequency, it will waste electricity resources. If it operates at a lower frequency, the indoor air environment will be poor, and the indoor air environment will be affected by the outdoor air environment at the same time. If the outdoor air environment is poor, it will be brought into the room during ventilation, which will further affect the indoor air environment. Therefore, it is necessary to accurately control the frequency of the fan to ensure a good indoor air environment while saving electricity resources. The existing frequency conversion cabinet remote frequency conversion control technology is not accurate and reasonable when used for frequency conversion control of the fan, which leads to the problem of wasting electricity resources and poor indoor air environment. 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 by monitoring the daily data of the fan during operation, analyzing the correlation between the factory air quality and the weather air quality, analyzing the correlation between the data time period and the factory air quality, and analyzing the correlation between the fan frequency required for the factory air quality, and then predicting the factory air quality, and then predicting the fan frequency, and finally regulating the fan frequency based on the predicted fan frequency, so as to solve the problem that the existing frequency conversion cabinet remote frequency conversion control technology is not accurate and reasonable enough when used for frequency conversion control of the fan, resulting in waste of electricity resources and poor indoor air environment.

[0005] To achieve the above-mentioned purpose, the present application provides a PLC-based frequency conversion cabinet remote frequency conversion control system, comprising a daily data monitoring module, a fan operation analysis module, a fan operation prediction module, and a fan frequency control module; the daily data monitoring module, the fan operation analysis module, and the fan frequency control module are respectively connected to the fan operation prediction module data;

[0006] The daily data monitoring module is used to monitor the daily data of the fan during operation;

[0007] The fan operation analysis module is used to analyze the operation rules of the fan based on daily data;

[0008] The fan operation prediction module is used to predict the fan frequency of the fan based on daily data and operation rules;

[0009] The fan frequency control module is used to regulate the fan frequency based on the predicted fan frequency.

[0010] Furthermore, the daily data monitoring module is configured with a daily data monitoring strategy, and the daily data monitoring strategy includes:

[0011] The daily data includes factory air quality, fan frequency, weather air quality and data period;

[0012] The plant air quality, fan frequency, weather air quality and data period constitute a daily data item. The data period represents that the daily data does not change within the time range of the data period. If any one of the plant air quality, fan frequency and weather air quality in the daily data changes, a new daily data item is generated.

[0013] The daily data is stored in a daily database.

[0014] Furthermore, the fan operation analysis module includes an air quality correlation unit, a time period quality correlation unit, and a frequency quality correlation unit;

[0015] The air quality correlation unit is used to analyze the correlation between the air quality of the factory building and the weather air quality;

[0016] The time period quality correlation unit is used to analyze the correlation between the data time period and the air quality of the factory building;

[0017] The frequency-quality correlation unit is used to analyze the correlation between fan frequencies required for factory air quality.

[0018] Furthermore, the air quality association unit is configured with an air quality association strategy, and the air quality association strategy includes:

[0019] Number the daily data of each day and use the symbol P n Indicates that n is a positive integer and n is the serial number of P, and then P is calculated according to the data period. n The daily data in the data are numbered and represented by the symbol Q(n,m), where m is a positive integer and (n,m) is the serial number of Q, Q(n,m) represents P n The mth daily data;

[0020] Take Q(1,m) in P1 as the reference benchmark, marked as E(1,i), the value range of i is the same as that of m, and each value of i in E(1,i) represents a time period grouping. There are max(i) time period groups in total, which are represented by the symbol T i Indicates that max() is the maximum value operator;

[0021] Starting with n=2 and m=1, analyze the intersection of the data periods of Q(n,m) and E(1,i), mark it as the period intersection, analyze the period intersection of Q(n,m) and E(1,i) for each value of i, find the maximum value, mark it as the maximum intersection, and include Q(n,m) in the period group T of E(1,i) corresponding to the maximum intersection i In the process, m+1 is added and the analysis is repeated. If the maximum value of m is reached, n+1 is added and m is reset to 1, and the analysis is repeated until the maximum values of n and m are reached at the same time.

[0022] For any T i , for T i Sort Q(n,m) in the order of weather air quality from small to large, and number them by symbol R(i,j), where j is a positive integer and (i,j) is the serial number of R, R(i,j) represents T i The jth Q(n,m) in

[0023] For any value of i, starting with j=2, calculate the difference in plant air quality between R(i,j) and R(i,1), marked as plant quality difference, and use symbol Ah Indicates that the difference in weather air quality between R(i,j) and R(i,1) is calculated at the same time, marked as weather quality difference, and represented by symbol B h Represents, where h is a positive integer and h is the serial number of A and B;

[0024] Each T i There exists a group A h and B h , with B h is the X-axis, A h Establish a plane rectangular coordinate system for the Y axis, named air quality correlation diagram, and divide each A h According to the corresponding B h Enter into the air quality correlation diagram;

[0025] Perform regression analysis on the air quality correlation diagram and select the regression function with the smallest discrete degree as the air quality correlation function.

[0026] Furthermore, the time period quality association unit is configured with a time period quality association strategy, and the time period quality association strategy includes:

[0027] For each piece of daily data, obtain the weather air quality of the daily data, substitute it into the air quality correlation function, mark the plant quality difference obtained by the solution as the environment elimination difference, reduce the plant air quality of the daily data by the environment elimination difference, and obtain the plant corrected air quality;

[0028] A rectangular coordinate system is established with time as the X-axis and the plant calibrated air quality as the Y-axis, named the period quality association diagram. The plant calibrated air quality of daily data is entered into the period quality association diagram according to the corresponding data period. Each daily data exists in the period quality association diagram in the form of a straight line, that is, within the time period of the data period, there are countless points in the plant calibrated air quality, and these countless points form a straight line, named the period quality line segment;

[0029] Get the length of the time period quality line segment, marked as the line segment length. If the line segment length is 1, generate a time period quality coordinate point at the midpoint of the time period quality line segment. If the line segment length is 2, generate a time period quality coordinate point at each of the two endpoints of the time period quality line segment. If the line segment length is greater than 2, calculate the line segment length minus 1 to obtain the equal fraction. Generate a time period quality coordinate point at each of the two endpoints of the time period quality line segment, and generate a time period quality coordinate point at each of the equal division points of the time period quality line segment.

[0030] The time period quality line segments are eliminated and only the time period quality coordinate points are retained. Regression analysis is performed on the time period quality coordinate points, and the regression function with the smallest discrete degree is selected as the time period quality correlation function.

[0031] Furthermore, the frequency quality association unit is configured with a frequency quality association strategy, and the frequency quality association strategy includes:

[0032] A rectangular coordinate system is established with the plant air quality as the horizontal axis and the fan frequency as the vertical axis. This is named the frequency-quality correlation diagram. The fan frequency in the daily data is entered into the frequency-quality correlation diagram according to the corresponding plant air quality.

[0033] Perform regression analysis on the frequency-quality correlation diagram and select the regression function with the smallest discrete degree as the frequency-quality correlation function.

[0034] Furthermore, the fan operation prediction module includes an air quality prediction unit and a fan frequency prediction unit;

[0035] The air quality prediction unit is used to predict the air quality of the factory building;

[0036] The fan frequency prediction unit is used to predict the fan frequency.

[0037] Furthermore, the air quality prediction unit is configured with an air quality prediction strategy, and the air quality prediction strategy includes:

[0038] Take the first time period as a period, name it the prediction period, obtain the prediction period after the current time, and mark it as the period to be predicted;

[0039] Obtain the median value of the time period to be predicted, mark it as the median value to be predicted, substitute the median value to be predicted into the time period quality correlation function, and solve it to obtain the first air quality;

[0040] Obtain the weather air quality of the time period to be predicted, substitute it into the air quality correlation function to obtain the second air quality;

[0041] The first air quality is added to the second air quality to obtain the predicted air quality.

[0042] Furthermore, the fan frequency prediction unit is configured with a fan frequency prediction strategy, and the fan frequency prediction strategy includes:

[0043] Substitute the predicted air quality into the frequency-quality correlation function and solve it to obtain the predicted frequency;

[0044] The predicted frequency is sent to the fan frequency control module.

[0045] Furthermore, the fan frequency control module is configured with a fan frequency control strategy, and the fan frequency control strategy includes:

[0046] Receive predicted frequency;

[0047] The frequency converter cabinet is remotely controlled through the PLC programmable logic controller, and then the fan frequency is adjusted to the predicted frequency through the frequency converter cabinet.

[0048] Beneficial effects of the present invention: The present invention monitors the daily data of the fan during operation, and then analyzes the correlation between the factory air quality and the weather air quality, and then analyzes the correlation between the data time period and the factory air quality, and at the same time analyzes the correlation between the fan frequency required for the factory air quality. The advantage is that the equipment in the factory usually performs the same operation in the same time period, so the impact of the equipment on the factory air quality in the same time period is usually fixed, and the factory usually has a ventilation system, and the ventilation system will exchange the air in the factory with the outdoor air, so the air quality of the outdoor weather also has a certain impact on the factory air quality. By analyzing the correlation between them, a data basis is provided for the subsequent prediction of the factory air quality and the fan frequency, thereby improving the reliability and comprehensiveness of the remote frequency conversion control technology of the frequency conversion cabinet when used for frequency conversion control of the fan;

[0049] The present invention predicts the fan frequency based on the correlation between factory air quality and weather air quality, the correlation between data period and factory air quality, and the correlation between the fan frequencies required for factory air quality, and finally regulates the fan frequency based on the predicted fan frequency. The advantage of the present invention is that the factors affecting factory air quality are considered from multiple angles and predicted, and the fan frequency can be regulated in advance, so that the factory air quality is always within a good range, thereby improving the effectiveness and accuracy of the remote frequency conversion control technology of the frequency conversion cabinet when used for frequency conversion control of the fan. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a functional block diagram of the system of the present invention;

[0051] Figure 2 The air quality correlation diagram of the present invention;

[0052] Figure 3 It is the time period quality association diagram of the present invention;

[0053] Figure 4 A schematic diagram of a time period quality line segment of the present invention;

[0054] Figure 5 A schematic diagram of the time period quality coordinate points of the present invention;

[0055] Figure 6 This is a frequency-quality correlation diagram of the present invention. DETAILED DESCRIPTION

[0056] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0057] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0058] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0059] Example 1, please refer to Figure 1 As shown, the present application provides a PLC-based frequency conversion cabinet remote frequency conversion control system, including a daily data monitoring module, a fan operation analysis module, a fan operation prediction module and a fan frequency control module; the daily data monitoring module, the fan operation analysis module and the fan frequency control module are respectively connected to the fan operation prediction module data;

[0060] The daily data monitoring module is used to monitor the daily data of the fan during operation;

[0061] The daily data monitoring module is configured with daily data monitoring strategies, which include:

[0062] Daily data includes plant air quality, fan frequency, weather air quality, and data time period;

[0063] The plant air quality, fan frequency, weather air quality, and data period constitute a daily data item. The data period represents the time range within which the daily data does not change. If any of the plant air quality, fan frequency, and weather air quality in the daily data changes, a new daily data item is generated.

[0064] Daily data is stored in the daily database;

[0065] In actual applications, some daily data in the daily database are shown in Table 1 below:

[0066] Table 1 Some daily data in the daily database

[0067] date Factory air quality Weather and air quality Fan frequency (Hz) Data period 2024.4.16 78 68 45 [8:26,9:38] 2024.4.16 82 68 50 [9:39,11:09] 2024.4.17 83 72 50 [8:32,9:31]

[0068] Among them, the factory air quality and weather air quality are both calculated using the AQI air quality index. The weather air quality refers to the indicators given by the weather forecast. The factory air quality is evaluated using the indoor air quality evaluation method, which is not further elaborated in this embodiment. The data period is accurate to the minute, such as 8:26 means 8:26, and the fan frequency is the frequency of the fan motor.

[0069] The fan operation analysis module is used to analyze the operation rules of the fan based on daily data; the fan operation analysis module includes an air quality correlation unit, a time period quality correlation unit, and a frequency quality correlation unit;

[0070] The air quality correlation unit is used to analyze the correlation between factory air quality and weather air quality;

[0071] The air quality association unit is configured with an air quality association strategy, which includes:

[0072] Number the daily data of each day and use the symbol P n Indicates that n is a positive integer and n is the serial number of P, and then P is calculated according to the data period. n The daily data in the data are numbered and represented by the symbol Q(n,m), where m is a positive integer and (n,m) is the serial number of Q, Q(n,m) represents P n The mth daily data;

[0073] In practical applications, taking Table 1 as an example, the daily data of April 16, 2024 and April 17, 2024 are numbered P1 and P2, where P1 is the daily data of April 16, 2024, and P2 is the daily data of April 17, 2024. From Table 1, we can see that there are two daily data of April 16, 2024, namely Q(1,1) and Q(1,2). Similarly, the data shown in Table 1 is partial data. In fact, there are 86 days of daily data in the numbering process, that is, 1≤n≤86. The number of daily data contained in different dates is different. Therefore, for each value of n, the value range of m changes.

[0074] Take Q(1,m) in P1 as the reference benchmark, marked as E(1,i), the value range of i is the same as that of m, and each value of i in E(1,i) represents a time period grouping. There are max(i) time period groups in total, which are represented by the symbol T i Indicates that max() is the maximum value operator;

[0075] Starting with n=2 and m=1, analyze the intersection of the data periods of Q(n,m) and E(1,i), mark it as the period intersection, analyze the period intersection of Q(n,m) and E(1,i) for each value of i, find the maximum value, mark it as the maximum intersection, and include Q(n,m) in the period group T of E(1,i) corresponding to the maximum intersection i In the process, m+1 is added and the analysis is repeated. If the maximum value of m is reached, n+1 is added and m is reset to 1, and the analysis is repeated until the maximum values of n and m are reached at the same time.

[0076] In actual application, the first daily data in Table 1 is Q(1,1), the second daily data is Q(1,2), and the third daily data is Q(2,1). Taking this as an example, when n=1, the value range of m is 1 to 8, so the value range of i is also 1 to 8, that is, there are time period groups T1 to T8, Q(1,1) to Q(1,8) are E(1,1) to E(1,8) respectively, starting with n=2 and m=1, the time period intersection of Q(2,1) and E(1,1) is [8:32,9:31]. Then analyze the time period intersection of Q(2,1) and E(1,2) to E(1,8) respectively. From the analysis results, the maximum value of the time period intersection is the time period intersection of Q(2,1) and E(1,1). Therefore, Q(2,1) is included in the time period group T1, and so on, analyze the time period group to which each Q(n,m) belongs;

[0077] For any T i , for T i Sort Q(n,m) in the order of weather air quality from small to large, and number them by symbol R(i,j), where j is a positive integer and (i,j) is the serial number of R, R(i,j) represents T i The jth Q(n,m) in

[0078] For any value of i, starting with j=2, calculate the difference in plant air quality between R(i,j) and R(i,1), marked as plant quality difference, and use symbol A h Indicates that the difference in weather air quality between R(i,j) and R(i,1) is calculated at the same time, marked as weather quality difference, and represented by symbol B h Represents, where h is a positive integer and h is the serial number of A and B;

[0079] See also Figure 2 As shown, each T i There exists a group A h and B h , with B h is the X-axis, A h Establish a plane rectangular coordinate system for the Y axis, named air quality correlation diagram, and divide each A h According to the corresponding B h Enter into the air quality correlation diagram;

[0080] Perform regression analysis on the air quality correlation graph and select the regression function with the smallest dispersion as the air quality correlation function;

[0081] In practical applications, T iBy grouping daily data with similar data periods into the same period group, the impact of pollutants generated in the factory on the air quality of the factory due to different data periods can be effectively removed. The impact of weather air quality on the air quality of the factory is usually fixed. After removing the impact of the data period, the impact relationship of weather air quality on the air quality of the factory can be analyzed. For A h and B h The calculation process has been described in detail in this embodiment and will not be elaborated here; the air quality correlation diagram is constructed as shown in FIG. Figure 2 As shown, the air quality correlation function obtained through regression analysis is A=0.0023×B 2 +0.1566×B+0.9174, where A is the plant quality difference and B is the weather quality difference;

[0082] The time period quality correlation unit is used to analyze the correlation between the data time period and the air quality of the factory building;

[0083] The time period quality association unit is configured with a time period quality association strategy, which includes:

[0084] For each piece of daily data, obtain the weather air quality of the daily data, substitute it into the air quality correlation function, mark the plant quality difference obtained by the solution as the environment elimination difference, reduce the plant air quality of the daily data by the environment elimination difference, and obtain the plant corrected air quality;

[0085] In practical applications, taking Q(1,1) as an example, the weather air quality is 68. Substituting it into the air quality correlation function, the environmental elimination difference is 22. The calculation result is rounded to an integer. The plant air quality in Q(1,1) is 78. Subtracting 22, the plant corrected air quality is 56. The plant corrected air quality eliminates the influence of weather air quality and only the air quality affected by pollutants generated in the plant is affected.

[0086] See also Figure 3 As shown in the figure, a plane rectangular coordinate system is established with time as the X-axis and the plant correction air quality as the Y-axis, which is named the period quality association diagram. The plant correction air quality of daily data is entered into the period quality association diagram according to the corresponding data period. Each daily data exists in the period quality association diagram in the form of a straight line, that is, within the time period of the data period, there are countless points on the plant correction air quality, and a straight line is formed by countless points, which is named the period quality line segment;

[0087] See also Figure 4As shown, the length of the time period quality segment is obtained, marked as the segment length. If the segment length is 1, a time period quality coordinate point is generated at the midpoint of the time period quality segment. If the segment length is 2, a time period quality coordinate point is generated at each of the two endpoints of the time period quality segment. If the segment length is greater than 2, the segment length is calculated minus 1 to obtain an equal fraction. A time period quality coordinate point is generated at each of the two endpoints of the time period quality segment, and a time period quality coordinate point is generated at each of the equal division points of the time period quality segment.

[0088] See also Figure 5 As shown in the figure, the time period quality line segments are removed and only the time period quality coordinate points are retained. Regression analysis is performed on the time period quality coordinate points, and the regression function with the smallest discrete degree is selected as the time period quality correlation function;

[0089] In practical applications, taking Q(1,1) as an example, its data period is [8:26,9:38], and the corrected air quality of the plant is 56. Therefore, the line segment corresponding to Q(1,1) in the period quality correlation diagram is the line segment with the X axis from 8:26 to 9:38 and the Y axis constant at 56. Similarly, the complete period quality correlation diagram is constructed as follows: Figure 3 As shown, Figure 4 Taking a time period quality line segment in as an example, the analysis process of the time period quality coordinate point is further explained. Figure 4 As shown, the X-axis is from 8:00 to 20:00, a total of 12 hours, so the length of the X-axis segment spanning 1 hour is 1, and the segment length only retains integers, for example Figure 4 The time period quality line segment in the total time period has gone through 1.6 hours, so the line segment length is 1.6, which is rounded to 2. Since the line segment length is 2, a time period quality coordinate point is generated at each of the two end points of the time period quality line segment. Assuming that the line segment length is 4, the line segment length is greater than 2, and it is subtracted by 1 to obtain an equal division of 3. A time period quality coordinate point is generated at each of the two end points of the time period quality line segment, and a time period quality coordinate point is generated at each of the 3 equal division points of the time period quality line segment, for a total of 4 time period quality coordinate points; the time period quality line segment is eliminated, and only the time period quality coordinate point after the time period quality coordinate point is retained. Figure 5 As shown in the figure, the time period quality correlation function obtained by regression analysis is Y=0.217×X 2 -0.2417×X+52.349, where X is time. Since the X-axis is between 8:00 and 20:00, 0 to 12 represents 8:00 to 20:00, and Y is the corrected air quality of the factory building.

[0090] The frequency-quality correlation unit is used to analyze the correlation between the fan frequencies required for plant air quality;

[0091] The frequency quality association unit is configured with a frequency quality association strategy, which includes:

[0092] See also Figure 6 As shown in the figure, a plane rectangular coordinate system is established with the plant air quality as the horizontal axis and the fan frequency as the vertical axis, which is named the frequency-quality correlation diagram. The fan frequency in the daily data is entered into the frequency-quality correlation diagram according to the corresponding plant air quality.

[0093] Perform regression analysis on the frequency-quality correlation diagram and select the regression function with the smallest discrete degree as the frequency-quality correlation function;

[0094] In practical applications, the frequency-quality correlation diagram is constructed as follows Figure 6 As shown in Figure 2, the frequency-quality correlation function obtained through regression analysis is C=0.5723×D+2.0482, where C is the fan frequency and D is the plant air quality.

[0095] The fan operation prediction module is used to predict the fan frequency of the fan based on daily data and operation rules; the fan operation prediction module includes an air quality prediction unit and a fan frequency prediction unit;

[0096] The air quality prediction unit is used to predict the air quality of the factory building;

[0097] The air quality prediction unit is configured with an air quality prediction strategy, which includes:

[0098] Take the first time period as a period, name it the prediction period, obtain the prediction period after the current time, and mark it as the period to be predicted;

[0099] Obtain the median value of the time period to be predicted, mark it as the median value to be predicted, substitute the median value to be predicted into the time period quality correlation function, and solve it to obtain the first air quality;

[0100] Obtain the weather air quality of the time period to be predicted, substitute it into the air quality correlation function to obtain the second air quality;

[0101] Adding the first air quality to the second air quality to obtain a predicted air quality;

[0102] In actual applications, the first duration can be set by the user. In this embodiment, the first duration is set to 30 minutes, that is, the air quality of the factory building in the next half hour is predicted and the fan frequency is accurately adjusted. The current time is 14:00, that is, the period to be predicted is 14:00 to 14:30, and the median value to be predicted is 14:15. Since 0 to 12 in this embodiment represents 8:00 to 20:00, the X corresponding to 14:15 is 6.25, which is substituted into the period quality correlation function Y=0.217×X 2-0.2417×X+52.349, the first air quality is 59, the calculation result is rounded to an integer, and the weather air quality of the predicted period is 65, which is substituted into the air quality correlation function A=0.0023×B 2 +0.1566×B+0.9174, the second air quality is 21, the result is rounded to an integer, and the first air quality is added to the second air quality to get the predicted air quality of 80;

[0103] The fan frequency prediction unit is used to predict the fan frequency;

[0104] The fan frequency prediction unit is configured with a fan frequency prediction strategy, which includes:

[0105] Substitute the predicted air quality into the frequency-quality correlation function and solve it to obtain the predicted frequency;

[0106] Send the predicted frequency to the fan frequency control module;

[0107] In practical applications, the predicted air quality is substituted into the frequency-quality correlation function C=0.5723×D+2.0482, and the predicted frequency is 48, and the calculation result is rounded to an integer.

[0108] The fan frequency control module is used to adjust the fan frequency based on the predicted fan frequency;

[0109] The fan frequency control module is configured with a fan frequency control strategy, which includes:

[0110] Receive predicted frequency;

[0111] The frequency converter cabinet is remotely controlled through the PLC programmable logic controller, and the fan frequency is then adjusted to the predicted frequency through the frequency converter cabinet;

[0112] In actual applications, the PLC programmable logic controller uses the existing PLC controller, sends control instructions to the frequency converter cabinet through the PLC controller, and then controls the fan frequency to 48Hz through the frequency converter cabinet.

[0113] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components 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 interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

Claims

1. The remote frequency conversion control system of the frequency conversion cabinet based on PLC is characterized by: It includes a daily data monitoring module, a fan operation analysis module, a fan operation prediction module and a fan frequency control module; the daily data monitoring module, the fan operation analysis module and the fan frequency control module are respectively connected to the fan operation prediction module data; The daily data monitoring module is used to monitor the daily data of the fan during operation; The fan operation analysis module is used to analyze the operation rules of the fan based on daily data; The fan operation prediction module is used to predict the fan frequency of the fan based on daily data and operation rules; The fan frequency control module is used to regulate the fan frequency based on the predicted fan frequency; The fan operation analysis module includes an air quality correlation unit, which is used to analyze the correlation between the air quality of the factory building and the weather air quality; The air quality association unit is configured with an air quality association strategy, and the air quality association strategy includes: Number the daily data of each day and use the symbol P n Indicates that n is a positive integer and n is the serial number of P, and then P is calculated according to the data period. n The daily data in the data are numbered and represented by the symbol Q(n,m), where m is a positive integer and (n,m) is the serial number of Q, Q(n,m) represents P n The mth daily data; Take Q(1,m) in P1 as the reference benchmark, marked as E(1,i), the value range of i is the same as that of m, and each value of i in E(1,i) represents a time period grouping. There are max(i) time period groups in total, which are represented by the symbol T i Indicates that max() is the maximum value operator; Starting with n=2 and m=1, analyze the intersection of the data periods of Q(n,m) and E(1,i), mark it as the period intersection, analyze the period intersection of Q(n,m) and E(1,i) for each value of i, find the maximum value, mark it as the maximum intersection, and include Q(n,m) in the period group T of E(1,i) corresponding to the maximum intersection i In the process, m+1 is added and the analysis is repeated. If the maximum value of m is reached, n+1 is added and m is reset to 1, and the analysis is repeated until the maximum values of n and m are reached at the same time. For any T i , for T i Sort Q(n,m) in the order of weather air quality from small to large, and number them by symbol R(i,j), where j is a positive integer and (i,j) is the serial number of R, R(i,j) represents T i The jth Q(n,m) in For any value of i, starting with j=2, calculate the difference in plant air quality between R(i,j) and R(i,1), marked as plant quality difference, and use symbol A h Indicates that the difference in weather air quality between R(i,j) and R(i,1) is calculated at the same time, marked as weather quality difference, and represented by symbol B h Represents, where h is a positive integer and h is the serial number of A and B; Each T i There exists a group A h and B h , with B h is the X-axis, A h Establish a plane rectangular coordinate system for the Y axis, named air quality correlation diagram, and divide each A h According to the corresponding B h Enter into the air quality correlation diagram; Perform regression analysis on the air quality correlation diagram and select the regression function with the smallest discrete degree as the air quality correlation function.

2. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 1 is characterized in that: The daily data monitoring module is configured with a daily data monitoring strategy, which includes: The daily data includes factory air quality, fan frequency, weather air quality and data period; The plant air quality, fan frequency, weather air quality and data period constitute a daily data item. The data period represents that the daily data does not change within the time range of the data period. If any one of the plant air quality, fan frequency and weather air quality in the daily data changes, a new daily data item is generated. The daily data is stored in a daily database.

3. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 1 is characterized in that: The wind turbine operation analysis module includes a time period quality correlation unit and a frequency quality correlation unit; The time period quality correlation unit is used to analyze the correlation between the data time period and the air quality of the factory building; The frequency-quality correlation unit is used to analyze the correlation between fan frequencies required for factory air quality.

4. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 3 is characterized in that: The time period quality association unit is configured with a time period quality association strategy, and the time period quality association strategy includes: For each piece of daily data, obtain the weather air quality of the daily data, substitute it into the air quality correlation function, mark the plant quality difference obtained by the solution as the environment elimination difference, reduce the plant air quality of the daily data by the environment elimination difference, and obtain the plant corrected air quality; A rectangular coordinate system is established with time as the X-axis and the plant calibrated air quality as the Y-axis, named the period quality association diagram. The plant calibrated air quality of daily data is entered into the period quality association diagram according to the corresponding data period. Each daily data exists in the period quality association diagram in the form of a straight line, that is, within the time period of the data period, there are countless points in the plant calibrated air quality, and these countless points form a straight line, named the period quality line segment; Get the length of the time period quality line segment, marked as the line segment length. If the line segment length is 1, generate a time period quality coordinate point at the midpoint of the time period quality line segment. If the line segment length is 2, generate a time period quality coordinate point at each of the two endpoints of the time period quality line segment. If the line segment length is greater than 2, calculate the line segment length minus 1 to obtain the equal fraction. Generate a time period quality coordinate point at each of the two endpoints of the time period quality line segment, and generate a time period quality coordinate point at each of the equal division points of the time period quality line segment. The time period quality line segments are eliminated and only the time period quality coordinate points are retained. Regression analysis is performed on the time period quality coordinate points, and the regression function with the smallest discrete degree is selected as the time period quality correlation function.

5. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 4 is characterized in that: The frequency quality association unit is configured with a frequency quality association strategy, and the frequency quality association strategy includes: A rectangular coordinate system is established with the plant air quality as the horizontal axis and the fan frequency as the vertical axis. This is named the frequency-quality correlation diagram. The fan frequency in the daily data is entered into the frequency-quality correlation diagram according to the corresponding plant air quality. Perform regression analysis on the frequency-quality correlation diagram and select the regression function with the smallest discrete degree as the frequency-quality correlation function.

6. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 5 is characterized in that: The fan operation prediction module includes an air quality prediction unit and a fan frequency prediction unit; The air quality prediction unit is used to predict the air quality of the factory building; The fan frequency prediction unit is used to predict the fan frequency.

7. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 6 is characterized in that: The air quality prediction unit is configured with an air quality prediction strategy, and the air quality prediction strategy includes: Take the first time period as a period, name it the prediction period, obtain the prediction period after the current time, and mark it as the period to be predicted; Obtain the median value of the time period to be predicted, mark it as the median value to be predicted, substitute the median value to be predicted into the time period quality correlation function, and solve it to obtain the first air quality; Obtain the weather air quality of the time period to be predicted, substitute it into the air quality correlation function to obtain the second air quality; The first air quality is added to the second air quality to obtain the predicted air quality.

8. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 7 is characterized in that: The fan frequency prediction unit is configured with a fan frequency prediction strategy, and the fan frequency prediction strategy includes: Substitute the predicted air quality into the frequency-quality correlation function and solve it to obtain the predicted frequency; The predicted frequency is sent to the fan frequency control module.

9. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 8, characterized in that: The fan frequency control module is configured with a fan frequency control strategy, which includes: Receive predicted frequency; The frequency converter cabinet is remotely controlled through the PLC programmable logic controller, and then the fan frequency is adjusted to the predicted frequency through the frequency converter cabinet.

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