Frequency conversion cabinet remote frequency conversion control system based on plc

Abstract

The invention discloses a frequency conversion cabinet remote frequency conversion control system based on plc, which relates to the technical field of frequency conversion cabinet remote frequency conversion control and comprises 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 for monitoring daily data during operation of the fan; the fan operation analysis module is used for analyzing the operation rule of the fan based on the daily data; the fan operation prediction module is used for predicting the fan frequency of the fan based on the daily data and the operation rule; the fan frequency control module is used for regulating and controlling the fan frequency based on the predicted fan frequency; the method is used for solving the problems that when an existing frequency conversion cabinet remote frequency conversion control technology is used for conducting frequency conversion control on a draught fan, frequency regulation and control on the draught fan are not accurate enough and not reasonable enough, electric power resources are prone to being wasted, and the indoor air environment is poor.

Description

Technical Field

[0001] The present invention relates to the technical field of remote variable-frequency control of variable-frequency cabinets, and specifically to a remote variable-frequency control system for variable-frequency cabinets based on PLC. Background Art

[0002] The remote variable-frequency control technology of variable-frequency cabinets refers to the technology of using remote communication technology to remotely monitor, operate, and control the frequency converters in variable-frequency cabinets through networks or other communication means. Through this technology, users can remotely and real-time monitor the operating status, parameters, and fault information of the frequency converters, and realize operations such as remotely turning on and off the frequency converters, adjusting speeds, and setting parameters, so as to achieve remote control and management of the equipment. This technology has important application significance in the fields of industrial automation and intelligent control, can improve production efficiency, reduce operating costs, and at the same time facilitate remote maintenance and management of the equipment.

[0003] When the existing remote variable-frequency control technology of variable-frequency cabinets is used for variable-frequency control of a fan to purify the indoor air environment, it usually sets multiple gears to control the fan or performs manual regulation, unable to achieve precise adjustment. And usually, the frequency of the fan is increased only after it is found that the indoor air environment is poor, making it difficult to predict the changes in the indoor air environment and thus adjust the frequency of the fan in advance, so that the indoor air environment can always be kept within a good range. For example, in the Chinese patent with the 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 is not dynamically adjusted according to the indoor air environment. If they all operate at a relatively high frequency, it will cause waste of electric power resources. If they operate at a relatively low frequency, the indoor air environment will be poor. Moreover, the indoor air environment is also affected by the outdoor air environment. If the outdoor air environment is poor, it will be brought into the indoor during ventilation, further affecting 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 electric power resources. The existing remote variable-frequency control technology of variable-frequency cabinets also has problems of inaccurate and unreasonable frequency regulation of the fan when used for variable-frequency control of the fan, resulting in easy waste of electric power 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 during the operation of the fan, then analyzing the correlation between the air quality in the plant and the air quality of the weather, then analyzing the correlation between the data period and the air quality in the plant, and at the same time analyzing the correlation between the fan frequencies required for the air quality in the plant, and then predicting the air quality in the plant, then predicting the fan frequencies, and finally regulating the fan frequencies based on the predicted fan frequencies, so as to solve the problems that the existing remote variable-frequency control technology for variable-frequency cabinets is not accurate and reasonable enough in regulating the frequencies of fans when used for variable-frequency control of fans, resulting in easy waste of electric power resources and poor indoor air environment.

[0005] To achieve the above object, the present application provides a remote variable-frequency control system for a variable-frequency cabinet based on a PLC, 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 for data connection; The daily data monitoring module is used to monitor the daily data during the operation of the fan; The fan operation analysis module is used to analyze the operation law of the fan based on the daily data; The fan operation prediction module is used to predict the fan frequency of the fan based on the daily data and the operation law; The fan frequency control module is used to regulate the fan frequency based on the predicted fan frequency.

[0006] Further, the daily data monitoring module is configured with a daily data monitoring strategy, and the daily data monitoring strategy includes: The daily data includes the air quality in the plant, the fan frequency, the air quality of the weather, and the data period; The air quality in the plant, the fan frequency, the air quality of the weather, and the data period form a piece of daily data. The data period represents that the daily data has not changed within the time range where the data period is located. If any one of the air quality in the plant, the fan frequency, and the air quality of the weather in the daily data changes, a new piece of daily data is generated; The daily data is stored in a daily database.

[0007] Further, the fan operation analysis module includes an air quality correlation unit, a period-quality correlation unit, and a frequency-quality correlation unit; The air quality correlation unit is used to analyze the correlation between the air quality in the plant and the air quality of the weather; The period-quality correlation unit is used to analyze the correlation between the data period and the air quality in the plant; The frequency-quality correlation unit is used to analyze the correlation between the fan frequencies required for the air quality in the plant.

[0008] Furthermore, the air quality correlation unit is configured with an air quality correlation strategy, and the air quality correlation strategy includes: Number the daily data for each day, denoted by the symbol P n , where n is a positive integer and n is the serial number of P. Then, number the daily data within P n according to the data time period, denoted 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 the m-th daily data in P n ; Take Q(1,m) in P 1 as the reference benchmark, denoted as E(1,i). The value range of i is the same as that of m. Each value of i in E(1,i) represents a time period group, and there are max(i) time period groups in total, denoted by the symbol T i , where max() is the maximum value operator; Starting from n = 2 and m = 1, analyze the intersection of the data time periods of Q(n,m) and E(1,i), denoted as the time period intersection. Analyze the time period intersection of Q(n,m) and E(1,i) for each value of i, find the maximum value among them, denoted as the maximum intersection, and incorporate Q(n,m) into the time period group T i corresponding to the maximum intersection. Then increment m by 1 and analyze again. If the maximum value of m is reached, increment n by 1 and reset m to 1, and analyze again until the maximum values of both n and m are reached; For any T i , sort the Q(n,m) in T i in ascending order of the air quality of the weather, and number them in order, denoted by the symbol R(i,j), where j is a positive integer and (i,j) is the serial number of R. R(i,j) represents the j-th Q(n,m) in T i ; For any value of i, starting from j = 2, calculate the difference in the air quality of the plant between R(i,j) and R(i,1), denoted as the plant quality difference, by the symbol A h , and at the same time calculate the difference in the air quality of the weather between R(i,j) and R(i,1), denoted as the weather quality difference, by the symbol B h , where h is a positive integer and h is the serial number of A and B; There is a set of A i and B h in each T h . Taking Bh is the X-axis, and A h is the Y-axis to establish a plane rectangular coordinate system, named the air quality correlation diagram. Each A h is entered into the air quality correlation diagram according to the corresponding B h ; Perform a regression analysis on the air quality correlation diagram, and select the regression function with the smallest degree of dispersion as the air quality correlation function.

[0009] Furthermore, the time period quality correlation unit is configured with a time period quality correlation strategy, and the time period quality correlation 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 obtained plant quality difference as the environmental elimination difference, and subtract the environmental elimination difference from the plant air quality of the daily data to obtain the plant corrected air quality; Establish a plane rectangular coordinate system with time as the X-axis and the plant corrected air quality as the Y-axis, named the time period quality correlation diagram. Enter the plant corrected air quality of the daily data into the time period quality correlation diagram according to the corresponding data time period. Each piece of daily data exists in the form of a straight line in the time period quality correlation diagram, that is, within the time of the data time period, there are countless points for the plant corrected air quality, and a straight line is formed by countless points, named the time period quality line segment; Obtain 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 number of equal parts, generate a time period quality coordinate point at each of the two endpoints of the time period quality line segment, and at the same time generate a time period quality coordinate point at each of the equal division points of the number of equal parts of the time period quality line segment; Delete the time period quality line segment, only retain the time period quality coordinate points, perform a regression analysis on the time period quality coordinate points, and select the regression function with the smallest degree of dispersion as the time period quality correlation function.

[0010] Furthermore, the frequency quality correlation unit is configured with a frequency quality correlation strategy, and the frequency quality correlation strategy includes: Establish a plane rectangular coordinate system with the plant air quality as the horizontal axis and the fan frequency as the vertical axis, named the frequency quality correlation diagram. Enter the fan frequencies in the daily data into the frequency quality correlation diagram according to the corresponding plant air quality; Perform a regression analysis on the frequency quality correlation diagram, and select the regression function with the smallest degree of dispersion as the frequency quality correlation function.

[0011] Further, 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 in the factory building; The fan frequency prediction unit is used to predict the fan frequency.

[0012] Further, the air quality prediction unit is configured with an air quality prediction strategy, and the air quality prediction strategy includes: Taking the first time period as a cycle, named the prediction cycle, obtaining the prediction cycle after the current time, and marking it as the to-be-predicted time period; Obtaining the median value of the to-be-predicted time period, marking it as the to-be-predicted median value, substituting the to-be-predicted median value into the time period quality correlation function, and solving to obtain the first air quality; Obtaining the weather air quality of the to-be-predicted time period, substituting it into the air quality correlation function to solve for the second air quality; Adding the first air quality and the second air quality to obtain the predicted air quality.

[0013] Further, the fan frequency prediction unit is configured with a fan frequency prediction strategy, and the fan frequency prediction strategy includes: Substituting the predicted air quality into the frequency-quality correlation function to solve for the predicted frequency; Sending the predicted frequency to the fan frequency control module.

[0014] Further, the fan frequency control module is configured with a fan frequency control strategy, and the fan frequency control strategy includes: Receiving the predicted frequency; Remotely controlling the frequency conversion cabinet through a PLC programmable logic controller, and then adjusting the fan frequency to the predicted frequency through the frequency conversion cabinet.

[0015] Advantages of the present invention: By monitoring the daily data during the operation of the fan, then analyzing the correlation between the air quality in the factory building and the weather air quality, analyzing the correlation between the data time period and the air quality in the factory building, and simultaneously analyzing the correlation between the air quality in the factory building and the required fan frequency, the advantage is that the equipment in the factory building usually performs the same operation within the same time period, so the impact of the equipment on the air quality in the factory building within the same time period is usually fixed, and there is usually a ventilation system in the factory, and the ventilation system will exchange the air in the factory building with the air outside, so the air quality of the outdoor weather also has a certain impact on the air quality in the factory building. By analyzing their correlations, it provides a data basis for the subsequent prediction of the air quality in the factory building and the fan frequency, and improves 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; The present invention predicts the fan frequency based on the correlation between the air quality in the plant and the air quality of the weather, the correlation between the data period and the air quality in the plant, and the correlation between the required fan frequency for the air quality in the plant. Finally, the fan frequency is regulated based on the predicted fan frequency. The advantage is that the factors affecting the air quality in the plant are considered from multiple perspectives and predicted, so that the fan frequency can be regulated in advance, keeping the air quality in the plant always within a good range, and improving the effectiveness and accuracy of the remote variable-frequency control technology of the variable-frequency cabinet when used for variable-frequency control of the fan. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic block diagram of the system of the present invention; Figure 2 is an air quality correlation diagram of the present invention; Figure 3 is a time period-quality correlation diagram of the present invention; Figure 4 is a schematic diagram of a time period-quality line segment of the present invention; Figure 5 is a schematic diagram of a time period-quality coordinate point of the present invention; Figure 6 is a frequency-quality correlation diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0018] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0019] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0020] Embodiment 1. Refer to Figure 1 As shown, the present application provides a remote variable-frequency control system for a variable-frequency cabinet based on a PLC, 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 for data connection; The daily data monitoring module is used to monitor the daily data during the operation of the fan; The daily data monitoring module is configured with a daily data monitoring strategy, and the daily data monitoring strategy includes: The daily data includes the air quality in the plant, the fan frequency, the air quality of the weather, and the data period; The air quality in the plant, the fan frequency, the air quality of the weather, and the data period constitute a piece of daily data. The data period represents that the daily data has not changed within the time range of the data period. If any one of the air quality in the plant, the fan frequency, and the air quality of the weather in the daily data changes, a new piece of daily data is generated; The daily data is stored in the daily database; In practical applications, some of the daily data in the daily database is shown in Table 1 below: Table 1 Some of the daily data in the daily database Date Air quality in the workshop Air quality of the weather 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] Among them, both the air quality in the plant and the air quality of the weather are calculated using the AQI air quality index. The air quality of the weather refers to the indicators given in the weather forecast, and the air quality in the plant is evaluated using the indoor air quality assessment method. This embodiment will not elaborate on it specifically. The data period is accurate to minutes. For example, 8:26 is 8 hours and 26 minutes, and the fan frequency is the operating frequency of the fan motor.

[0021] The fan operation analysis module is used to analyze the operation law of the fan based on the daily data; the fan operation analysis module includes an air quality correlation unit, a period quality correlation unit, and a frequency quality correlation unit; The air quality correlation unit is used to analyze the correlation between the air quality in the plant and the air quality of the weather; The air quality correlation unit is configured with an air quality correlation strategy, and the air quality correlation strategy includes: Number the daily data of each day, represented by the symbol P n where n is a positive integer and n is the serial number of P. Then, number the daily data in P n according to the data period, 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 the mth piece of daily data in P n ; In practical applications, taking Table 1 as an example, the daily data of two days, April 16, 2024 and April 17, 2024, are numbered to obtain P 1 and P 2 , where P 1 is the daily data of April 16, 2024, and P 2The daily data for April 17, 2024. As can be seen from Table 1, there are two pieces of daily data for April 16, 2024, namely Q(1,1) and Q(1,2), and so on. The data shown in Table 1 is partial data. In fact, there are a total of 86 days of daily data during the numbering process, that is, 1 ≤ n ≤ 86, and the number of daily data included in different dates varies. Therefore, for each value of n, the range of m values changes; Take Q(1,m) in P 1 as a reference benchmark and label it as E(1,i). The range of i is the same as that of m. Each value of i in E(1,i) represents a time period group, and there are max(i) time period groups, represented by the symbol T i where max() is the maximum value operator; Starting from n = 2 and m = 1, analyze the intersection of the data time periods of Q(n,m) and E(1,i), label it as the time period intersection. Analyze the time period intersection of Q(n,m) and E(1,i) for each value of i, find the maximum value among them, label it as the maximum intersection, and incorporate Q(n,m) into the time period group T i corresponding to the maximum intersection. Then increment m by 1 and analyze again. If the maximum value of m is reached, increment n by 1 and reset m to 1, and analyze again until both the maximum value of n and the maximum value of m are reached; In practical applications, the first piece of daily data in Table 1 is Q(1,1), the second is Q(1,2), the third is Q(2,1), and so on. For example, when n = 1, the range of m values is from 1 to 8. Therefore, the range of i values is also from 1 to 8, that is, there are time period groups T 1 to T 8 . Q(1,1) to Q(1,8) are respectively E(1,1) to E(1,8). Starting from 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 intersections of Q(2,1) with E(1,2) to E(1,8) respectively. From the analysis results, the maximum value of the time period intersections is the time period intersection of Q(2,1) and E(1,1). Therefore, incorporate Q(2,1) into the time period group T 1 and so on, analyzing the time period group to which each Q(n,m) belongs; For any T i , sort the Q(n,m) in T i in ascending order of weather air quality and number them. It is represented by the symbol R(i,j), where j is a positive integer and (i,j) is the serial number of R. R(i,j) represents the jth Q(n,m) in T i ; For any value of i, starting from j = 2, calculate the difference in the air quality of the factory building between R(i, j) and R(i, 1), mark it as the factory building quality difference, and represent it by the symbol A h At the same time, calculate the difference in the air quality of the weather between R(i, j) and R(i, 1), mark it as the weather quality difference, and represent it by the symbol B h where h is a positive integer and h is the serial number of A and B; Please refer to Figure 2 As shown, there is a set of A i and B h in each T h . Taking B h as the X-axis and A h as the Y-axis, establish a plane rectangular coordinate system, name it the air quality correlation graph, and input each A h into the air quality correlation graph according to the corresponding B h ; Perform regression analysis on the air quality correlation graph, and select the regression function with the smallest degree of dispersion as the air quality correlation function; In practical applications, T i is the time period grouping. Grouping daily data with similar data time periods into the same time period grouping can effectively remove the influence of pollutants generated in the factory building on the air quality of the factory building due to different data time periods. The influence of the air quality of the weather on the air quality of the factory building is usually fixed. After removing the influence of the data time period, the influence relationship between the air quality of the weather and the air quality of the factory building can be analyzed. The calculation process for A h and B h has been described in detail in this embodiment and will not be elaborated here; The constructed air quality correlation graph is as Figure 2 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 factory building quality difference and B is the weather quality difference; 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 time period quality correlation unit is configured with a time period quality correlation strategy, and the time period quality correlation strategy includes: For each piece of daily data, obtain the air quality of the weather of the daily data, substitute it into the air quality correlation function, mark the obtained factory building quality difference as the environmental elimination difference, and subtract the environmental elimination difference from the air quality of the factory building of the daily data to obtain the factory building corrected air quality; In practical applications, taking Q(1,1) as an example, the air quality of the weather is 68. Substituting it into the air quality correlation function, the environmental elimination difference obtained by solving is 22. The calculation result is rounded to an integer. The air quality of the factory building in Q(1,1) is 78. Subtracting 22 gives the corrected air quality of the factory building as 56. The corrected air quality of the factory building eliminates the influence of the weather air quality and is only affected by the pollutants generated in the factory building; Please refer to Figure 3 As shown, taking time as the X-axis and the corrected air quality of the factory building as the Y-axis, a plane rectangular coordinate system is established and named the time-period quality correlation diagram. The corrected air quality of the daily data is entered into the time-period quality correlation diagram according to the corresponding data time periods. Each piece of daily data exists in the form of a straight line in the time-period quality correlation diagram. That is, within the time of the data time period, there are countless points for the corrected air quality of the factory building, and a straight line is formed by countless points, which is named the time-period quality line segment; Please refer to Figure 4 As shown, obtain the length of the time-period quality line segment, marked as the line segment length. If the line segment length is 1, a time-period quality coordinate point is generated at the midpoint of the time-period quality line segment. If the line segment length is 2, a time-period quality coordinate point is generated 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 number of equal parts. A time-period quality coordinate point is generated at each of the two endpoints of the time-period quality line segment, and at the same time, a time-period quality coordinate point is generated at each of the equal division points of the number of equal parts of the time-period quality line segment; Please refer to Figure 5 As shown, remove the time-period quality line segment and only retain the time-period quality coordinate points. Perform regression analysis on the time-period quality coordinate points, and select the regression function with the smallest degree of dispersion as the time-period quality correlation function; In practical applications, taking Q(1,1) as an example, its data time period is [8:26, 9:38], and the corrected air quality of the factory building is 56. Therefore, the line segment corresponding to Q(1,1) in the time-period quality correlation diagram is a line segment with the X-axis from 8:26 to 9:38 and the Y-axis constantly 56. Similarly, a complete time-period quality correlation diagram is constructed as Figure 3 shown, taking Figure 4 one of the time-period quality line segments in Figure 4 as an example, the analysis process of the time-period quality coordinate points is further explained. As Figure 4If the time period quality line segment in Figure 5 has experienced 1.6 hours in total, then the length of the line segment is 1.6, which is rounded up to 2. Since the length of the line segment is 2, a time period quality coordinate point is generated at each of the two endpoints of the time period quality line segment. Assuming the length of the line segment is 4, at this time the length of the line segment is greater than 2, and subtracting 1 from it gives the number of equal parts as 3. A time period quality coordinate point is generated at each of the two endpoints of the time period quality line segment, and at the same time a time period quality coordinate point is generated at each of the 3 equal division points of the time period quality line segment, resulting in a total of 4 time period quality coordinate points; After removing the time period quality line segment and only retaining the time period quality coordinate points, the time period quality coordinate points are as shown in Figure 5 Shown, through regression analysis, the time period quality correlation function is obtained as 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 is used to represent 8:00 to 20:00, and Y is the corrected air quality of the workshop; The frequency quality correlation unit is used to analyze the correlation between the fan frequencies required for the air quality of the workshop; The frequency quality correlation unit is configured with a frequency quality correlation strategy, and the frequency quality correlation strategy includes: Please refer to Figure 6 Shown, taking the air quality of the workshop as the horizontal axis and the fan frequency as the vertical axis, a plane rectangular coordinate system is established, named the frequency quality correlation diagram, and the fan frequencies in the daily data are entered into the frequency quality correlation diagram according to the corresponding air quality of the workshop; Perform regression analysis on the frequency quality correlation diagram, and select the regression function with the smallest degree of dispersion as the frequency quality correlation function; In practical applications, the constructed frequency quality correlation diagram is as shown in Figure 6 Shown, through regression analysis, the frequency quality correlation function is obtained as C = 0.5723×D + 2.0482, where C is the fan frequency and D is the air quality of the workshop.

[0022] The fan operation prediction module is used to predict the fan frequency of the fan based on the daily data and the operation rules; 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 workshop; The air quality prediction unit is configured with an air quality prediction strategy, and the air quality prediction strategy includes: Taking the first time period as a cycle, named the prediction cycle, obtain the prediction cycle after the current time, and mark it as the time period to be predicted; Obtain the median of the time period to be predicted, mark it as the median to be predicted, substitute the median to be predicted into the time period quality correlation function, and solve to obtain the first air quality; Obtain the weather air quality during the period to be predicted, and substitute it into the air quality correlation function to solve for the second air quality; Add the first air quality and the second air quality to obtain the predicted air quality; In practical 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 in the factory building within 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 from 14:00 to 14:30. The median value to be predicted is 14:15. Since this embodiment uses 0 to 12 to represent 8:00 to 20:00, the X corresponding to 14:15 is 6.25. Substitute it into the period quality correlation function Y = 0.217×X 2 -0.2417×X + 52.349, solve to obtain the first air quality as 59, and round the calculation result to an integer. Obtain the weather air quality during the period to be predicted as 65, and substitute it into the air quality correlation function A = 0.0023×B 2 +0.1566×B + 0.9174, solve to obtain the second air quality as 21, and round the calculation result to an integer. Add the first air quality and the second air quality to obtain the predicted air quality as 80; The fan frequency prediction unit is used to predict the fan frequency; 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 to solve for the predicted frequency; Send the predicted frequency to the fan frequency control module; In practical applications, substitute the predicted air quality into the frequency-quality correlation function C = 0.5723×D + 2.0482, and solve to obtain the predicted frequency as 48, and round the calculation result to an integer.

[0023] The fan frequency control module is used to adjust the fan frequency based on the predicted fan frequency; The fan frequency control module is configured with a fan frequency control strategy, and the fan frequency control strategy includes: Receive the predicted frequency; Remotely control the frequency conversion cabinet through the PLC programmable logic controller, and then adjust the fan frequency to the predicted frequency through the frequency conversion cabinet; In practical applications, the PLC programmable logic controller uses an existing PLC controller. Send a control command to the frequency conversion cabinet through the PLC controller, and then adjust the fan frequency to 48 Hz through the frequency conversion cabinet.

[0024] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media that contain computer-usable program code. Among them, the storage medium can 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 memory, flash memory, a magnetic disk, or an optical disk. These computer program instructions can 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 a manufactured article including an instruction device, and the instruction device implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or the functions specified in multiple blocks.

[0025] 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 only illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another 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 displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

Claims

1. PLC-based frequency conversion cabinet remote frequency conversion control system, characterized in that: 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 with 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 adjust the fan frequency based on the predicted fan frequency.

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, and the daily data monitoring strategy includes: The daily data includes plant 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. The data period represents that the daily data has not changed 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 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 fan operation analysis module includes an air quality association unit, a time period quality association unit and a frequency quality association unit; The air quality correlation unit is used to analyze the correlation between the air quality of the factory building and the weather air quality; The time period quality association 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 the fan frequencies required for the air quality of the factory building.

4. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 3 is characterized in that: 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, using the symbol P n Indicates, where n is a positive integer and n is the serial number of P, and then P is sorted 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 time periods of Q(n,m) and E(1,i), mark it as the time period intersection, analyze the time 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 time 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 again until the maximum values ​​of n and m are reached at the same time. For any T i , for T i Q(n,m) in the formula is sorted and numbered in ascending order of weather air quality, represented by the symbol R(i,j), where j is a positive integer and (i,j) is the serial number of R, and R(i,j) represents T i The jth Q(n,m) in For any value of i, starting with j=2, calculate the difference between the plant air quality in R(i,j) and R(i,1), marked as the plant quality difference, and represented by the 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 It represents, where h is a positive integer and h is the serial number of A and B; Each T i There exists a set 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 the 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.

5. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 4 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 daily data, the weather air quality of the daily data is obtained, and it is substituted into the air quality correlation function. The plant quality difference obtained by solving is marked as the environment elimination difference, and the plant air quality of the daily data is reduced by the environment elimination difference to obtain the plant corrected air quality; A plane rectangular coordinate system is established with time as the X-axis and the plant correction air quality as the Y-axis, named as the time period quality association diagram. The plant correction air quality of daily data is entered into the time period quality association diagram according to the corresponding data period. Each daily data exists in the time 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 correction air quality, and countless points form a straight line, named as the time 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, a time period quality coordinate point is generated at the midpoint of the time period quality line segment. If the line segment length is 2, a time period quality coordinate point is generated 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 an equal fraction, and generate a time period quality coordinate point at each of the two endpoints of the time period quality line segment. At the same time, a time period quality coordinate point is generated at each of the equal division points of the equal fraction 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.

6. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 5 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 plane rectangular coordinate system is established with the plant air quality as the horizontal axis and the fan frequency as the vertical axis, 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.

7. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 6 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.

8. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 7 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 length as a period, name it as 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 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.

9. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 8 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 for the predicted frequency; The predicted frequency is sent to the fan frequency control module.

10. The PLC-based frequency conversion cabinet remote frequency conversion control system according to claim 9, characterized in that: The fan frequency control module is configured with a fan frequency control strategy, and the fan frequency control strategy 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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