Intelligent energy-saving fan operation control method and system for papermaking industry

Abstract

The intelligent energy-saving fan operation control method comprises the steps that a plurality of sampling points are preset in a workshop, a reference light source is arranged on the opposite side of the sampling points, and the light source emits polarized light with preset intensity to penetrate through a dust area; acquiring environment image data of sampling points preset in a workshop; analyzing dust concentration data of the sampling points according to the environment image data; according to the position relation between each sampling point and the corresponding fan and the dust concentration index, the initial power of the corresponding fan is calculated through a distance weighting algorithm; and monitoring the dust concentration change rate of each region in real time, and dynamically adjusting the power of each fan by adopting a PID (Proportion Integration Differentiation) algorithm when detecting that the dust concentration change rate of the target region reaches a critical value. The dust concentration in the workshop is analyzed based on the environment image data, the fan power is dynamically adjusted, accurate response to dust concentration changes in different areas is achieved, and therefore the fan operation efficiency is improved, and energy waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent energy-saving fan operation control method and system for the papermaking industry. Background Art

[0002] In today's papermaking industry, dust pollution is an issue that cannot be ignored. Paper mills generate a lot of dust in many links such as raw material processing, pulping, and papermaking. This dust not only has a serious impact on the air quality in the workshop, endangers the health of workers, causes occupational health problems such as respiratory diseases, but also may reduce the quality of paper and affect production efficiency.

[0003] In order to deal with the dust problem, paper mills currently generally use fans for ventilation and dust removal. However, the existing fan operation mode has significant defects. At present, most paper mill fans are in a full-time open mode, that is, no matter what the actual dust concentration in each area of ​​the workshop is, the fan always runs continuously at a constant power.

[0004] This operation mode has many disadvantages. On the one hand, because the difference in dust concentration in different areas of the workshop is not taken into account, the fan still maintains high power operation in some areas with low dust concentration, resulting in a large waste of energy and increasing the production cost of the enterprise. On the other hand, for areas with high dust concentration, the fan with constant power may not provide sufficient ventilation, and thus cannot effectively reduce the dust concentration, making it difficult for the air quality in the workshop to reach the ideal standard. Summary of the invention

[0005] In order to solve at least one of the technical problems mentioned above, the present invention provides an intelligent energy-saving fan operation control method and system for the papermaking industry.

[0006] In a first aspect, the present invention provides an intelligent energy-saving fan operation control method for the papermaking industry, the method comprising:

[0007] A plurality of sampling points are preset in the workshop, and a reference light source is set on the opposite side of the sampling points, wherein the light source emits polarized light of preset intensity to penetrate the dust area;

[0008] Collect environmental image data of preset sampling points in the workshop; analyze dust concentration data of sampling points based on environmental image data;

[0009] According to the positional relationship between each sampling point and the corresponding fan and the dust concentration index, the initial power of the corresponding fan is calculated by the distance weighted algorithm;

[0010] The dust concentration change rate in each area is monitored in real time. When it is detected that the dust concentration change rate in the target area reaches the critical value, the PID algorithm is used to dynamically adjust the power of each fan.

[0011] Preferably, analyzing the dust concentration data of the sampling points according to the environmental image data includes:

[0012] Analyze the average brightness value of environmental image data;

[0013] The average brightness value is input into the preset dust concentration prediction model to obtain the dust concentration data of the corresponding sampling point;

[0014] The dust concentration prediction model is expressed as:

[0015]

[0016] In the formula, C represents the dust concentration data, I represents the average brightness value of the environmental image data, and I 0 The brightness value of the reference image in the absence of dust, d represents the distance between the reference light source and the sampling point, and k represents the scattering coefficient of the dust.

[0017] Preferably, the calculation by the distance weighted algorithm is expressed as follows:

[0018]

[0019] P init =K·∑W i ·C i ,

[0020] In the formula, C i Represents the dust concentration data of the sampling point, d i Indicates the distance between the sampling point and the fan, α indicates the attenuation coefficient, the default value is α=0.5, W i Represents the weight coefficient of the sampling point, P init represents the initial power, and K represents the power conversion factor.

[0021] Preferably, the method further comprises:

[0022] If the calculated initial power of the fan exceeds the rated power range, the boundary value of the rated power range is taken.

[0023] In a second aspect, the present invention further provides an intelligent energy-saving fan operation control system for the papermaking industry, the system comprising:

[0024] A sampling point and light source setting module is used to preset multiple sampling points in the workshop and set a reference light source on the opposite side of the sampling point, wherein the light source emits polarized light of preset intensity to penetrate the dust area;

[0025] The environmental image data acquisition and analysis module is used to collect environmental image data of preset sampling points in the workshop; based on the environmental image data, the dust concentration data of the sampling points is analyzed;

[0026] The fan initial power calculation module is used to calculate the initial power of the corresponding fan through a distance weighted algorithm according to the position relationship between each sampling point and the corresponding fan and the dust concentration index;

[0027] The real-time monitoring and dynamic adjustment module is used to monitor the dust concentration change rate in each area in real time. When it is detected that the dust concentration change rate in the target area reaches a critical value, the PID algorithm is used to dynamically adjust the power of each fan.

[0028] Preferably, the environmental image data acquisition and analysis module is also used for:

[0029] Analyze the average brightness value of environmental image data;

[0030] The average brightness value is input into the preset dust concentration prediction model to obtain the dust concentration data of the corresponding sampling point;

[0031] The dust concentration prediction model is expressed as:

[0032]

[0033] In the formula, C represents the dust concentration data, I represents the average brightness value of the environmental image data, and I 0 The brightness value of the reference image in the absence of dust, d represents the distance between the reference light source and the sampling point, and k represents the scattering coefficient of the dust.

[0034] Preferably, the calculation by the distance weighted algorithm is expressed as follows:

[0035]

[0036] P init =K·∑W i ·C i ,

[0037] In the formula, C i Represents the dust concentration data of the sampling point, d i Indicates the distance between the sampling point and the fan, α indicates the attenuation coefficient, the default value is α=0.5, W i Represents the weight coefficient of the sampling point, P init represents the initial power, and K represents the power conversion factor.

[0038] In a third aspect, the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0039] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation thereof.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1) The present invention monitors the dust concentration in real time and dynamically adjusts the fan power according to the dust concentration and the position relationship between the sampling point and the fan. In areas or time periods with low dust concentration, the fan power is reduced to reduce energy consumption; in areas or time periods with high dust concentration, the fan power is increased to ensure good ventilation effect. This on-demand adjustment method can effectively reduce the energy consumption of the fan and achieve energy-saving goals.

[0042] 2) The present invention calculates the initial power of the fan by using a distance-weighted algorithm, taking into account factors such as the distance between the sampling point and the fan and the dust concentration. A reasonable weight is assigned to each sampling point according to the distance and dust concentration, accurately reflecting the actual demand for fan power in different areas, avoiding energy waste or insufficient ventilation due to uniform fixed power operation, and deriving the actual initial power in combination with the power conversion coefficient. The fan power can be flexibly adapted according to the dust distribution in the workshop, thereby improving energy utilization efficiency, ensuring good ventilation in the workshop, and facilitating smooth production and workers' health.

[0043] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0045] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0046] Figure 1 A flow chart of an intelligent energy-saving fan operation control method for the papermaking industry provided by an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of the structure of an intelligent energy-saving fan operation control system for the papermaking industry provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] Currently, paper mill fans use a constant power operation mode that is turned on throughout the entire process. They are unable to accurately adjust the power according to the differences in dust concentration in different areas of the workshop and real-time changes, which leads to energy waste and cannot effectively reduce dust concentration. They also lack intelligence and precision, making it difficult to meet environmental protection and production needs.

[0051] See also Figure 1 , Figure 1 The present invention provides a flow chart of an intelligent energy-saving fan operation control method for the papermaking industry. Figure 1 As shown, the method includes:

[0052] S100, multiple sampling points are preset in the workshop, and a reference light source is set on the opposite side of the sampling point, wherein the light source emits polarized light of preset intensity to penetrate the dust area;

[0053] In the papermaking workshop, multiple sampling points are preset according to the layout of the workshop and the distribution of fans. The sampling points are evenly distributed in areas prone to dust generation, such as near pulpers, beaters, paper machines and other equipment. On the opposite side of each sampling point, a reference light source is precisely set. The reference light source uses a high-stability light-emitting diode array, which is collimated by an optical lens group to emit polarized light with a preset intensity. The polarization direction of the polarized light is calibrated so that it can stably penetrate the dusty area and reduce interference caused by light scattering and refraction.

[0054] S200, collecting environmental image data of preset sampling points in the workshop; analyzing dust concentration data of the sampling points according to the environmental image data;

[0055] The environmental image data of the preset sampling points in the workshop are collected at preset time intervals (such as once per second). The collected image data is first subjected to noise reduction processing, and a noise reduction algorithm based on wavelet transform is used to remove random noise in the image, and the image is grayed to convert the color image into a grayscale image. Further, according to the Lambert-Beer law, the average brightness value of the environmental image data is analyzed based on the exponential relationship between the attenuation of light intensity and the dust concentration and path length. The average brightness value is input into the preset dust concentration prediction model trained with historical experimental data to obtain the dust concentration data of the corresponding sampling point.

[0056] S300, calculating the initial power of the corresponding fan by a distance weighted algorithm according to the positional relationship between each sampling point and the corresponding fan and the dust concentration index;

[0057] The positional relationship between each sampling point and the corresponding fan is obtained in real time, and the distance-weighted algorithm is used to calculate the initial power of the corresponding fan in combination with the dust concentration index. In the distance-weighted algorithm, the weight coefficient is dynamically adjusted according to the distance between the sampling point and the fan and the dust concentration, taking into account the different degrees of influence of sampling points at different positions on the fan power. For example, a higher weight is assigned to a sampling point that is closer to the fan and has a higher dust concentration; a lower weight is assigned to a sampling point that is farther away from the fan and has a lower dust concentration. In this way, the initial power of the corresponding fan can be calculated more accurately, so that the fan can output a reasonable power according to the actual dust situation during the initial operation stage.

[0058] S400 monitors the dust concentration change rate in each area in real time. When it is detected that the dust concentration change rate in the target area reaches a critical value, the PID algorithm is used to dynamically adjust the power of each fan.

[0059] In order to accurately and in real time monitor the rate of change of dust concentration, the preset sampling points in the workshop are used to continuously collect environmental image data through industrial cameras, and a reasonable and high data collection frequency is set, such as collecting image data once every 1 second, to ensure that the dynamic changes of dust concentration are captured in time and avoid missing important concentration change information due to long collection intervals. After each environmental image data is collected, preprocessing such as noise reduction, grayscale, edge detection and morphological operations are performed, and the dust concentration data of the corresponding sampling point is calculated based on the dust concentration prediction model of the Lambert-Beer law. In one possible embodiment, parallel computing technology is used to process image data of multiple sampling points at the same time. The dust concentration change rate is calculated using a time series analysis method. Taking each sampling point as a unit, the continuously collected dust concentration data are arranged in chronological order to form a time series. For example, let the dust concentration of a certain sampling point collected for the nth time be C n , the dust concentration collected for the n-1th time is C n-1 , the time interval is Δt (Δt = 1), then the dust concentration change rate of the sampling point during this time period is r n The calculation formula is: Repeat the above calculation process to update the dust concentration change rate of each sampling point in real time.

[0060] According to the production process requirements, environmental standards and historical operation data of the papermaking workshop, a reasonable critical value of the dust concentration change rate is pre-set. For example, for different production areas, such as the raw material processing area and the papermaking area, different critical values ​​can be set respectively due to the characteristics of their dust generation and the different allowable dust concentration ranges. The raw material processing area may allow a relatively high dust concentration change rate, while the papermaking area has a higher requirement for paper quality, so its critical value will be set relatively low. After calculating the dust concentration change rate of each sampling point, the change rate r of each sampling point is calculated. n The corresponding critical value r 0 Real-time comparison is performed. If the dust concentration change rate r of a certain sampling point (i.e., target area) n Reach or exceed its corresponding critical value r 0 , immediately triggering the PID algorithm to adjust the fan power.

[0061] Before triggering the PID algorithm to adjust the fan power, the parameters of the PID algorithm are initialized. The PID algorithm contains three parameters: proportional coefficient K p , integral coefficient K i and the differential coefficient K d The initial values ​​of these parameters are set according to the actual situation and experience of the papermaking workshop. For example, for areas where dust concentration changes are more sensitive, the proportional coefficient K may be appropriately increased. p, in order to speed up the adjustment speed of the fan power; for areas where the dust concentration changes relatively steadily, the proportional coefficient K can be appropriately reduced p , to avoid too frequent fan power adjustment. The core of the PID algorithm is to calculate the fan power adjustment amount ΔP based on the deviation e between the dust concentration change rate and the critical value. Deviation e = r n -r target , where r target Indicates the target change rate. The output P of the proportional link p =K p e, directly produces a regulating effect according to the current deviation, so that the fan power can quickly respond to the change of dust concentration; the output P of the integral link i =K i ·∑ k k＝1 e·Δt, to accumulate past deviations, in order to eliminate steady-state errors, ensure that the fan power can eventually stabilize at a suitable value, and keep the dust concentration within a reasonable range; the output of the differential link The deviation trend in the future can be predicted based on the rate of change of the deviation, and the fan power can be adjusted in advance to improve the response speed and stability of the system. The final fan power adjustment amount ΔP = P p +P i +P d According to the fan power adjustment amount ΔP calculated by the PID algorithm, the power of the corresponding fan is adjusted in real time.

[0062] In this embodiment, the dust concentration is monitored in real time, and the fan power is dynamically adjusted according to the dust concentration and the position relationship between the sampling point and the fan. In areas or time periods with low dust concentration, the fan power is reduced to reduce energy consumption; in areas or time periods with high dust concentration, the fan power is increased to ensure good ventilation effect. This on-demand adjustment method can effectively reduce the energy consumption of the fan and achieve energy saving goals.

[0063] Preferably, analyzing the dust concentration data of the sampling points according to the environmental image data includes:

[0064] Analyze the average brightness value of environmental image data;

[0065] The average brightness value is input into the preset dust concentration prediction model to obtain the dust concentration data of the corresponding sampling point;

[0066] The dust concentration prediction model is expressed as:

[0067]

[0068] In the formula, C represents the dust concentration data, I represents the average brightness value of the environmental image data, and I 0The brightness value of the reference image in the absence of dust, d represents the distance between the reference light source and the sampling point, and k represents the scattering coefficient of the dust.

[0069] Based on the principle of Lambert-Beer law, when light is emitted from the reference light source and passes through the dust area to the image acquisition device, the dust will scatter and absorb the light, causing the light intensity to decay. The average brightness value I of the image will decrease with the increase of dust concentration. ln(I 0 / I) reflects the attenuation of light intensity, ln(I 0 The larger the ratio of ln(I / I) to I (i.e. the greater the light attenuation), the 0 The larger the value of / I), the higher the dust concentration. The kd in the denominator is a normalized parameter, where k takes into account the ability of the dust itself to scatter light, and d takes into account the distance that the light travels. Through the normalization of this denominator, the dust concentration C can be accurately calculated.

[0070] The dust concentration prediction model in this embodiment can quickly quantify the dust concentration by comparing the average brightness value of the environmental image data with the dust-free reference brightness value, combining the distance between the reference light source and the sampling point and the dust scattering coefficient, thereby greatly shortening the time required for dust concentration quantification. It can provide data support for dust concentration monitoring in places such as papermaking workshops in a timely manner, so as to quickly respond to changes in dust concentration.

[0071] Preferably, the calculation by the distance weighted algorithm is expressed as follows:

[0072]

[0073] P init =K·∑W i ·C i ,

[0074] In the formula, C i Represents the dust concentration data of the sampling point, d i Indicates the distance between the sampling point and the fan, α indicates the attenuation coefficient, the default value is α=0.5, W i Represents the weight coefficient of the sampling point, P init represents the initial power, and K represents the power conversion factor.

[0075] W i Indicates the weight coefficient of the i-th sampling point, reflecting the relative importance of the i-th sampling point in calculating the initial power of the wind turbine. i Represents the dust concentration data of the i-th sampling point. The higher the dust concentration, the greater the demand for fans in this area, and the greater the contribution in calculating the weight. iIndicates the distance between the i-th sampling point and the fan. The closer the distance to the fan, the greater the impact on the weight calculation. The attenuation coefficient is used to adjust the influence of distance in the weight calculation. By changing the value of the attenuation coefficient, the influence of distance on the weight can be flexibly adjusted according to the actual situation. For all sampling points The sum is calculated, where j traverses all sampling points and obtains a sum for a single sampling point. Normalize so that the sum of the weight coefficients of all sampling points is 1. init Indicates the initial power of the fan, which is calculated by comprehensively considering the weight coefficients of each sampling point and the dust concentration data. K represents the power conversion factor, which is used to convert the value calculated based on the sampling point data into the actual fan power value. It is related to the specific specifications and performance of the fan. i ·C i is the weight coefficient W for all sampling points i and dust concentration data C i The products of are summed up, and through this summation, the conditions of all sampling points are taken into account to obtain a value that can reflect the fan power demand of the dust conditions in the entire area.

[0076] In this embodiment, a distance-weighted algorithm is used to comprehensively consider factors such as the distance between the sampling point and the fan, dust concentration, etc. to calculate the initial power of the fan. A reasonable weight is given to each sampling point according to the distance and dust concentration, accurately reflecting the actual demand for fan power in different areas, avoiding energy waste or insufficient ventilation due to uniform fixed power operation, and combining the power conversion coefficient to obtain the actual initial power. The fan power can be flexibly adapted according to the dust distribution in the workshop, thereby improving energy utilization efficiency, ensuring good ventilation in the workshop, and facilitating smooth production and the health of workers.

[0077] Preferably, the method further comprises:

[0078] If the calculated initial power of the fan exceeds the rated power range, the boundary value of the rated power range is taken.

[0079] In summary, the method provided in this embodiment can at least achieve the following effects:

[0080] 1) The present invention monitors the dust concentration in real time and dynamically adjusts the fan power according to the dust concentration and the position relationship between the sampling point and the fan. In areas or time periods with low dust concentration, the fan power is reduced to reduce energy consumption; in areas or time periods with high dust concentration, the fan power is increased to ensure good ventilation effect. This on-demand adjustment method can effectively reduce the energy consumption of the fan and achieve energy-saving goals.

[0081] 2) The present invention calculates the initial power of the fan by using a distance-weighted algorithm, taking into account factors such as the distance between the sampling point and the fan and the dust concentration. A reasonable weight is assigned to each sampling point according to the distance and dust concentration, accurately reflecting the actual demand for fan power in different areas, avoiding energy waste or insufficient ventilation due to uniform fixed power operation, and deriving the actual initial power in combination with the power conversion coefficient. The fan power can be flexibly adapted according to the dust distribution in the workshop, thereby improving energy utilization efficiency, ensuring good ventilation in the workshop, and facilitating smooth production and workers' health.

[0082] See also Figure 2 In one embodiment, an intelligent energy-saving fan operation control system for the papermaking industry is also provided, the system comprising:

[0083] The sampling point and light source setting module 100 is used to preset multiple sampling points in the workshop, and set a reference light source on the opposite side of the sampling point, and the light source emits polarized light of preset intensity to penetrate the dust area;

[0084] The environmental image data acquisition and analysis module 200 is used to acquire environmental image data of preset sampling points in the workshop; and analyze dust concentration data of the sampling points according to the environmental image data;

[0085] The fan initial power calculation module 300 is used to calculate the initial power of the corresponding fan through a distance weighted algorithm according to the position relationship between each sampling point and the corresponding fan and the dust concentration index;

[0086] The real-time monitoring and dynamic adjustment module 400 is used to monitor the dust concentration change rate of each area in real time. When it is detected that the dust concentration change rate of the target area reaches a critical value, the PID algorithm is used to dynamically adjust the power of each fan.

[0087] Preferably, the environmental image data acquisition and analysis module 200 is also used for:

[0088] Analyze the average brightness value of environmental image data;

[0089] The average brightness value is input into the preset dust concentration prediction model to obtain the dust concentration data of the corresponding sampling point;

[0090] The dust concentration prediction model is expressed as:

[0091]

[0092] In the formula, C represents the dust concentration data, I represents the average brightness value of the environmental image data, and I 0 The brightness value of the reference image in the absence of dust, d represents the distance between the reference light source and the sampling point, and k represents the scattering coefficient of the dust.

[0093] Preferably, the calculation by the distance weighted algorithm is expressed as follows:

[0094]

[0095] P init =K·∑W i ·C i ,

[0096] In the formula, C i Represents the dust concentration data of the sampling point, d i Indicates the distance between the sampling point and the fan, α indicates the attenuation coefficient, the default value is α=0.5, W i Represents the weight coefficient of the sampling point, P init represents the initial power, and K represents the power conversion factor.

[0097] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0098] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0099] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0100] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0101] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

Claims

1. An intelligent energy-saving fan operation control method for papermaking industry, characterized in that: The method comprises: A plurality of sampling points are preset in the workshop, and a reference light source is set on the opposite side of the sampling points, wherein the light source emits polarized light of preset intensity to penetrate the dust area; Collect environmental image data of preset sampling points in the workshop; analyze dust concentration data of sampling points based on environmental image data; According to the positional relationship between each sampling point and the corresponding fan and the dust concentration index, the initial power of the corresponding fan is calculated by the distance weighted algorithm; The dust concentration change rate in each area is monitored in real time. When it is detected that the dust concentration change rate in the target area reaches the critical value, the PID algorithm is used to dynamically adjust the power of each fan.

2. The intelligent energy-saving fan operation control method for the papermaking industry according to claim 1 is characterized in that: Analyzing dust concentration data at sampling points according to environmental image data includes: Analyze the average brightness value of environmental image data; The average brightness value is input into the preset dust concentration prediction model to obtain the dust concentration data of the corresponding sampling point; The dust concentration prediction model is expressed as: Where C represents the dust concentration data, I represents the average brightness value of the environmental image data, I0 represents the reference image brightness value in the dust-free condition, d represents the distance between the reference light source and the sampling point, and k represents the scattering coefficient of the dust.

3. The intelligent energy-saving fan operation control method for the papermaking industry according to claim 1 is characterized in that: The calculation by the distance weighted algorithm is expressed as follows: P init =K·∑W i ·C i , In the formula, C i Represents the dust concentration data of the sampling point, d i Indicates the distance between the sampling point and the fan, α indicates the attenuation coefficient, the default value is α=0.5, W i Represents the weight coefficient of the sampling point, P init represents the initial power, and K represents the power conversion factor.

4. The intelligent energy-saving fan operation control method for the papermaking industry according to claim 1 is characterized in that: The method further comprises: If the calculated initial power of the fan exceeds the rated power range, the boundary value of the rated power range is taken.

5. An intelligent energy-saving fan operation control system for the papermaking industry, characterized in that: The system comprises: A sampling point and light source setting module is used to preset multiple sampling points in the workshop and set a reference light source on the opposite side of the sampling point, wherein the light source emits polarized light of preset intensity to penetrate the dust area; The environmental image data acquisition and analysis module is used to collect environmental image data of preset sampling points in the workshop; based on the environmental image data, the dust concentration data of the sampling points is analyzed; The fan initial power calculation module is used to calculate the initial power of the corresponding fan through a distance weighted algorithm according to the position relationship between each sampling point and the corresponding fan and the dust concentration index; The real-time monitoring and dynamic adjustment module is used to monitor the dust concentration change rate in each area in real time. When it is detected that the dust concentration change rate in the target area reaches a critical value, the PID algorithm is used to dynamically adjust the power of each fan.

6. The intelligent energy-saving fan operation control system for the papermaking industry according to claim 5 is characterized in that: The environmental image data acquisition and analysis module is also used for: Analyze the average brightness value of environmental image data; The average brightness value is input into the preset dust concentration prediction model to obtain the dust concentration data of the corresponding sampling point; The dust concentration prediction model is expressed as: Where C represents the dust concentration data, I represents the average brightness value of the environmental image data, I0 represents the reference image brightness value in the dust-free condition, d represents the distance between the reference light source and the sampling point, and k represents the scattering coefficient of the dust.

7. The intelligent energy-saving fan operation control system for the papermaking industry according to claim 5 is characterized in that: The calculation by the distance weighted algorithm is expressed as follows: P init =K·∑W i ·C i , In the formula, C i Represents the dust concentration data of the sampling point, d i Indicates the distance between the sampling point and the fan, α indicates the attenuation coefficient, the default value is α=0.5, W i Represents the weight coefficient of the sampling point, P init represents the initial power, and K represents the power conversion factor.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and when the processor executes the computer instructions, the electronic device executes the intelligent energy-saving fan operation control method for the papermaking industry as described in any one of claims 1 to 4.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the intelligent energy-saving fan operation control method for the papermaking industry as described in any one of claims 1 to 4.

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