Energy-saving control system of integrated digital fan

Abstract

The invention belongs to the technical field of fan energy-saving control, and particularly relates to an energy-saving control system of an integrated digital fan, which dynamically separates and quantifies a basic load and an additional load in fan operation, intercepts historical load data of a preorder time window, analyzes deviation characteristics of the basic load and a process target and fluctuation rule characteristics of the additional load, and obtains an energy-saving control result of the integrated digital fan. The limitation of load mixing processing and the hysteresis of real-time monitoring in the prior art are broken through, then the working frequency of the frequency converter is dynamically adjusted, the energy efficiency optimization precision is highly improved, meanwhile, fan operation energy efficiency ratio data in the adjustment process are collected in real time, and the energy efficiency optimization precision is improved. And a frequency conversion regulation and control optimization instruction is generated based on the energy efficiency ratio and is fed back to the frequency conversion regulation and control module to form a self-adaptive optimization loop, so that the real-time energy efficiency requirement can be accurately matched, intelligent calibration and dynamic adjustment and optimization of the working frequency of the fan are realized, and the self-adaptive capability of the system to multiple working conditions is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy-saving control of fans, and specifically relates to an energy-saving control system for an integrated digital fan. Background Art

[0002] The energy-saving control of fans has a direct and crucial relationship with the precise execution of fan frequency converters, and it is also a necessary condition for achieving energy conservation. Through dynamic speed regulation, precise control, and multi-objective optimization of the frequency converter, the energy consumption of the fan is transformed from "extensive" to "demand-based supply", which has significant economic benefits and environmental protection value in industrial, construction, and other fields.

[0003] In the prior art, there are also some solutions related to the energy-saving control of fan frequency converters. For example, a fan frequency conversion energy-saving control system and control method in a ventilation and air-conditioning system with the Chinese patent publication number CN117249570A, which finds the branch pipe at the air outlet that is farthest from the fan and in the working state, obtains the total pressure value at the installation position of the air volume regulating valve on it, screens the branch pipe with the smallest total pressure value among them as the most unfavorable branch pipe, controls the fan frequency converter to change the fan speed, and adjusts the total pressure value at the installation position of the air volume regulating valve on the most unfavorable branch pipe to be smaller and meet the minimum working total pressure value of the air volume regulating valve on it, which not only does not affect the normal operation of the air volume regulating valve but also reduces the energy consumption of the fan, thus achieving the purpose of energy conservation.

[0004] Another Chinese patent with the publication number CN119146536A discloses an adaptive fan variable-frequency air volume control method. It predicts the required air volume according to the actual working conditions of each area and detects the actual air volume, calculates the total required air volume, and obtains the initial frequency of the fan in combination with the fan characteristic curve. Then, according to the difference between the total required air volume and the total measured air volume and the opening degree of the end damper, the initial frequency of the fan is corrected according to the fan frequency correction rule to achieve the adaptive control of the fan frequency, taking into account the air volume requirements of the most unfavorable end while achieving the energy-saving effect.

[0005] Although the above solutions propose some methods for the energy-saving control of fan frequency converters, the prior art still has the following limitations: 1. The prior art relies on the immediate or delayed regulation mechanism of real-time monitoring values in the regulation of the frequency converter, lacking the forward-looking prediction ability of the changing trend of the fan load, resulting in the regulation action always lagging behind the actual working condition changes, unable to adjust the parameters of the frequency converter in advance before the load fluctuation occurs, and also making the energy conservation lag.

[0006] 2. The prior art does not analyze the composition characteristics of the fan load, treats the basic load and the additional load together, and ignores the differences in dynamic response and energy consumption ratio between the two, resulting in insufficient control accuracy. In addition, the prior art has a single regulation strategy for the frequency converter, relying only on the total air volume data, which is likely to limit the energy efficiency optimization and there is a risk of exceeding the working conditions. Summary of the Invention

[0007] To overcome the drawbacks in the background art, the embodiments of the present invention provide an energy-saving control system for an integrated digital fan, which can effectively solve the problems involved in the above-mentioned background art.

[0008] The object of the present invention can be achieved through the following technical solutions: an energy-saving control system for an integrated digital fan, including: a load quantification module, a load prediction module, a frequency conversion regulation module, and a dynamic optimization module.

[0009] The load quantification module is connected to the load prediction module, the load prediction module is connected to the frequency conversion regulation module, and the frequency conversion regulation module is connected to the dynamic optimization module.

[0010] The load quantification module dynamically separates and quantifies the basic load and the additional load during the operation of the fan. The basic load is determined based on the real-time monitoring data of the air volume and the characteristics of the pipe network resistance, and the additional load is calculated and generated in real time based on the dynamic pressure difference between the inlet and outlet, the amplitude of the flow oscillation, and the change in the medium density.

[0011] The load prediction module uses the quantification results of the basic load and the additional load as inputs, intercepts the historical load data of the previous time window including the current moment, analyzes the deviation characteristics between the basic load and the process target and the fluctuation law characteristics of the additional load, and combines the process requirements of the subsequent time window to predict the load change trend.

[0012] The frequency conversion regulation module dynamically adjusts the working frequency of the frequency converter according to the load change trend output by the load prediction module.

[0013] The dynamic optimization module real-time collects the fan operation energy efficiency ratio data during the execution of the frequency conversion regulation module, generates a frequency conversion regulation optimization instruction based on the energy efficiency ratio comparison result, and feeds the optimization instruction back to the frequency conversion regulation module to form an adaptive optimization loop.

[0014] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) By intercepting the historical load data of the previous time window, analyzing the deviation characteristics between the basic load and the process target and the fluctuation law characteristics of the additional load, the present invention breaks through the lag of real-time monitoring, combines the subsequent process requirements to achieve a forward-looking prediction of the load change trend, and ensures that the control action precedes the actual working condition change.

[0015] (2) By dynamically separating and quantifying the basic load and the additional load during the operation of the fan, the present invention breaks through the limitations of the prior art in mixed processing, and generates a reference frequency based on the predicted basic load during the subsequent dynamic adjustment of the working frequency of the frequency converter, and forms a reasonable working frequency range by combining the normalized correction of the predicted additional load, highlighting the targeted differences of different load characteristics, and effectively improving the accuracy of energy efficiency optimization.

[0016] (3) The present invention generates a voltage stabilization and frequency limit control and frequency optimization dual-mode control instruction through energy efficiency comparison, constructs a dynamic adaptive frequency conversion optimization mechanism, and feeds it back to the frequency conversion control module for continuous self-learning optimization. It can not only accurately match the real-time energy efficiency requirements, realize intelligent calibration and dynamic optimization of the fan operating frequency, but also significantly improve the system's adaptive ability to multiple working conditions. Description of the Drawings

[0017] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0018] Figure 1 It is a schematic diagram of module connection provided by the first embodiment of the present invention.

[0019] Figure 2 It is a specific logic schematic diagram for generating a frequency conversion control optimization instruction based on the energy efficiency comparison result in the first embodiment of the present invention.

[0020] Figure 3 It is a schematic structural diagram of a device provided by the second embodiment of the present invention. Detailed Embodiments

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.

[0022] Embodiment 1

[0023] Referring to Figure 1 As shown, the first embodiment of the present invention provides an energy-saving control system for an integrated digital fan, including: a load quantification module, a load prediction module, a frequency conversion control module, and a dynamic optimization module.

[0024] The load quantification module is connected to the load prediction module, the load prediction module is connected to the frequency conversion control module, and the frequency conversion control module is connected to the dynamic optimization module.

[0025] The load quantification module dynamically separates and quantifies the basic load and additional load during the operation of the fan. The basic load is determined based on the real-time monitoring data of the air volume and the pipe network resistance characteristics, and the additional load is generated by real-time calculation based on the dynamic pressure difference between the inlet and outlet, the flow oscillation amplitude, and the change in the medium density.

[0026] It should be noted that the basic load is the inherent load that the fan must bear to meet the air volume and pressure requirements of the process system design, and is directly related to the core parameters set by the process.

[0027] The additional load is the dynamic load that the fan bears additionally due to the actual operating environment deviating from the ideal working conditions (such as the pressure difference between the inlet and outlet, flow rate fluctuations, and medium property differences).

[0028] In addition, both the above-mentioned flow rate and air volume refer to the volume of gas passing through the fan per unit time.

[0029] In a preferred embodiment of the present invention, the basic load is as follows in the quantification process: The first air volume data at the inlet of the fan and the second air volume data at the outlet of the fan are collected in real time through the built-in air volume sensor of the fan, and the first and second air volume data are arithmetically averaged to obtain the real-time effective air volume of the fan.

[0030] The ratio of the real-time effective air volume of the fan to the pre-stored equivalent cross-sectional area of the fan flow channel is used as the real-time effective flow velocity inside the fan.

[0031] The real-time effective flow velocity inside the fan is substituted into the pre-set pipe network resistance formula to obtain the pipe resistance overcome by the fan based on the real-time effective flow velocity inside.

[0032] It should be noted that the above-mentioned pre-set pipe network resistance formula is specifically the Darcy-Weisbach formula, and its input parameters also include fluid density, pipe friction coefficient, and equivalent length-diameter ratio, which all come from the pre-set data of the engineer during the fan pipe network design stage.

[0033] Taking the product of the real-time effective air volume of the fan and the pipe resistance overcome by its synchronous effective flow velocity as the numerator, and taking the pre-stored design efficiency of the fan as the denominator to perform a ratio operation, and taking the result of the ratio operation as the quantified value of the basic load.

[0034] It should be noted that the above-mentioned pre-stored equivalent cross-sectional area of the fan flow channel is set based on the flow channel geometric topology structure specification corresponding to the fan model, or is dynamically updated through a laser scanning calibration experiment, and the pre-stored design efficiency of the fan is specifically extracted with reference to the fan factory performance manual guide specification.

[0035] In a preferred embodiment of the present invention, the additional load is as follows in the quantification process: The time-domain signal of the pressure difference between the inlet and outlet is collected in real time, and the root mean square value of the time-domain signal within the analysis sliding window is used to obtain the dynamic pressure difference fluctuation intensity between the inlet and outlet of the fan.

[0036] The signal of the built-in air volume sensor of the fan is band-pass filtered, and the oscillation amplitude within the pre-calibrated frequency band of the fan process characteristics is used as the flow rate oscillation amplitude of the fan.

[0037] By synthesizing the dynamic pressure difference fluctuation intensity at the inlet and outlet of the fan and the flow rate oscillation amplitude, a preset flow resistance coefficient is introduced to obtain the transient flow resistance component.

[0038] The change in the medium density is obtained in real time through a gas density sensor built into the fan. The change is the difference between the current density value and the density value under the reference working condition. Combining with the real-time effective air volume of the fan, a preset inertia correction coefficient is introduced to obtain the medium inertia component.

[0039] It should be noted that the above transient flow resistance component is composed of the cumulative sum of the dynamic pressure difference fluctuation intensity at the inlet and outlet of the fan, the flow rate oscillation amplitude, and the preset flow resistance coefficient. The medium inertia component is the product of the change in the medium density and the preset inertia correction coefficient. Among them, both the preset flow resistance coefficient and the preset inertia correction coefficient are obtained through experimental calibration: the former is obtained by changing the fan speed or load under the reference working condition (stable flow rate, constant density), measuring the corresponding relationship between the dynamic pressure difference fluctuation intensity, the flow rate oscillation amplitude, and the actual flow resistance, and fitting; the latter is obtained by measuring the relationship between the change in density and the load response and performing regression analysis in an experimental environment with controllable density (such as filling gases with different densities). And the experimental calibration processes of both are executed by professionals at the initial stage of system development and pre-stored in the cloud database for direct extraction and use.

[0040] The transient flow resistance component and the medium inertia component are superimposed to generate an additional load.

[0041] The load prediction module takes the quantization results of the basic load and the additional load as inputs, intercepts the historical load data of the previous time window including the current moment, analyzes the deviation characteristics between the basic load and the process target and the fluctuation law characteristics of the additional load, and predicts the load change trend in combination with the process requirements of the subsequent time window.

[0042] In a preferred embodiment of the present invention, the deviation characteristics between the basic load and the process target are as follows: Read the process requirement parameters at each sampling moment in the previous time window from the fan process control log, and quantify the basic load corresponding to the process target at each sampling moment in the previous time window based on the set air volume and the associated pipeline flow velocity requirements in the process requirement parameters.

[0043] It should be noted that the process requirement parameters include but are not limited to the set air volume, the associated pipeline flow velocity requirements, the set wind pressure, the valve opening, and the medium temperature.

[0044] The analysis logic of the basic load corresponding to each sampling moment in the previous time window and the subsequent time window is the same as the basic load quantization logic in the above load quantization module. The calculation process is as follows: Substitute the numerical value of the associated pipeline flow velocity requirement in the process requirement parameters into the preset pipeline network resistance formula to obtain the theoretical pipeline resistance overcome by the fan based on the pipeline flow velocity requirement.

[0045] Taking the product of the set air volume in the process requirement parameters and the theoretical pipeline resistance overcome under the synchronous pipeline flow velocity requirement as the numerator, and the pre-stored fan design efficiency as the denominator to expand the ratio operation, and taking the result of the ratio operation as the basic load of the corresponding process target.

[0046] Subtracting the actual basic load at each sampling moment in the previous time window from the process target basic load to generate a time series deviation sequence.

[0047] Determining the deviation direction feature by linearly regressing the slope of the time series deviation sequence, and taking the ratio of the cumulative absolute deviation of the time series deviation sequence to the length of the previous time window as the deviation intensity feature.

[0048] The deviation feature between the basic load and the process target in the previous time window is composed of the product of the deviation direction feature and the deviation intensity feature.

[0049] In a preferred embodiment of the present invention, the process for determining the deviation direction feature by the slope of the time series deviation sequence is as follows: if the slope of the time series deviation sequence is a non-negative number, the deviation direction feature value is determined to be 1, and conversely, the deviation direction feature value is determined to be -1.

[0050] In a preferred embodiment of the present invention, the fluctuation law feature of the additional load is as follows: taking whether the relative change rate of the process requirement parameters at adjacent sampling moments exceeds a preset threshold as the judgment basis, the previous time window is divided into a parameter change interval and a parameter steady state interval.

[0051] It should be noted that the relative change rate of the process requirement parameters at the above-mentioned adjacent sampling moments (a certain sampling moment and its previous sampling moment) specifically refers to the maximum relative change rate of the variables in the process requirement parameters at adjacent sampling moments. The parameter change interval specifically includes each sampling moment in the previous time window whose relative change rate of the process requirement parameters with respect to its previous sampling moment exceeds the preset threshold, and the parameter steady state interval specifically includes each sampling moment in the previous time window whose relative change rate of the process requirement parameters with respect to its previous sampling moment does not exceed the preset threshold.

[0052] Establishing a dynamic mapping relationship between the change amount of the process requirement parameters and the change amount of the additional load for the parameter change interval, and generating the additional load change rate feature by iteratively updating the historical calibration correlation coefficient between the pre-stored process requirement parameters and the additional load.

[0053] It should be noted that the historical calibration correlation coefficient between the above-mentioned pre-stored process requirement parameters and the additional load is based on historical operation data (such as fan control logs, sensor records, etc.). Through statistical regression analysis, the quantitative correlation coefficients between various variables (such as set air volume, pipeline flow rate, valve opening, etc.) within the process requirement parameters and the additional load are calculated to reflect the influence weight of the unit change of a certain parameter variable on the additional load.

[0054] The specific process of iterative updating of the historical calibration correlation coefficient between the pre-stored process requirement parameters and the additional load is as follows: Retrieve the process requirement parameter variables whose relative change rate at the sampling moment within the parameter change interval exceeds the preset threshold, and mark them as significantly changing variables. Associate the significantly changing variables with the change amount of the additional load at their corresponding sampling moments (which can be recorded in coordinate form), integrate the association sequence set of each significantly changing variable and the additional load variable within the parameter change interval, and based on the data in the association sequence set, use the Pearson correlation coefficient or grey correlation degree calculation method to obtain the real-time correlation coefficient between the change value of the significantly changing variable and the change amount of the additional load at its corresponding sampling moment. Compare it with the historical calibration correlation coefficient between the significantly changing variable and the additional load, and use a weighted fusion method (requiring that the preset weight of the historical calibration correlation coefficient is greater than the preset weight of the real-time correlation coefficient, which can be exemplified as 0.7 and 0.3) to obtain the iterative update value of the historical calibration correlation coefficient between the significantly changing variable and the additional load. Special note: The relative change rate of the process requirement parameters at each sampling moment within the parameter change interval and its previous sampling moment all exceed the preset threshold, and the number of significantly changing variables is at least 1.

[0055] Calculate the standard deviation of the actual additional load at each sampling moment for the parameter steady state interval to generate the additional load volatility feature.

[0056] It should be noted that the calculation method of the above standard deviation is as follows: Construct a sequence containing the actual additional load at each sampling moment within the parameter steady state interval, and use the existing standard deviation formula for calculation. Since the existing standard deviation formula is a mature statistical tool and has formed a recognized standard expression, it is directly used here, so the specific details are not elaborated.

[0057] Fuse the additional load change rate feature and the volatility feature to form the additional load fluctuation law feature of the previous time window.

[0058] In a preferred embodiment of the present invention, the load change trend is as follows in the prediction process: Based on the set air volume and the associated pipeline flow rate requirements in the process requirement parameters at each sampling moment within the subsequent time window, calculate the basic load of the corresponding process target, and calibrate it by superimposing the basic load deviation feature of the previous time window to obtain the predicted reference load at each sampling moment within the subsequent time window.

[0059] It should be noted that the calibration by superimposing the base load deviation characteristics of the previous time window specifically refers to accumulating the base load corresponding to the process target at each sampling moment within the subsequent time window and the base load deviation characteristics.

[0060] Compare the process demand parameters at the current moment with those at each sampling moment in the subsequent time window. When the parameters are consistent, use the median of the volatility confidence interval of the current additional load as the predicted additional load. When the parameters are inconsistent, dynamically correct the current additional load based on the process parameter deviation value and the correlation coefficient to obtain the predicted additional load.

[0061] Sort out the predicted base load and predicted additional load at each sampling moment in the subsequent time window to generate the prediction result of the load change trend.

[0062] In the embodiment of the present invention, by intercepting the historical load data of the previous time window, analyzing the deviation characteristics between the base load and the process target and the fluctuation law characteristics of the additional load, breaking through the lag of real-time monitoring, and combining with the subsequent process requirements to achieve the forward prediction of the load change trend, it ensures that the control action is prior to the actual working condition change.

[0063] The variable frequency control module dynamically adjusts the working frequency of the frequency converter according to the load change trend output by the load prediction module.

[0064] In a preferred embodiment of the present invention, the working frequency of the frequency converter refers to the following dynamic adjustment process: according to the predicted base load at each sampling moment in the subsequent time window, match the preset load-frequency mapping curve to generate the reference working frequency.

[0065] Extract the predicted additional load at each sampling moment in the subsequent time window, normalize it respectively with the preset minimum limit value and maximum limit value of the additional load to obtain the minimum load correction ratio and the maximum load correction ratio. Introduce the frequency sensitivity factor and multiply it with the two load correction ratios to obtain the minimum and maximum values of the working frequency correction, and superimpose them on the reference working frequency to form a reasonable working frequency range.

[0066] It should be noted that the above frequency sensitivity factor is a dimensionless or proportional coefficient with a specific physical unit, used to quantify the sensitivity of the additional load change to the adjustment demand of the fan working frequency. Its core function is to convert the normalized load correction ratios (minimum and maximum values) into corresponding working frequency correction amounts, ensuring that the frequency adjustment and the load fluctuation are consistent in physical logic and system response characteristics. It is possible to artificially apply a known additional load step change under stable working conditions and record the corresponding working frequency response. Through linear regression fitting of multiple groups of experimental data, the slope of the additional load step change and the working frequency response is used as the frequency sensitivity factor.

[0067] Integrate the reasonable working frequency ranges at each sampling moment in the subsequent time window, perform frequency distribution statistics on the ranges, select the continuous sub-range with the highest overlapping frequency as the effective adjustment range, and use the lower limit value of the effective adjustment range as the working frequency of the frequency converter.

[0068] In the embodiment of the present invention, by dynamically separating and quantifying the basic load and additional load during the operation of the fan, the limitation of the prior art's mixed processing is broken through. During the subsequent dynamic adjustment of the working frequency of the frequency converter, a reference frequency is generated based on the predicted basic load, and a reasonable working frequency range is formed by combining the normalized correction of the predicted additional load, highlighting the targeted differences in different load characteristics and effectively improving the accuracy of energy efficiency optimization.

[0069] The dynamic optimization module collects the fan operation energy efficiency ratio data during the execution of the frequency conversion control module in real time, generates a frequency conversion control optimization instruction based on the energy efficiency ratio comparison result, and feeds the optimization instruction back to the frequency conversion control module to form an adaptive optimization loop.

[0070] Refer to Figure 2 As shown, in a preferred embodiment of the present invention, the process of generating a frequency conversion control optimization instruction based on the energy efficiency ratio comparison result is as follows: If the fan operation energy efficiency ratio at the sampling moment in the subsequent time window exceeds or reaches the energy efficiency ratio threshold of the energy-saving standard, a frequency optimization instruction is generated. The working frequency is successively increased at a unit gradient step within the effective adjustment range, and the fan operation energy efficiency ratios before and after the increase are compared. If the comparison result is greater, the increased working frequency is retained to approach the optimal working frequency.

[0071] If the fan operation energy efficiency ratio at the sampling moment in the subsequent time window continuously remains lower than the energy efficiency ratio threshold of the energy-saving standard for a preset duration, a voltage stabilization and frequency limiting control instruction is generated, and the standby parameter group is switched to execute voltage stabilization and frequency limiting control. The standby parameter group includes a safety curve for presetting the amplitude of frequency fluctuation limitation, a voltage fluctuation suppression coefficient, and a forced power upper limit value.

[0072] In a preferred embodiment of the present invention, the fan operation energy efficiency ratio is the ratio of the effective output power of the fan to the input electric power.

[0073] It should be noted that the above-mentioned effective output power of the fan refers to the mechanical power actually transmitted by the fan to the fluid, that is, the useful work power used to overcome the system resistance (such as pipeline friction, static pressure difference, etc.) and drive the gas flow. Its calculated value is the same as the specific quantization value of the basic load during the operation of the fan in the above-mentioned load quantization module. The input electric power refers to the electric energy consumed by the fan from the power grid, including the sum of motor losses, transmission losses, and the mechanical power of the fan. The reason why the working frequency of the frequency converter affects the fan operation energy efficiency ratio to show energy saving is that by adjusting the frequency conversion to match the load demand, the fan operates in the high-efficiency area, helping to maximize the fan operation energy efficiency ratio.

[0074] In the embodiment of the present invention, a voltage stabilization and frequency limit control and frequency optimization dual-mode regulation instruction is generated through energy efficiency comparison, a dynamic adaptive frequency conversion optimization mechanism is constructed, and it is fed back to the frequency conversion regulation module for continuous self-learning and optimization. It can not only accurately match the real-time energy efficiency requirements, realize intelligent calibration and dynamic optimization of the working frequency of the fan, but also significantly improve the adaptive ability of the system to multiple working conditions.

[0075] Embodiment 2

[0076] Referring to Figure 3 As shown in the figure, in the second embodiment of the present invention, a device is provided, including: a processor, a memory, and a communication bus: a computer-readable program executable by the processor is stored on the memory. The communication bus realizes the connection and communication between the processor and the memory. When the processor executes the computer-readable program, the energy-saving control system of the integrated digital fan can be realized.

[0077] Specifically, the above-mentioned memory and processor can be general memory and processor, and no specific limitation is made here. When the processor runs the computer-readable program stored in the memory, it can execute the relevant steps of the above system.

[0078] The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above system can be completed by the integrated logic circuit in the hardware of the processor or the instructions in the form of software. The above-mentioned processor can be a general processor, including a central processor, a network processor, etc. It can also be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The method steps related to the system disclosed in the embodiments of the present application in combination can be directly embodied as being executed by the hardware decoding processor, or executed by the combination of the hardware and software modules in the decoding processor. The software module can be located in the random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register and other mature storage media in the art. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above system.

[0079] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. Energy-saving control system for an integrated digital fan, characterized in that The system includes: A load quantization module that dynamically separates and quantizes the basic load and additional load during the operation of the fan. The basic load is determined based on the real-time monitoring data of the air volume and the characteristics of the pipe network resistance. The additional load is generated by real-time calculation based on the dynamic pressure difference between the inlet and outlet, the amplitude of the flow oscillation, and the change in the medium density; A load prediction module that takes the quantization results of the basic load and additional load as inputs, intercepts the historical load data of the previous time window including the current moment, analyzes the deviation characteristics between the basic load and the process target and the fluctuation law characteristics of the additional load, and predicts the load change trend in combination with the process requirements of the subsequent time window; A frequency conversion regulation module that dynamically adjusts the operating frequency of the frequency converter according to the load change trend output by the load prediction module; A dynamic optimization module that real-time collects the fan operation energy efficiency ratio data during the execution of the frequency conversion regulation module, generates a frequency conversion regulation optimization instruction based on the energy efficiency ratio comparison result, and feeds the optimization instruction back to the frequency conversion regulation module to form an adaptive optimization loop.

2. The energy-saving control system of the integrated digital fan according to claim 1, characterized in that: The basic load refers to the following quantization process: The first air volume data at the inlet of the fan and the second air volume data at the outlet of the fan are real-time collected through the built-in air volume sensor of the fan, and the arithmetic mean processing is performed on the first and second air volume data to obtain the real-time effective air volume of the fan; The ratio of the real-time effective air volume of the fan to the pre-stored equivalent cross-sectional area of the fan flow channel is used as the real-time effective flow velocity inside the fan; The real-time effective flow velocity inside the fan is substituted into the pre-set pipe network resistance formula to obtain the pipe resistance overcome by the fan based on the real-time effective flow velocity inside; Taking the product of the real-time effective air volume of the fan and the pipe resistance overcome by its synchronous effective flow velocity as the numerator, and the pre-stored design efficiency of the fan as the denominator to expand the ratio operation, and taking the ratio operation result as the basic load quantization value.

3. The energy-saving control system of the integrated digital fan according to claim 1, wherein: The additional load refers to the following quantization process: The time-domain signal of the pressure difference between the inlet and outlet is real-time collected, and the root mean square value of the time-domain signal within the sliding window is analyzed to obtain the dynamic pressure difference fluctuation intensity between the inlet and outlet of the fan; The signal of the built-in air volume sensor of the fan is subjected to band filtering, and the oscillation amplitude within the pre-calibrated frequency band of the fan process characteristics is used as the flow oscillation amplitude of the fan; Combining the dynamic pressure difference fluctuation intensity between the inlet and outlet of the fan and the flow oscillation amplitude, a preset flow resistance coefficient is introduced to obtain the transient flow resistance component; The change in the medium density is real-time obtained through the built-in gas density sensor of the fan. The change is the difference between the current density value and the reference working condition density value. Combining the real-time effective air volume of the fan, a preset inertia correction coefficient is introduced to obtain the medium inertia component; The transient flow resistance component and the medium inertia component are superimposed to generate the additional load.

4. The energy-saving control system of the integrated digital fan according to claim 1, characterized in that: The deviation characteristics between the basic load and the process target refer to the following analysis process: The process requirement parameters at each sampling moment within the previous time window are read from the fan process control log. Based on the set air volume and the associated pipe flow velocity requirements in the process requirement parameters, the basic load corresponding to the process target at each sampling moment within the previous time window is quantified; The difference is taken between the actual basic load at each sampling moment within the previous time window and the basic load of the process target to generate a time series deviation sequence; The slope of the time series deviation sequence is analyzed by linear regression to determine the deviation direction feature, and the ratio of the cumulative absolute deviation of the time series deviation sequence to the length of the previous time window is used as the deviation intensity feature; The deviation feature between the base load and the process target within the previous time window is constituted by the product of the deviation direction feature and the deviation intensity feature.

5. The energy-saving control system of the integrated digital fan according to claim 4, characterized in that: The process of determining the deviation direction feature based on the slope of the time series deviation sequence is as follows: If the slope of the time series deviation sequence is non - negative, the deviation direction feature value is determined to be 1, otherwise the deviation direction feature value is determined to be - 1.

6. The energy-saving control system of the integrated digital fan according to claim 1, wherein: The fluctuation law feature of the additional load is as follows: Taking whether the relative change rate of the process demand parameters at adjacent sampling times exceeds a preset threshold as the judgment criterion, the previous time window is divided into a parameter change interval and a parameter steady - state interval; For the parameter change interval, a dynamic mapping relationship between the change amount of the process demand parameters and the change amount of the additional load is established, and the additional load change rate feature is generated by iteratively updating the historical calibration correlation coefficient between the pre - stored process demand parameters and the additional load; For the parameter steady - state interval, the standard deviation of the actual additional load at each sampling time is calculated to generate the additional load volatility feature; The additional load change rate feature and the volatility feature are fused to constitute the additional load fluctuation law feature of the previous time window.

7. The energy-saving control system of the integrated digital fan according to claim 6, characterized in that: The load change trend is as follows: Based on the set air volume and the associated pipeline flow velocity requirements in the process demand parameters at each sampling time within the subsequent time window, the base load corresponding to the process target is calculated, and it is calibrated by superimposing the base load deviation feature of the previous time window to obtain the predicted reference load at each sampling time within the subsequent time window; Compare the process demand parameters at the current moment with those at each sampling time within the subsequent time window. When the parameters are the same, the median value of the volatility confidence interval of the current additional load is used as the predicted additional load. When the parameters are different, the current additional load is dynamically corrected based on the process parameter deviation value and the correlation coefficient to obtain the predicted additional load; Arrange the predicted base load and the predicted additional load at each sampling time within the subsequent time window to generate the load change trend prediction result.

8. The energy-saving control system of the integrated digital fan according to claim 7, characterized in that: The operating frequency of the frequency converter is as follows: According to the predicted base load at each sampling time within the subsequent time window, the preset load - frequency mapping curve is matched to generate the reference operating frequency; Extract the predicted additional load at each sampling time within the subsequent time window, and normalize it with the preset minimum and maximum limits of the additional load respectively to obtain the minimum load correction ratio and the maximum load correction ratio. Introduce a frequency - sensitive factor and multiply it with the two load correction ratios to obtain the minimum and maximum values of the operating frequency correction, and superimpose them on the reference operating frequency to form a reasonable operating frequency range; Integrate the reasonable operating frequency range at each sampling time within the subsequent time window and conduct a frequency distribution statistics on the interval. Select the continuous sub - interval with the highest overlapping frequency as the effective adjustment range, and use the lower limit value of the effective adjustment range as the operating frequency of the frequency converter.

9. The energy-saving control system of the integrated digital fan according to claim 8, characterized in that: The generation of the variable-frequency regulation optimization instruction based on the energy efficiency comparison result is as follows: If the energy efficiency ratio of the fan operation at the sampling moment in the subsequent time window exceeds or reaches the energy efficiency ratio threshold of the energy-saving standard, a frequency optimization instruction is generated, and the operating frequency is successively increased by a unit gradient step within the effective adjustment range, and the energy efficiency ratios of the fan operation before and after the increase are compared. If the comparison result is greater, the increased operating frequency is retained to approach the optimal operating frequency; If the energy efficiency ratio of the fan operation at the sampling moment in the subsequent time window continuously remains lower than the energy efficiency ratio threshold of the energy-saving standard for a preset duration, a voltage stabilization and frequency limitation control instruction is generated, and the voltage stabilization and frequency limitation control is switched to the standby parameter group. The standby parameter group includes a safety curve for presetting the fluctuation range of the limited frequency, a voltage fluctuation suppression coefficient, and a forced power upper limit value.

10. The energy-saving control system of the integrated digital fan according to claim 9, characterized in that: The energy efficiency ratio of the fan operation is the ratio of the effective output power of the fan to the input electric power.

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