An intelligent monitoring system and method for coal bioconversion production

Abstract

The present invention relates to the field of industrial automation control, and particularly to an intelligent monitoring system and method for coal bioconversion production. The content includes: collecting parameters in real time and performing standardized processing, and based on the standardized parameters, calculating the topological fluctuation entropy through a topological fluctuation entropy calculation algorithm; constructing an adaptive resonance energy flow model based on the standardized parameters and the topological fluctuation entropy, and calculating the adaptive resonance energy flow; introducing a dual-threshold monitoring mechanism, and performing adaptive regulation on coal bioconversion production based on the topological fluctuation entropy, the adaptive resonance energy flow, and the standardized parameters. It solves the problems that traditional production monitoring methods usually rely on the analysis of single parameters, such as temperature, humidity, etc., and are prone to ignoring the coupling relationship and overall fluctuation characteristics between parameters; relying on linear models and being unable to accurately describe the periodic fluctuations and nonlinear effects in complex systems; and overreacting to local fluctuations may lead to energy waste and equipment wear.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation control, and particularly to an intelligent monitoring system and method for coal bioconversion production. Background Art

[0002] With the continuous increase in energy demand and the growing importance of environmental protection, the efficient utilization and clean conversion of coal resources have gradually become the focus of global energy technology development; coal bioconversion is a process of converting organic substances in coal into utilizable energy or chemicals using microorganisms or enzymatic reactions. The core principle is that under specific environmental conditions, through the metabolic action of organisms, the organic substances in coal are degraded, releasing energy or being converted into other valuable chemicals. This not only can significantly reduce the pollutant emissions of coal but also can achieve the multiple utilization of coal, having strong market potential. With the characteristics of high efficiency and environmental protection, it has become a key technology for solving the sustainable utilization of coal resources. However, although coal bioconversion technology has great potential, due to its complex bioconversion process, the dynamic behavior of the system is complex and changeable, and it is affected by many factors. Therefore, it is currently difficult to control and optimize the coal bioconversion process.

[0003] Traditional production monitoring methods have the following problems: usually relying on the analysis of a single parameter, such as temperature, humidity, etc., it is easy to ignore the coupling relationship between parameters and the overall fluctuation characteristics; relying on linear models, it is impossible to accurately describe the periodic fluctuations and non-linear effects in complex systems; excessive response to local fluctuations may lead to energy waste and equipment wear. Summary of the Invention

[0004] The present invention provides an intelligent monitoring system and method for coal bioconversion production to solve the technical problems that traditional production monitoring methods usually rely on the analysis of a single parameter, such as temperature, humidity, etc., are easy to ignore the coupling relationship between parameters and the overall fluctuation characteristics; rely on linear models, and cannot accurately describe the periodic fluctuations and non-linear effects in complex systems; and excessive response to local fluctuations may lead to energy waste and equipment wear.

[0005] An intelligent monitoring system and method for coal bioconversion production of the present invention specifically include the following technical solutions:

[0006] An intelligent monitoring method for coal bioconversion production includes the following steps:

[0007] S1. Collect parameters in real time and perform standardization processing to obtain standardized parameters; based on the standardized parameters, calculate the topological fluctuation entropy through a topological fluctuation entropy calculation algorithm.

[0008] S2. Based on the standardized parameters and topological fluctuation entropy, construct an adaptive resonance energy flow model, calculate the adaptive resonance energy flow; introduce a dual-threshold monitoring mechanism, and based on the topological fluctuation entropy, adaptive resonance energy flow and standardized parameters, perform adaptive regulation on coal bioconversion production.

[0009] Preferably, the S1 specifically includes:

[0010] In the implementation process of the topological fluctuation entropy calculation algorithm, calculate the change amount of the standardized parameters between the current time and the previous time, introduce a smoothing factor and historical fluctuation standard deviation, and convert the fluctuation amplitude of the standardized parameters into a natural logarithm term to quantify the fluctuation intensity of the standardized parameters.

[0011] Preferably, the S1 specifically includes:

[0012] In the implementation process of the topological fluctuation entropy calculation algorithm, introduce a topological coupling coefficient, integrate the topological coupling coefficients between all parameter pairs in the form of a sum of squares, and combine with the natural logarithm term to calculate the topological fluctuation entropy.

[0013] Preferably, the S2 specifically includes:

[0014] The adaptive resonance energy flow model divides the calculation of the adaptive resonance energy flow into two parts. The first part is the linear contribution of the topological fluctuation entropy, and the second part introduces the non-linear resonance term of the parameters, and simulates the periodic interaction between the standardized parameters and the topological fluctuation entropy through a sine function.

[0015] Preferably, the S2 specifically includes:

[0016] In the implementation process of the dual-threshold monitoring mechanism, set threshold ranges for the topological fluctuation entropy and the adaptive resonance energy flow respectively. When both the topological fluctuation entropy and the adaptive resonance energy flow are within the threshold ranges, it indicates that the coal bioconversion production is in an ideal state and no intervention is required; when any one of the topological fluctuation entropy and the adaptive resonance energy flow exceeds the threshold range, it indicates that it is necessary to calculate the regulation variable to correct the deviation.

[0017] Preferably, the S2 specifically includes:

[0018] In the calculation process of the regulation variable, calculate the partial derivatives of the adaptive resonance energy flow with respect to the associated parameters of the regulation variable and the partial derivatives of the topological fluctuation entropy with respect to the associated parameters of the regulation variable, and introduce a weight coefficient to perform weighted summation on the partial derivatives of each associated parameter to obtain the total sum of the weighted partial derivatives of the associated parameters.

[0019] Preferably, the S2 specifically includes:

[0020] Based on the sum of weighted partial derivatives of correlation parameters, the absolute value and sign functions are introduced, combined with the gain coefficient, to calculate the control variable, and the control variable is transformed into an actual control signal to achieve control.

[0021] An intelligent monitoring system for coal bioconversion production includes the following parts:

[0022] A data acquisition module, a parameter standardization module, a topological fluctuation entropy calculation module, an adaptive resonance energy flow calculation module, a threshold monitoring and judgment module, a control variable generation module, and an execution control module;

[0023] Data acquisition module: Collect parameters in real time and output the parameters collected in real time to the parameter standardization module;

[0024] Parameter standardization module: Standardize the parameters collected in real time to obtain the standardized parameters; Output the standardized parameters to the topological fluctuation entropy calculation module, the adaptive resonance energy flow calculation module, and the control variable generation module;

[0025] Topological fluctuation entropy calculation module: Based on the standardized parameters, use the topological fluctuation entropy calculation algorithm to calculate the topological fluctuation entropy; Output the topological fluctuation entropy to the adaptive resonance energy flow calculation module, the threshold monitoring and judgment module, and the control variable generation module;

[0026] Adaptive resonance energy flow calculation module: Based on the standardized parameters and the topological fluctuation entropy, construct an adaptive resonance energy flow model to calculate the adaptive resonance energy flow; Output the adaptive resonance energy flow to the threshold monitoring and judgment module and the control variable generation module;

[0027] Threshold monitoring and judgment module: Set threshold ranges for the topological fluctuation entropy and the adaptive resonance energy flow respectively. When both the topological fluctuation entropy and the adaptive resonance energy flow are within the threshold ranges, it indicates that the coal bioconversion production is in an ideal state and no intervention is required; When any one of the topological fluctuation entropy and the adaptive resonance energy flow exceeds the threshold range, it indicates that regulation is needed; Output the regulation instruction to the control variable generation module;

[0028] Control variable generation module: Calculate the control variable based on the standardized parameters, the topological fluctuation entropy, and the adaptive resonance energy flow; Output the control variable as an actual control signal to the execution control module;

[0029] Execution control module: Achieve control according to the actual control signal.

[0030] The beneficial effects of the technical solution of the present invention are:

[0031] 1. By collecting temperature, humidity and other parameters in real time through sensors and performing standardized processing, the differences in the dimensions and magnitudes of different parameters can be eliminated, enabling the analysis and calculation of each parameter within a unified range. For the standardized parameters, through the calculation of topological fluctuation entropy, the fluctuation characteristics of the intelligent monitoring system can be comprehensively quantified, avoiding the limitations of traditional methods that rely only on the analysis of a single parameter.

[0032] 2. By introducing the topological coupling coefficient, the interaction and correlation between parameters can be considered, thus enhancing the accuracy of fluctuation analysis and the reliability of system state monitoring.

[0033] 3. The proposed adaptive resonance energy flow model can effectively simulate the interaction between periodic fluctuations and topological fluctuation entropy in the production process, accurately reflect the dynamic changes of the energy state, and improve the monitoring and control ability of energy flow in the production process.

[0034] 4. By introducing a dual-threshold monitoring mechanism, a reasonable threshold range is set for topological fluctuation entropy and adaptive resonance energy flow to achieve accurate regulation judgment. Only when any one of the topological fluctuation entropy and adaptive resonance energy flow exceeds the threshold range, the regulation is triggered, thus avoiding unnecessary adjustment operations. The accurate regulation judgment mechanism not only optimizes the resource allocation in the production process, but also reduces energy consumption and equipment wear, extends the service life of the equipment, and reduces maintenance costs.

[0035] 5. During the regulation process, by calculating the partial derivatives of the adaptive resonance energy flow and topological fluctuation entropy and performing weighted summation in combination with the weight coefficient, the influence degree of the associated parameters of the regulation variable on the system state is accurately quantified. By introducing the gain coefficient and sign function, it is ensured that the adjustment amplitude and direction of the regulation variable meet the production requirements and adapt to the specific control scenario, effectively improving the accuracy of regulation and ensuring the dynamic balance and stability of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a structural diagram of an intelligent monitoring system for coal bioconversion production according to the present invention;

[0037] Figure 2 It is a flowchart of an intelligent monitoring method for coal bioconversion production according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0038] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below 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 of 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 shall fall within the protection scope of the present invention.

[0039] Unless otherwise defined, 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.

[0040] The following specifically describes the specific solutions of an intelligent monitoring system and method for coal bioconversion production provided by the present invention in conjunction with the accompanying drawings.

[0041] Referring to the attached Figure 1 , which shows the structure diagram of an intelligent monitoring system for coal bioconversion production provided by an embodiment of the present invention. The system includes the following parts:

[0042] A data acquisition module, a parameter standardization module, a topological fluctuation entropy calculation module, an adaptive resonance energy flow calculation module, a threshold monitoring and judgment module, a control variable generation module, and an execution control module;

[0043] Data acquisition module: Use sensors to collect parameters in real time and output the parameters collected in real time to the parameter standardization module;

[0044] Parameter standardization module: Standardize the parameters collected in real time to obtain standardized parameters; Output the standardized parameters to the topological fluctuation entropy calculation module, the adaptive resonance energy flow calculation module, and the control variable generation module;

[0045] Topological fluctuation entropy calculation module: Based on the standardized parameters, use the topological fluctuation entropy calculation algorithm to calculate the topological fluctuation entropy; Output the topological fluctuation entropy to the adaptive resonance energy flow calculation module, the threshold monitoring and judgment module, and the control variable generation module;

[0046] Adaptive resonance energy flow calculation module: Based on the standardized parameters and the topological fluctuation entropy, construct an adaptive resonance energy flow model and calculate the adaptive resonance energy flow; Output the adaptive resonance energy flow to the threshold monitoring and judgment module and the control variable generation module;

[0047] Threshold monitoring and judgment module: Sets reasonable threshold ranges for topological fluctuation entropy and adaptive resonance energy flow respectively; When both the topological fluctuation entropy and the adaptive resonance energy flow are within the threshold range, it indicates that the coal bioconversion production is in an ideal state and no intervention is required; When any one of the topological fluctuation entropy and the adaptive resonance energy flow exceeds the threshold range, it indicates that regulation is needed; Outputs the regulation instruction to the regulation variable generation module;

[0048] Regulation variable generation module: Calculates the regulation variable based on the standardized parameters, topological fluctuation entropy, and adaptive resonance energy flow; Outputs the regulation variable as the actual control signal to the execution control module;

[0049] Execution control module: Implements control according to the actual control signal.

[0050] Refer to the appendix Figure 2 which shows the flowchart of an intelligent monitoring method for coal bioconversion production provided by an embodiment of the present invention. The method includes the following steps:

[0051] S1. Real-time collect parameters and perform standardization processing to obtain standardized parameters; Based on the standardized parameters, through the topological fluctuation entropy calculation algorithm, calculate the topological fluctuation entropy;

[0052] Use sensors to collect parameters in real time such as temperature, humidity, etc.; In order to eliminate the differences in different parameter dimensions and magnitudes and facilitate subsequent unified analysis and calculation, perform standardization processing on the real-time collected parameters to obtain standardized parameters with a value range of [0,1]. The standardization processing formula is as follows:

[0053] ,

[0054] where, represents the standardized th parameter at time ; represents the minimum value of the th parameter, from the existing database; represents the maximum value of the th parameter, from the existing database;

[0055] In a complex dynamic system, such as the bioconversion process, different parameters (such as temperature, humidity, oxygen concentration, etc.) will change over time. The changes may be either characteristics of normal operation or signals of out-of-control. In order to effectively monitor the production state and quantify parameter fluctuations, based on the standardized parameters, through the topological fluctuation entropy calculation algorithm, calculate the topological fluctuation entropy to comprehensively describe the fluctuation characteristics of the parameters and avoid the limitations of single-parameter analysis;

[0056] The topological fluctuation entropy calculation algorithm converts the fluctuation amplitude of each standardized parameter into a natural logarithm term to quantify the fluctuation intensity. To prevent the situation of too small fluctuation amplitude or denominator being zero, a smoothing factor is introduced. The smoothing factor is a small constant used to ensure the stability of the calculation while retaining the sensitivity of the fluctuation characteristics. At the same time, the historical fluctuation standard deviation is introduced as a reference benchmark, and the historical fluctuation standard deviation is dynamically updated through historical data, reflecting the long-term fluctuation characteristics of each parameter.

[0057] Since the coupling relationship between parameters will affect the overall fluctuation characteristics, for example, an increase in temperature may lead to a decrease in humidity. To capture the parameter topological structure, a topological coupling coefficient is introduced. The topological coupling coefficient is obtained through correlation analysis of historical data, reflecting the dynamic correlation strength between parameters. The correlation analysis method adopted is a well-known technical means for those skilled in the art and will not be elaborated here. By means of the sum of squares form, the topological coupling coefficients between all parameter pairs are integrated. The design of the sum of squares enhances the weight of the coupling relationship, while the square root avoids the numerical value being too large, ensuring that the magnitude of the topological fluctuation entropy is appropriate.

[0058] The calculation formula of the topological fluctuation entropy is:

[0059] ,

[0060] where, represents the topological fluctuation entropy at time , reflecting the fluctuation characteristics at time ; represents the negative sign, conforming to the definition of entropy; represents traversing all standardized parameters (from to ), and accumulating the contributions of each parameter; represents the natural logarithm term, used to quantify the fluctuation amplitude of the standardized parameter, reflecting the contribution of dynamic changes to the disorder degree of the system. The logarithmic form smooths the influence of changes and avoids the result being distorted due to extreme values; represents the absolute value of the change amount of the th standardized parameter between time and , used to measure the fluctuation intensity of the parameter. The absolute value ensures that both positive and negative changes are regarded as fluctuation contributions; represents the historical fluctuation standard deviation of the th parameter at time , used to reflect the historical fluctuation level of the parameter, and using the historical fluctuation standard deviation as a benchmark to standardize the relative magnitude; denotes the smoothing factor, which is set as a constant to avoid a zero denominator and ensure the numerical stability of the formula. It can be specifically set according to the specific implementation scenario and is not limited here; denotes the topological coupling term, which is used to quantify the interaction strength between parameters, reflect the complexity of the parameter topological structure, and serve as an amplification factor for the topological fluctuation entropy. The square sum and square root are used to maintain numerical stability; denotes the th parameter after standardization and the th parameter. The topological coupling coefficient between them reflects the correlation or mutual influence between parameters. The larger the value of the topological coupling coefficient, the stronger the coupling;

[0061] By combining the fluctuation characteristics with the topological relationship between parameters, it is possible to capture the dynamic behavior of the overall coal bioconversion production and avoid the limitations of traditional methods that only focus on a single parameter;

[0062] S2. Based on the standardized parameters and topological fluctuation entropy, construct an adaptive resonance energy flow model, calculate the adaptive resonance energy flow; introduce a double-threshold monitoring mechanism, and based on the topological fluctuation entropy, adaptive resonance energy flow, and standardized parameters, perform adaptive regulation on the coal bioconversion production;

[0063] Based on the standardized parameters and topological fluctuation entropy, construct an adaptive resonance energy flow model, and calculate the adaptive resonance energy flow;

[0064] During the bioconversion process, the energy flow is not only related to the fluctuation intensity but also affected by the periodic changes of parameters. To monitor and regulate the dynamic energy state during the bioconversion process, an adaptive resonance energy flow model is constructed; the adaptive resonance energy flow model introduces the concept of resonance. Specifically, the calculation of the adaptive resonance energy flow is divided into two parts. The first part is the linear contribution of the topological fluctuation entropy, which is used to reflect the direct influence of the fluctuation intensity on the adaptive resonance energy flow. The second part introduces the non-linear resonance term of the parameters, and the periodic interaction between the standardized parameters and the topological fluctuation entropy is simulated through a sine function;

[0065] The calculation formula of the adaptive resonance energy flow:

[0066] ,

[0067] where, denotes the adaptive resonance energy flow at time , which is used to measure the energy state and dynamic balance at time ; denotes the first adjustment coefficient, which is used to control the influence of the topological fluctuation entropy on the adaptive resonance energy flow. It can be specifically set according to the specific implementation scenario and is not limited here; Denotes the second adjustment coefficient, which is used to control the overall impact of the non - linear term on the adaptive resonance energy flow and can be specifically set according to the specific implementation scenario and is not limited herein; Denotes the sine function, which introduces the non - linear resonance effect to simulate the periodic interaction between the parameters and the topological fluctuation entropy; Denotes the resonant frequency of the standardized

[0068] To achieve the adaptive regulation of coal bioconversion production, a dual - threshold monitoring mechanism is introduced, and reasonable threshold ranges are set for the topological fluctuation entropy and the adaptive resonance energy flow respectively, which are shown as follows:

[0069] ,

[0070] ,

[0071] Among them, Denotes the minimum value of the topological fluctuation entropy; Denotes the maximum value of the topological fluctuation entropy; Denotes the minimum value of the adaptive resonance energy flow; Denotes the maximum value of the adaptive resonance energy flow;

[0072] The threshold range is determined based on production requirements and can be specifically set according to the specific implementation scenario and is not limited herein;

[0073] Design the judgment logic: When both the topological fluctuation entropy and the adaptive resonance energy flow are within the threshold range, it is considered that the coal bioconversion production is in an ideal state and no intervention is required. Once any one of the topological fluctuation entropy and the adaptive resonance energy flow exceeds the threshold range, it is necessary to calculate the regulation variable to correct the deviation;

[0074] The regulation variable starts from the influence of the associated parameters of the regulation variable and quantifies the influence degree of each associated parameter on the topological fluctuation entropy and the adaptive resonance energy flow through partial derivatives; specifically, by calculating the partial derivative of the adaptive resonance energy flow with respect to the associated parameter, the sensitivity of the energy state to the change of the associated parameter is obtained. At the same time, calculate the partial derivative of the topological fluctuation entropy with respect to the associated parameter to obtain the sensitive direction of the fluctuation state, and introduce a weight coefficient to perform weighted summation on the partial derivatives of each associated parameter to obtain the total sum of the weighted partial derivatives of the associated parameters, thereby reflecting the correlation and relative importance among the associated parameters;

[0075] To enable the control variable to not only reflect the magnitude of the adjustment but also indicate the direction of the adjustment, a combination of the absolute value and the sign function is introduced. The absolute value is used to determine the intensity of the control to ensure that the control variable is proportional to the degree of deviation, while the sign function determines the positive or negative of the control variable based on the partial derivative and direction of the topological fluctuation entropy. To adapt to the physical characteristics of different control variables, a gain coefficient is introduced to convert the dimensionless calculation result into a specific control unit, enhancing the practicality;

[0076] The calculation formula of the control variable is:

[0077] ,

[0078] where, represents the value of the th control variable at time , which is used to adjust the production operation conditions, such as oxygen supply, moisture, etc.; represents the gain coefficient of the th control variable to ensure that the output value matches the actual physical system. It can be specifically set according to the specific implementation scenario and is not limited here; represents the absolute value of the sum of the weighted partial derivatives of the adaptive resonance energy flow with respect to the correlation parameters, which is used to quantify the sensitivity of the adaptive resonance energy flow to changes in multiple correlation parameters and determine the basic magnitude of the control amplitude. The absolute value is used to ensure that the control strength is always positive; represents the summation of the contributions of all the correlation parameters in the th set of correlation parameters for the th control variable; represents the th correlation parameter of the th control variable; represents the th set of correlation parameters of the th control variable; represents the partial derivative of the adaptive resonance energy flow with respect to the correlation parameter; represents the sign function part, which is used to determine the direction of the control (positive or negative), represents the partial derivative of the topological fluctuation entropy with respect to the correlation parameter; represents the th standardized correlation parameter of the th control variable at time ;

[0079] Through the double-threshold monitoring mechanism, it can accurately judge whether intervention is needed, avoiding unnecessary control operations, thereby reducing energy consumption and equipment wear.

[0080] In summary, an intelligent monitoring system and method for coal bioconversion production have been completed.

[0081] The order of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

[0083] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A method for intelligent monitoring of coal bioconversion production, characterized in that: The following steps are involved: S1. Collect parameters in real time and perform standardization to obtain standardized parameters; calculate the change of the standardized parameters between the current time and the previous time through the topological fluctuation entropy calculation algorithm, introduce the smoothing factor and the historical fluctuation standard deviation, convert the fluctuation amplitude of the standardized parameters into the natural logarithm term, and quantify the fluctuation intensity of the standardized parameters; introduce the topological coupling coefficient, integrate the topological coupling coefficients between all parameter pairs in the form of the sum of squares, and combine the natural logarithm term to calculate the topological fluctuation entropy; S2. Based on the standardized parameters and topological fluctuation entropy, an adaptive resonant energy flow model is constructed. The calculation of the adaptive resonant energy flow is divided into two parts. The first part is the linear contribution of the topological fluctuation entropy. The second part introduces the nonlinear resonance term of the parameters. The periodic interaction between the standardized parameters and the topological fluctuation entropy is simulated by the sine function, and the adaptive resonant energy flow is calculated. A dual-threshold monitoring mechanism is introduced to adaptively control coal bioconversion production based on topological fluctuation entropy, adaptive resonant energy flow and standardized parameters.

2. The method for intelligent monitoring of coal bioconversion production according to claim 1, characterized in that: The S2 specifically includes: In the process of implementing the dual-threshold monitoring mechanism, threshold ranges are set for the topological fluctuation entropy and the adaptive resonant energy flow respectively. When both the topological fluctuation entropy and the adaptive resonant energy flow are within the threshold range, it indicates that the coal bioconversion production is in an ideal state and no intervention is required; when either the topological fluctuation entropy or the adaptive resonant energy flow exceeds the threshold range, it indicates that it is necessary to calculate the control variables and correct the deviation.

3. The method for intelligent monitoring of coal bioconversion production according to claim 2, characterized in that: The S2 specifically includes: In the process of calculating the control variables, the partial derivatives of the adaptive resonant energy flow with respect to the associated parameters of the control variables and the partial derivatives of the topological fluctuation entropy with respect to the associated parameters of the control variables are calculated, and weight coefficients are introduced to perform weighted summation on the partial derivatives of each associated parameter to obtain the sum of the weighted partial derivatives of the associated parameters.

4. The method for intelligent monitoring of coal bioconversion production according to claim 3, characterized in that: The S2 specifically includes: Based on the sum of weighted partial derivatives of associated parameters, the absolute value and sign functions are introduced, and combined with the gain coefficient, the control variable is calculated and converted into an actual control signal to achieve control.

5. A coal bioconversion production intelligent monitoring system, applied to a coal bioconversion production intelligent monitoring method as claimed in claim 1, characterized in that: Includes the following parts: Data acquisition module, parameter standardization module, topological fluctuation entropy calculation module, adaptive resonance energy flow calculation module, threshold monitoring and judgment module, control variable generation module and execution control module; Data acquisition module: collects parameters in real time and outputs the collected parameters to the parameter standardization module; Parameter standardization module: standardizes the parameters collected in real time to obtain standardized parameters; Output the standardized parameters to the topological fluctuation entropy calculation module, the adaptive resonance energy flow calculation module and the control variable generation module; Topological fluctuation entropy calculation module: Based on the standardized parameters, the topological fluctuation entropy calculation algorithm is used to calculate the topological fluctuation entropy; Output the topological fluctuation entropy to the adaptive resonant energy flow calculation module, the threshold monitoring and judgment module and the control variable generation module; Adaptive resonant energy flow calculation module: Based on the standardized parameters and topological fluctuation entropy, an adaptive resonant energy flow model is constructed to calculate the adaptive resonant energy flow; Outputting the adaptive resonant energy flow to the threshold monitoring and judgment module and the control variable generation module; Threshold monitoring and judgment module: set threshold ranges for topological fluctuation entropy and adaptive resonance energy flow respectively. When both topological fluctuation entropy and adaptive resonance energy flow are within the threshold range, it indicates that the coal bioconversion production is in an ideal state and no intervention is required; when either topological fluctuation entropy or adaptive resonance energy flow exceeds the threshold range, it indicates that regulation is required; output the regulation instruction to the regulation variable generation module; Control variable generation module: calculates control variables based on standardized parameters, topological fluctuation entropy and adaptive resonant energy flow; Outputting the control variable as an actual control signal to the execution control module; Execution control module: implements control according to actual control signals.

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