Intelligent monitoring system and method for coal biotransformation production

Abstract

The invention relates to the field of industrial automation control, in particular to an intelligent monitoring system and method for coal bioconversion production. The method comprises the steps that parameters are collected in real time and subjected to standardization processing, and based on the standardized parameters, the topological fluctuation entropy is obtained through calculation through a topological fluctuation entropy calculation algorithm; based on the standardized parameters and the topological fluctuation entropy, an adaptive resonance energy flow model is constructed, and adaptive resonance energy flow is obtained through calculation; and introducing a dual-threshold monitoring mechanism, and carrying out adaptive regulation and control on coal bioconversion production based on topological fluctuation entropy, adaptive resonance energy flow and standardized parameters. The problems that a traditional production monitoring method generally depends on analysis of single parameters such as temperature and humidity, and the coupling relation and the overall fluctuation characteristic between the parameters are prone to being ignored; the periodic fluctuation and the nonlinear effect in a complex system cannot be accurately described by depending on a linear model; and the problems of energy waste and equipment abrasion can be caused by excessive reaction to local fluctuation are solved.

Description

Technical Field

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

[0002] With the continuous increase in energy demand and the increasing importance of environmental protection, the efficient utilization and clean transformation of coal resources have gradually become the focus of global energy technology development; coal bioconversion is the process of converting organic matter in coal into usable energy or chemicals using microorganisms or enzymatic reactions. The core principle is to degrade organic matter in coal through the metabolism of organisms under specific environmental conditions, release energy or convert it into other valuable chemicals, which can not only significantly reduce coal pollutant emissions, but also achieve multiple utilization of coal. It has strong market potential and has become a key technology to solve the sustainable utilization of coal resources with its high efficiency and environmental protection characteristics. However, although coal bioconversion technology has great potential, it involves complex biological transformation processes, resulting in complex and changeable dynamic behaviors of the system, and 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: they usually rely on the analysis of a single parameter, such as temperature, humidity, etc., and easily ignore the coupling relationship between parameters and the overall fluctuation characteristics; they rely on linear models and cannot accurately describe the periodic fluctuations and nonlinear effects in complex systems; overreaction 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., and easily ignore the coupling relationship and overall fluctuation characteristics between parameters; rely on linear models and cannot accurately describe periodic fluctuations and nonlinear effects in complex systems; and overreact to local fluctuations may cause energy waste and equipment wear.

[0005] The present invention provides an intelligent monitoring system and method for coal bioconversion production, which specifically includes the following technical solutions: A method for intelligent monitoring of coal bioconversion production, comprising the following steps: S1, real-time collection of parameters and standardization processing to obtain standardized parameters; based on the standardized parameters, the topological fluctuation entropy is calculated by the topological fluctuation entropy calculation algorithm; S2. Based on the standardized parameters and topological fluctuation entropy, an adaptive resonant energy flow model is constructed, and the adaptive resonant energy flow is calculated; a dual-threshold monitoring mechanism is introduced to adaptively control the coal bioconversion production based on the topological fluctuation entropy, adaptive resonant energy flow and standardized parameters.

[0006] Preferably, the S1 specifically includes: In the implementation process of the topological fluctuation entropy calculation algorithm, the change of the standardized parameters between the current time and the previous time is calculated, and the smoothing factor and the historical fluctuation standard deviation are introduced to convert the fluctuation amplitude of the standardized parameters into natural logarithmic terms to quantify the fluctuation intensity of the standardized parameters.

[0007] Preferably, the S1 specifically includes: In the implementation process of the topological fluctuation entropy calculation algorithm, the topological coupling coefficient is introduced. The topological coupling coefficients between all parameter pairs are integrated in the form of square sum, and combined with the natural logarithm term to calculate the topological fluctuation entropy.

[0008] Preferably, the S2 specifically includes: The adaptive resonant energy flow model divides the calculation of the adaptive resonant energy flow into two parts. The first part is the linear contribution of the topological fluctuation entropy, and the second part introduces the nonlinear resonance term of the parameter and simulates the periodic interaction between the standardized parameter and the topological fluctuation entropy through a sine function.

[0009] Preferably, 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.

[0010] Preferably, 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.

[0011] Preferably, 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.

[0012] An intelligent monitoring system for coal bioconversion production 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; outputs 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; the topological fluctuation entropy is output to the adaptive resonance 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; the adaptive resonant energy flow is output 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; outputs the control variables as actual control signals to the execution control module; Execution control module: implements control according to actual control signals.

[0013] The beneficial effects of the technical solution of the present invention are: 1. By collecting temperature, humidity and other parameters in real time through sensors and performing standardization, the differences in dimensions and magnitudes of different parameters can be eliminated, so that each parameter can be analyzed and calculated within a unified range; the standardized parameters can be fully quantified through topological fluctuation entropy calculation to achieve the fluctuation characteristics of the intelligent monitoring system, thus avoiding the limitation of traditional methods that rely only on single parameter analysis.

[0014] 2. By introducing the topological coupling coefficient, the interaction and correlation between parameters can be considered, thereby enhancing the accuracy of fluctuation analysis and the reliability of system status monitoring.

[0015] 3. The introduction of the adaptive resonant 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 ability to monitor and control energy flow in the production process.

[0016] 4. By introducing a dual-threshold monitoring mechanism, a reasonable threshold range is set for the topological fluctuation entropy and the adaptive resonant energy flow to achieve accurate control judgment. The control is triggered only when either the topological fluctuation entropy or the adaptive resonant energy flow exceeds the threshold range, thus avoiding unnecessary adjustment operations. The accurate control 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.

[0017] 5. During the control process, the partial derivatives of the adaptive resonant energy flow and the topological fluctuation entropy are calculated, and weighted summation is performed in combination with the weight coefficient to accurately quantify the degree of influence of the associated parameters of the control variables on the system state; the gain coefficient and sign function are introduced to ensure that the adjustment amplitude and direction of the control variables meet the production requirements and adapt to the specific control scenarios, which effectively improves the accuracy of the control and ensures the dynamic balance and stability of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a structural diagram of an intelligent monitoring system for coal bioconversion production according to the present invention; Figure 2 This is a flow chart of the intelligent monitoring method for coal bioconversion production described in the present invention. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all 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.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0021] The specific scheme of the intelligent monitoring system and method for coal bioconversion production provided by the present invention is described in detail below with reference to the accompanying drawings.

[0022] See attached Figure 1, which shows a structure diagram of a coal bioconversion production intelligent monitoring system provided by an embodiment of the present invention, the system 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: uses sensors to collect 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; outputs 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; the topological fluctuation entropy is output to the adaptive resonance 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; the adaptive resonant energy flow is output to the threshold monitoring and judgment module and the control variable generation module; Threshold monitoring and judgment module: reasonable threshold ranges are set 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; the regulation instructions are output 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; outputs the control variables as actual control signals to the execution control module; Execution control module: implements control according to actual control signals.

[0023] See attached Figure 2 , which shows a flow chart of a method for intelligent monitoring of coal bioconversion production provided by an embodiment of the present invention, the method comprising the following steps: S1, real-time collection of parameters and standardization processing to obtain standardized parameters; based on the standardized parameters, the topological fluctuation entropy is calculated by the topological fluctuation entropy calculation algorithm; Real-time parameter collection using sensors , such as temperature, humidity, etc.; in order to eliminate the differences in dimensions and magnitudes of different parameters and facilitate subsequent unified analysis and calculation, the parameters collected in real time are standardized to obtain standardized parameters with a value range of [0,1]. The standardization formula is as follows: , in, Indicates at time After standardization parameters; Indicates The minimum value of the parameters comes from the existing database; Indicates The maximum value of the parameters comes from the existing database; In complex dynamic systems, such as biotransformation processes, different parameters (such as temperature, humidity, oxygen concentration, etc.) will change over time. The changes may be characteristics of normal operation or signals of loss of control. In order to effectively monitor the production status and quantify parameter fluctuations, the topological fluctuation entropy is calculated based on the standardized parameters through the topological fluctuation entropy calculation algorithm to comprehensively describe the fluctuation characteristics of the parameters while avoiding the limitations of single parameter analysis. The topological fluctuation entropy calculation algorithm converts the fluctuation amplitude of each standardized parameter into a natural logarithm term to quantify the fluctuation intensity. In order to prevent the fluctuation amplitude from being too small or the 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. The historical fluctuation standard deviation is dynamically updated through historical data, reflecting the long-term fluctuation characteristics of each parameter. Since the coupling relationship between parameters will affect the overall fluctuation characteristics, such as temperature increase may lead to humidity decrease, in order to capture the parameter topological structure, a topological coupling coefficient is introduced. The topological coupling coefficient is obtained by correlation analysis of historical data, which reflects the dynamic correlation strength between parameters. The correlation analysis method adopted is a technical means well known to those skilled in the art and will not be described in detail here. The topological coupling coefficients between all parameter pairs are integrated in the form of a sum of squares. The design of the sum of squares enhances the weight of the coupling relationship, while the square root avoids excessive values ​​and ensures that the magnitude of the topological fluctuation entropy is moderate. The calculation formula of topological fluctuation entropy is: , in, Indicates at time The topological fluctuation entropy at time The volatility characteristics of It indicates a negative sign, which conforms to the definition of entropy; Indicates traversal of all The standardized parameters (from arrive ), summing up the contribution of each parameter; Represents the natural logarithm term, which is used to quantify the fluctuation amplitude of the standardized parameter and reflect the contribution of dynamic changes to the disorder of the system. The logarithmic form smoothes the impact of the change and avoids distortion of the results caused by extreme values. Represents the standardized Parameters in time and The absolute value of the change between is used to measure the fluctuation intensity of the parameter. The absolute value ensures that both positive and negative changes are considered as fluctuation contributions. Indicates at time No. The historical fluctuation standard deviation of the parameter is used to reflect the historical fluctuation level of the parameter, and the historical fluctuation standard deviation is used as a benchmark for standardization. The relative size of Represents the smoothing factor, which is set as a constant to avoid the denominator being zero to ensure the stability of the formula value. It can be set according to the specific implementation scenario and is not limited here; It represents the topological coupling term, which is used to quantify the interaction strength between parameters and reflect the complexity of the topological structure of parameters. It is used as an amplification factor of the topological fluctuation entropy. The square root is used to maintain numerical stability. Represents the standardized Parameters and The topological coupling coefficient between the parameters reflects the correlation or mutual influence between the parameters. The larger the value of the topological coupling coefficient, the stronger the coupling. By combining the fluctuation characteristics with the topological relationship between parameters, the dynamic behavior of the overall coal bioconversion production can be captured, avoiding the limitation of traditional methods that only focus on a single parameter; S2. Based on the standardized parameters and topological fluctuation entropy, an adaptive resonant energy flow model is constructed to calculate the adaptive resonant energy flow; a dual-threshold monitoring mechanism is introduced to adaptively regulate the coal bioconversion production based on the topological fluctuation entropy, the adaptive resonant energy flow and the standardized parameters; Based on the standardized parameters and topological fluctuation entropy, an adaptive resonant energy flow model is constructed, and the adaptive resonant energy flow is calculated. In the process of biotransformation, energy flow is not only related to the intensity of fluctuations, but also affected by the periodic changes of parameters. In order to monitor and regulate the dynamic energy state in the process of biotransformation, an adaptive resonant energy flow model is constructed; the adaptive resonant energy flow model introduces the concept of resonance. Specifically, 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, which is used to reflect the direct influence of the fluctuation intensity on the adaptive resonant energy flow. The second part introduces the nonlinear resonance term of the parameter, and simulates the periodic interaction between the standardized parameter and the topological fluctuation entropy through a sine function. The calculation formula of adaptive resonant energy flow is: , in, Indicates at time The adaptive resonant energy flow at time is used to measure the Energy status and dynamic balance under represents the first adjustment coefficient, which is used to control the influence of the topological fluctuation entropy on the adaptive resonant energy flow, and can be set according to the specific implementation scenario and is not limited here; represents the second adjustment coefficient, which is used to control the overall influence of the nonlinear term on the adaptive resonant energy flow, and can be set according to the specific implementation scenario, and is not limited here; Representing a sinusoidal function, nonlinear resonance effects are introduced to simulate the periodic interaction between parameters and topological fluctuation entropy; Represents the standardized The resonant frequency of each parameter is used to adjust the influence period of the topological fluctuation entropy in the sine function, so that the resonance effect of each parameter is independent and reflects the differentiated dynamics between the parameters; In order to realize the adaptive regulation of coal bioconversion production, a dual-threshold monitoring mechanism is introduced, and reasonable threshold ranges are set for topological fluctuation entropy and adaptive resonance energy flow, respectively, as shown below: , , in, represents the minimum value of topological fluctuation entropy; represents the maximum value of topological fluctuation entropy; Indicates the minimum value of adaptive resonant energy flow; Indicates the maximum value of adaptive resonant energy flow; The threshold range is determined based on production requirements and can be set according to specific implementation scenarios, and is not limited here; Design 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 either the topological fluctuation entropy or the adaptive resonance energy flow exceeds the threshold range, it is necessary to calculate the control variables to correct the deviation. The control variable starts with the influence of the associated parameters of the control variable, and quantifies the influence of each associated parameter on the topological fluctuation entropy and the adaptive resonant energy flow through partial derivatives; specifically, by calculating the partial derivatives of the adaptive resonant energy flow with respect to the associated parameters, the sensitivity of the energy state to the change of the associated parameters is obtained. At the same time, the partial derivatives of the topological fluctuation entropy with respect to the associated parameters are calculated to obtain the sensitive direction of the fluctuation state, and a weight coefficient is 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, thereby reflecting the correlation and relative importance between the associated parameters; In order to make the control variable reflect both the amplitude of adjustment and the direction of adjustment, a combination of absolute value and sign function is introduced. The absolute value is used to determine the intensity of control and 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 according to the partial derivative and direction of the topological fluctuation entropy. In order to adapt to the physical characteristics of different control variables, the gain coefficient is introduced to convert the dimensionless calculation results into specific control units, which enhances practicality. The calculation formula of the control variable is: , in, Indicates at time Time The value of a control variable is used to adjust production operating conditions, such as oxygen supply, moisture, etc. Indicates The gain coefficient of the control variable ensures that the output value matches the actual physical system. It can be 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 resonant energy flow with respect to the associated parameters. It is used to quantify the sensitivity of the adaptive resonant energy flow to the changes of multiple associated parameters and determine the basic size of the control amplitude. The absolute value is used to ensure that the control amplitude is always positive. Indicates The set of associated parameters of the control variables Sum the contributions of all associated parameters in; Indicates The associated parameters of the control variables; Indicates A set of associated parameters for each control variable; Indicates The associated parameter of the control variable The weight coefficient is used to allocate the contribution ratio of each associated parameter to the control variable, which can be set according to the specific implementation scenario and is not limited here; represents the partial derivative of the adaptive resonant energy flow with respect to the associated parameters; Represents the sign function part, which is used to determine the direction of regulation (positive or negative). represents the partial derivative of the topological fluctuation entropy with respect to the associated parameter; Indicates at time After standardization The associated parameter of the control variable ; Through the dual-threshold monitoring mechanism, it is possible to accurately determine whether intervention is needed, avoiding unnecessary control operations, thereby reducing energy consumption and equipment wear.

[0024] In summary, an intelligent monitoring system and method for coal bioconversion production has been completed.

[0025] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the 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.

[0026] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0027] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should 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, real-time collection of parameters and standardization processing to obtain standardized parameters; based on the standardized parameters, the topological fluctuation entropy is calculated by the topological fluctuation entropy calculation algorithm; S2. Based on the standardized parameters and topological fluctuation entropy, an adaptive resonant energy flow model is constructed, 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 S1 specifically includes: In the implementation process of the topological fluctuation entropy calculation algorithm, the change of the standardized parameters between the current time and the previous time is calculated, and the smoothing factor and the historical fluctuation standard deviation are introduced to convert the fluctuation amplitude of the standardized parameters into natural logarithmic terms to quantify the fluctuation intensity of the standardized parameters.

3. The method for intelligent monitoring of coal bioconversion production according to claim 2, characterized in that: The S1 specifically includes: In the implementation process of the topological fluctuation entropy calculation algorithm, the topological coupling coefficient is introduced. The topological coupling coefficients between all parameter pairs are integrated in the form of square sum, and combined with the natural logarithm term to calculate the topological fluctuation entropy.

4. The method for intelligent monitoring of coal bioconversion production according to claim 1, characterized in that: The S2 specifically includes: The adaptive resonant energy flow model divides the calculation of the adaptive resonant energy flow into two parts. The first part is the linear contribution of the topological fluctuation entropy, and the second part introduces the nonlinear resonance term of the parameter and simulates the periodic interaction between the standardized parameter and the topological fluctuation entropy through a sine function.

5. The method for intelligent monitoring of coal bioconversion production according to claim 4, 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.

6. The method for intelligent monitoring of coal bioconversion production according to claim 5, 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.

7. The method for intelligent monitoring of coal bioconversion production according to claim 6, 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.

8. An intelligent monitoring system for coal bioconversion production, applied to the intelligent monitoring method for coal bioconversion production 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 the actual control signal.

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