Pneumatic ash conveying self-adaptive control method and system based on real-time monitoring data

Abstract

The application provides a kind of pneumatic ash conveying self-adaptive control method and system based on real-time monitoring data, it is related to pneumatic conveying automatic control technical field, method includes: obtaining the real-time monitoring data of pneumatic ash conveying system;According to real-time monitoring data, the system stability margin and the ash conveying urgency of each ash bucket are calculated;Determine the rated pressure deviation of each ash conveying branch, and determine the global control mode in combination with stability margin and ash conveying urgency;According to the global control mode, the preliminary valve opening increment of each ash conveying branch is calculated, and the safety projection is carried out in combination with the current valve opening to obtain the safety valve opening;The final control signal of last control cycle is weighted and fused with safety valve opening through trigonometric function smoother, to obtain the final control signal of current control cycle and output to valve positioner;The above steps are executed cyclically, to realize the adaptive closed-loop regulation of pneumatic ash conveying process.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology for pneumatic conveying, and in particular to an adaptive control method and system for pneumatic ash conveying based on real-time monitoring data. Background Technology

[0002] Pneumatic ash conveying systems are widely used in industrial settings such as coal-fired power plants and industrial boilers. They typically utilize compressed gas as power to transport fly ash from multiple ash hoppers to ash storage via multiple conveying branches. During the parallel operation of these multiple conveying branches, the pressure status of each branch, the ash hopper level, and the valve opening jointly reflect the current operating condition of the system. By monitoring these operating parameters in real time and combining them with automatic control technology to coordinate and adjust the ash conveying process, comprehensive perception and continuous control of the ash conveying status across multiple branches can be achieved. This provides a technical foundation for the stable operation, safe regulation, and improved conveying efficiency of pneumatic ash conveying systems.

[0003] Current pneumatic ash conveying control technology can adjust the ash conveying cycle, identify operating status, and control corresponding valves through pressure monitoring, and can determine the conveying demand of the ash hopper by combining material level information. In the automated control system, by collecting operating parameters such as real-time pressure of each ash conveying branch, real-time material level of each ash hopper, and current valve opening of each ash conveying branch, system status information within the same control cycle can be generated. Based on this, pressure status analysis, conveying demand assessment, and valve adjustment can be carried out, providing important support for realizing the automation, continuous, and precise control of the pneumatic ash conveying process.

[0004] However, existing technologies mostly rely on adjusting the pressure of a single branch or a fixed threshold, making it difficult to comprehensively characterize the correlation between the pressures of multiple branches and the urgency of ash hopper conveying; at the same time, there is still room for improvement in the coordinated control between local pressure status, conveying demand and overall operating status. Summary of the Invention

[0005] To address the technical challenges of existing technologies that rely heavily on single-branch pressure or fixed thresholds for adjustment, making it difficult to comprehensively characterize the correlation between multiple branch pressures and the urgency of ash hopper conveying; and the fact that there is still room for improvement in the coordinated control of local pressure status, conveying demand, and overall operating status, this invention provides a pneumatic ash conveying adaptive control method and system based on real-time monitoring data.

[0006] The technical solutions provided by the embodiments of the present invention are as follows: The first aspect of this invention provides an adaptive control method for pneumatic ash conveying based on real-time monitoring data, comprising: S1: Acquire real-time monitoring data of the pneumatic ash conveying system; S2: Calculate the stability margin of the pneumatic ash conveying system based on real-time monitoring data; S3: Calculate the urgency of ash conveying, which reflects the urgency of conveying each ash hopper; S4: Based on real-time monitoring data, determine the rated pressure deviation of each ash conveying branch, and determine the global control mode based on stability margin, urgency of each ash conveying branch, and rated pressure deviation. S5: Calculate the initial valve opening increment for each ash conveying branch according to the global control mode; S6: Based on the current valve opening and the initial valve opening increment of each ash conveying branch, perform safety projection to obtain multiple safety valve openings; S7: By using a trigonometric function smoother, the opening degree of each safety valve is weighted and fused with the final control signal of the previous control cycle to obtain the final control signal of the current control cycle of each ash conveying branch, and output to the valve positioner of the corresponding ash conveying branch. S8: Return to step S1 and enter the next control cycle. This cycle is repeated to achieve adaptive closed-loop regulation of the pneumatic ash conveying process.

[0007] A second aspect of the present invention provides a pneumatic ash conveying adaptive control system based on real-time monitoring data, comprising: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the pneumatic ash conveying adaptive control method based on real-time monitoring data as described in the first aspect.

[0008] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this embodiment of the invention, to address the problem that existing technologies have difficulty comprehensively characterizing the pressure correlation status of multiple branches and the urgency of ash hopper conveying, the stability margin of the pneumatic ash conveying system is calculated based on real-time monitoring data, and the urgency of ash conveying in each ash hopper is calculated. The global control mode is determined by combining the rated pressure deviation of each ash conveying branch. To address the problem of insufficient coordinated control of local pressure status, conveying demand and overall operating status, the initial valve opening increment is calculated based on the global control mode, and the safety valve opening is obtained through safety projection. Then, the final control signal of the previous control cycle is weighted and fused through a trigonometric function smoother and output to the valve positioner, thereby realizing the adaptive closed-loop regulation of the pneumatic ash conveying process. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating an adaptive control method for pneumatic ash conveying based on real-time monitoring data, provided in an embodiment of the present invention.

[0011] Figure 2 This invention provides a weight distribution diagram of the correlation between various ash conveying branches.

[0012] Figure 3 This invention provides a diagram showing the relationship between a congestion propagation risk operator, a lower risk threshold, an upper risk threshold, and their changes over time, as well as their determination.

[0013] Figure 4 This is a schematic diagram of a pneumatic ash conveying adaptive control system based on real-time monitoring data, provided in an embodiment of the present invention. Detailed Implementation

[0014] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0015] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0016] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0017] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Reference manual attached Figure 1 The diagram shows a flowchart of an adaptive control method for pneumatic ash conveying based on real-time monitoring data provided by an embodiment of the present invention.

[0020] This invention provides a pneumatic ash conveying adaptive control method based on real-time monitoring data. This method can be implemented by a pneumatic ash conveying adaptive control device based on real-time monitoring data, which can be a terminal or a server. The processing flow of the pneumatic ash conveying adaptive control method based on real-time monitoring data may include the following steps: S1: Obtain real-time monitoring data of the pneumatic ash conveying system.

[0021] The pneumatic ash conveying system utilizes conveying gas to transport materials from each ash hopper to the target location via corresponding conveying branches. Real-time monitoring data consists of operational status data collected by detection devices within the current control cycle, including real-time pressure of each conveying branch, real-time material level of each ash hopper, and current valve opening of each conveying branch. A correlation is established between the corresponding conveying branches and ash hoppers, serving as input data for subsequent stability margin calculations, ash conveying urgency calculations, global control mode determination, and valve control.

[0022] Specifically, the real-time pressure of each ash conveying branch is collected by the corresponding pressure detection device, the real-time material level of each ash hopper is collected by the corresponding material level detection device, and the current valve opening of each ash conveying branch is obtained from the feedback signal of the valve positioner or the current execution status saved by the controller. The above data are collected within the same control cycle, and a data index can be established according to the ash conveying branch number, ash hopper number, and sampling time t to ensure that the pressure data, valve opening data, and material level data of the connected ash hoppers of the same ash conveying branch remain consistent.

[0023] It should be noted that by using the above method, subsequent stability assessment, demand assessment and valve control are based on the status information of the same control cycle, reducing the status mismatch caused by asynchronous updates of different detection devices.

[0024] S2: Calculate the stability margin of the pneumatic ash conveying system based on real-time monitoring data.

[0025] Among them, the stability margin is a quantitative indicator used to characterize the current overall operating status of the pneumatic ash conveying system, which is determined based on the real-time pressure correlation between each ash conveying branch. It is obtained by calculating the real-time pressure of each ash conveying branch through discretization order calculation, symmetric weight matrix construction, weighted Laplace matrix construction, and matrix operation, and is used as the input parameter for ash conveying urgency calculation and global control mode determination.

[0026] Specifically, the n ash conveying branches are considered as n state nodes, and the node association strength is determined based on the real-time pressure difference between any two ash conveying branches. When the pressure difference between multiple branches is small, the corresponding association weight is large. When the pressure of some branches deviates from that of other branches, the corresponding association weight decreases. Thus, the stability of the system is characterized by the pressure synchronization relationship of multiple branches and the overall coupling state.

[0027] Furthermore, this step employs a deterministic calculation process based on spectral relationships, including sequentially connected pressure difference discretization, correlation weight construction, Laplace matrix construction, and stability margin calculation, without involving the sample training phase of the machine learning model.

[0028] In one possible implementation, S2 specifically includes sub-steps S201 to S206: S201: Based on the pressure difference between the real-time pressures of any two different ash conveying branches in the real-time monitoring data and the preset effective resolution of the pressure sensor, determine the discretization order between the corresponding two ash conveying branches.

[0029] Specifically, the absolute value of the real-time pressure difference between any two ash conveying branches is used as the basic quantity for the degree of pressure difference between the branches. The effective resolution of the pressure sensor is used as the discretization scale, and the division position of the discrete interval is adjusted by a preset correction amount. Then, the result is rounded down to map the continuous pressure difference into an integer discretization order. Therefore, the discretization order between the i-th ash conveying branch and the j-th ash conveying branch can be determined according to the following formula:

[0030] Where, q ij p represents the discretization order between the i-th and j-th ash conveying branches. i (t) represents the real-time pressure of the i-th ash conveying branch at time t, p j (t) represents the real-time pressure of the j-th ash conveying branch at time t, δ p Λ represents the preset effective resolution of the pressure sensor, and Λ represents the preset correction amount for the discretization order.

[0031] Specifically, the above formula uses the effective resolution δ of the pressure sensor. p As the minimum reliable pressure change scale, the real-time pressure difference between the two branches is made dimensionless, and the discretization offset near the critical value is compensated by a preset correction amount Λ, thereby reducing the impact of small fluctuations below the sensor's recognizable scale on the subsequent weight construction.

[0032] For example, when the effective resolution of the pressure sensor is 0.5 kPa, the pressure difference between the two branches is 1.2 kPa, and the preset correction Λ is 1, the discretization order is 3 according to the above formula. The preset effective resolution of the pressure sensor and the preset correction can be set according to actual needs, and this invention does not limit them.

[0033] It should be noted that the above discretization process converts the continuous pressure simulation quantity into a discrete pressure difference index, and provides a discretized input for determining the subsequent branch association weights.

[0034] S202: Based on the discretization order, construct a symmetric weight matrix through double factorial operation.

[0035] Specifically, after obtaining the discretization order between any two ash conveying branches, a nonlinear attenuation relationship is constructed using a double factorial ratio, converting the discretization order into corresponding basic correlation weights. The basic correlation weights are larger when the discretization order is smaller, and gradually decrease as the discretization order increases, characterizing the fundamental impact of real-time pressure differences on the branch correlation relationship. Since the absolute value of the pressure difference between the two ash conveying branches is symmetrical, the corresponding basic correlation weights are also symmetrical.

[0036] For any two different ash conveying branches, a double factorial operation is performed based on the corresponding discretization order, and the ratio of the odd-numbered double factorial result to the even-numbered double factorial result is determined as the basic association weight. For the same ash conveying branch itself, the corresponding basic association weight is set to 0.

[0037] Furthermore, the correlation is corrected based on the basic correlation weights between each ash conveying branch and the pre-determined branch correlation correction coefficients to obtain the final correlation weights. The branch correlation correction coefficients characterize the branch correlation features formed by the actual conveying connection relationships and historical operating states, excluding the current real-time pressure difference, and are pre-determined based on the actual conveying connection relationships of the pneumatic ash conveying system and the historical operating data of the corresponding ash conveying branches. Based on the above relationships, the elements in the symmetric weight matrix can be determined according to the following formula:

[0038] Among them, w ij The element in the i-th row and j-th column of the symmetric weight matrix is ​​represented by ! !, where ! represents the double factorial operator.

[0039] Specifically, the association is corrected based on the basic association weight between the i-th ash conveying branch and the j-th ash conveying branch and the corresponding branch association correction coefficient, to obtain the final association weight w between the i-th ash conveying branch and the j-th ash conveying branch. ij Because the relationship between the two ash conveying branches is symmetrical, therefore w ij =w ji For the same ash conveying branch itself, no connection relationship is established between branches, and the corresponding final association weight is set to 0. A symmetric weight matrix is ​​constructed based on the final association weights between each pair of ash conveying branches.

[0040] It should be noted that the double factorial ratio is used to characterize the basic attenuation effect of the discretization order on the degree of branch correlation, while the branch correlation correction coefficient is used to correct the basic attenuation relationship by combining the actual conveying connection relationship and historical operating characteristics of the pneumatic ash conveying system. Therefore, the final correlation weight is a comprehensive correlation degree characterization obtained after the basic correlation weight is corrected by the branch correlation, rather than a direct output of the double factorial ratio corresponding to the discretization order.

[0041] Figure 2 This invention provides a weight distribution diagram of the correlation between various ash conveying branches.

[0042] Specifically, Figure 2 Both the horizontal and vertical axes represent ash conveying branches, including branches 1 to 6. The values ​​in the matrix cells represent the final association weights between corresponding ash conveying branches, obtained through the basic association weight determination and association correction process described above. The association weight scale on the right indicates the magnitude of the final association weights, and dashed boxes mark areas of weak association. Regarding inclusion relationships... Figure 2 This includes ash conveying branches, the final association weights between branches, the association weight scale, and local weak association regions. In terms of connectivity, each ash conveying branch is paired to form a branch pair, and each branch pair corresponds to a final association weight, collectively forming a symmetric weight matrix. Specifically, branch 4 forms weak connections with branches 5 and 6, corresponding to final association weights of 0.28 and 0.31 respectively. The final association weight between branches 5 and 6 is 0.82.

[0043] It should be noted that, Figure 2 The values ​​shown are the final association weights obtained after the basic association weights are corrected by the corresponding branch associations, and are not the direct calculation results of the double factorial ratio. Figure 2 The final correlation weights intuitively represent the overall correlation strength between different ash conveying branches, providing a data foundation for subsequent processing based on symmetric weight matrices.

[0044] S203: Construct a diagonal matrix based on the sum of the off-diagonal elements in each row of the symmetric weight matrix.

[0045] The diagonal matrix is ​​a square matrix of the same order as the symmetric weight matrix. The (i)th diagonal element of the diagonal matrix is ​​obtained by summing all the off-diagonal elements in the (i)th row of the symmetric weight matrix. The remaining off-diagonal elements are set to 0. Each diagonal element is used to characterize the total correlation weight between the corresponding ash-carrying branch and other ash-carrying branches, and serves as the input for constructing the weighted Laplace matrix.

[0046] S204: Construct a weighted Laplace matrix based on the difference between the diagonal matrix and the symmetric weight matrix.

[0047] The weighted Laplace matrix is ​​obtained by subtracting the symmetric weight matrix from the diagonal matrix. The rows and columns of the matrix correspond to each ash conveying branch, and are used to uniformly represent the correlation weights between each ash conveying branch and the total correlation weights corresponding to each ash conveying branch in matrix form, and serve as the basis matrix for subsequent stability margin calculation.

[0048] S205: Add the weighted Laplace matrix to a square matrix of the same order with all elements equal to 1, to obtain the sum matrix.

[0049] Among them, the same-order all-one square matrix is ​​a square matrix with the same number of rows and columns as the weighted Laplace matrix and all matrix elements are 1. The sum matrix is ​​obtained by adding the corresponding matrix elements in the weighted Laplace matrix and the same-order all-one square matrix item by item, and is used to calculate the determinant value corresponding to the stability margin.

[0050] S206: Calculate the stability margin of the pneumatic ash conveying system based on the determinant value of the summed matrix and the trace of the weighted Laplace matrix.

[0051] Specifically, to convert the pressure correlation information between multiple ash conveying branches into a quantitative indicator characterizing the overall operating state of the system, the determinant of the weighted Laplace matrix plus a square matrix of the same order is used to characterize the overall structural features formed by the correlation weights of each ash conveying branch. The trace of the weighted Laplace matrix is ​​used to characterize the overall scale formed by the total correlation weights of each ash conveying branch, and the stability margin is determined by the ratio of the two. Therefore, the stability margin of the pneumatic ash conveying system can be determined according to the following formula:

[0052] Where Γ represents the stability margin of the pneumatic ash conveying system, and L W Denotes the weighted Laplace matrix, 1 n×n The weighted Laplace matrix L represents... W An n′×n′ square matrix of the same order with all elements equal to 1, where n′ represents the total number of ash conveying branches, det() calculates the determinant of the matrix, and tr() calculates the trace of the matrix.

[0053] Specifically, a diagonal matrix is ​​constructed by summing the off-diagonal elements of each row of the symmetric weight matrix, and the weighted Laplacian matrix L is obtained from the difference between the diagonal matrix and the symmetric weight matrix. W Then add it to a square matrix of the same order of all 1s to obtain the sum matrix, and calculate the stability margin Γ according to the above formula.

[0054] From the perspective of spectral relationships, the weighted Laplace matrix is ​​used to characterize the interconnected structure of a multi-branch network. The determinant value reflects the overall topological characteristics, and the trace reflects the overall scale of the node weighting degree. The ratio of the two is used to comprehensively quantify the degree of synchronization stability under multi-branch coupling conditions. When the pressure on each branch is relatively coordinated, the stability margin Γ remains at a high level. When some branches experience significant pressure deviations and disrupt the synchronization relationship between branches, the stability margin Γ decreases accordingly.

[0055] Through the above processing, the real-time pressure difference of multiple branches is successively converted into discretization order, branch association weight and weighted Laplace matrix, and the stability margin is determined by combining the matrix determinant and trace. This expands the system stability state from the judgment of single branch pressure to the overall quantification of the pressure association state of multiple branches, providing unified state parameters for subsequent ash conveying urgency calculation and global control mode determination.

[0056] In this embodiment of the invention, the real-time pressure difference between different ash conveying branches is discretized by the effective resolution of the pressure sensor, and a symmetric weight matrix that reflects the pressure correlation strength of the branches is constructed by using double factorial operation. On this basis, a diagonal matrix and a weighted Laplace matrix are constructed in sequence, and the stability margin is determined by combining the matrix determinant and trace. This expands the judgment of the system stability state from single branch pressure monitoring to the overall quantification of the pressure correlation state of multiple branches, which can improve the ability to identify pressure imbalance and potential instability of multiple branches.

[0057] S3: Calculate the urgency of ash conveying, which reflects the urgency of conveying each ash hopper.

[0058] Among them, the urgency of ash conveying is an indicator used to quantify the current conveying demand of the corresponding ash hopper, which is determined based on the real-time material level of each ash hopper, the material level distribution status of the ash hopper group, and the stability margin. The urgency of ash conveying of each ash hopper is associated with the corresponding ash hopper and serves as an input parameter for determining the global control mode, dynamic limit, and dynamic maximum step size.

[0059] Specifically, the urgency of ash conveying is used to dynamically assess the priority level at which each ash hopper needs to be conveyed at the current moment. This indicator is not determined solely by the material level in a single ash hopper, but rather by comprehensively considering the material level distribution characteristics of the ash hopper group and the aforementioned system stability margin.

[0060] For example, when the overall system stability margin is low, even if the material level in a certain ash hopper is high, its urgency for conveying ash may be suppressed to prevent forced conveying under highly unstable conditions from exacerbating system risks. Conversely, when the system is stable, the urgency of high-level ash hoppers will significantly increase. This mechanism ensures that the assessment of conveying demand can adaptively adjust according to the system's health condition, achieving a dynamic balance between safety and efficiency.

[0061] In one possible implementation, S3 specifically includes sub-steps S301 to S306: S301: Determine the median and standard deviation of the material level based on the real-time material level of each ash hopper in the real-time monitoring data.

[0062] Among them, the real-time material level is the material level of the corresponding ash hopper collected by the material level detection device in the current control cycle; the median material level is the statistical value of the middle position determined after sorting the real-time material levels of all ash hoppers in the current control cycle according to their numerical values; and the standard deviation of the material level is a statistical value calculated based on the dispersion of the real-time material levels of all ash hoppers relative to their average material level. The two are used together to characterize the material level distribution status of the ash hopper group in the current control cycle.

[0063] S302: Based on the standard deviation of the material level and the preset effective resolution of the material level sensor, the weighting coefficient is determined through double factorial operation.

[0064] Among them, the effective resolution of the level sensor is a parameter that is preset based on the smallest level change that the level sensor can effectively distinguish. The weighting coefficient is an adjustment parameter obtained by taking the level standard deviation and the level change scale characterized by the effective resolution of the level sensor as input and performing a double factorial operation. It is used to adjust the influence of the level standard deviation on the adaptive threshold in the subsequent adaptive threshold calculation.

[0065] Specifically, after obtaining the standard deviation of the material level in all hoppers within the current control cycle, the standard deviation is compared with the preset effective resolution of the material level sensor to determine the significance of the current material level dispersion of the hopper group relative to the minimum effective resolution of the material level sensor. The scale comparison result is discretized to convert the continuously changing material level dispersion into a non-negative integer order for double factorial operations.

[0066] Furthermore, based on the obtained non-negative integer orders, double factorial operations are performed on the odd-numbered terms corresponding to the non-negative integer orders to obtain the odd double factorial result, and double factorial operations are performed on the even-numbered terms corresponding to the non-negative integer orders to obtain the even double factorial result. The odd double factorial result is used as the numerator, and the even double factorial result is used as the denominator; the ratio between the two is determined as the weighting coefficient.

[0067] It should be noted that by first scaling the standard deviation of the material level using the effective resolution of the material level sensor, and then performing a double factorial operation, the impact of small material level fluctuations within the effective resolution range of the material level sensor on the subsequent adaptive threshold determination process can be reduced, so that the weighting coefficients mainly reflect the discrete state of the material level of the ash hopper group that can be effectively identified by the material level detection device.

[0068] S303: Combine the median level, standard deviation of level, and weighting coefficients to determine the adaptive threshold through permutation calculation.

[0069] Specifically, to ensure that the criterion for determining the ash hopper level changes with the actual level distribution of the current ash hopper group, rather than using a fixed level threshold, this embodiment of the invention uses the median of the real-time levels of all ash hoppers as the base position for the adaptive threshold, and uses the standard deviation of the level to characterize the dispersion of the real-time level of each ash hopper relative to the center position of the group. Based on this, a weighting coefficient is used to adjust the influence of the level dispersion on the threshold, and a permutation ratio term related to the number of ash hoppers is introduced, so that the dispersion correction amount under different ash hopper number configurations can be adjusted accordingly according to the system scale. Therefore, the adaptive threshold simultaneously includes group center level information, level dispersion information, and ash hopper number information, and can dynamically change with the level distribution of the current control cycle. Therefore, the adaptive threshold can be determined according to the following formula:

[0070] Where θ represents the adaptive threshold, L med This represents the median real-time material level in all ash hoppers during the current control cycle. Indicates the weighting coefficient. σ represents the number of permutations of selecting 2 ash hoppers from N ash hoppers. L This represents the real-time standard deviation of the material level in all ash hoppers within the current control cycle.

[0071] Specifically, the median level L is first determined based on the real-time material level in each ash hopper. med and material level standard deviation σ L These two statistics represent the central material level and dispersion of the ash hopper group at the current moment, respectively. Next, based on the standard deviation of the material level and the preset effective resolution of the material level sensor, weighting coefficients are determined through a double factorial operation. This coefficient serves a similar purpose to the aforementioned discretization order, eliminating the disturbance of level measurement noise on the threshold calculation. Finally, by combining the median level, the standard deviation of the level, and the weighting coefficients, the adaptive threshold is determined using a formula.

[0072] Furthermore, we introduce the permutation of terms. The significance lies in enabling the threshold to adaptively scale with the number and size of ash hoppers, avoiding the problem of poor applicability of fixed coefficients under different system configurations. This adaptive threshold is no longer a static empirical value, but a statistical boundary that dynamically drifts with the distribution of material levels in the group, and can accurately distinguish between "normal material level" and "high material level" under the current working conditions.

[0073] It should be noted that this optional implementation dynamically generates the material level determination benchmark through statistical features, and adjusts the shape of the urgency calculation curve in combination with the system's stable state, so that the conveying priority can adaptively drift with the distribution of material levels in the group and the health status of the system.

[0074] S304: Determine the first shape factor and the second shape factor based on the stability margin using sine and cosine functions.

[0075] The first shape factor α and the second shape factor β both use the stability margin Γ as input, and are used to adjust the shape of the ash conveying urgency curve in the low-level and high-level material zones, respectively. α is used to control L h When ≤θ, the rate at which urgency increases with material level change; β is used to control L. h When the urgency level approaches θ, the rate of change of the urgency level is close to that of the high urgency level range.

[0076] Specifically, the first shape factor and the second shape factor are used to dynamically adjust the shape of the ash conveying urgency curve based on the stability margin of the pneumatic ash conveying system. The first shape factor is determined using a cosine function corresponding to the stability margin, and the second shape factor is determined using a sine function corresponding to the stability margin, so that the first shape factor decreases as the stability margin increases, and the second shape factor increases as the stability margin increases. Based on the above mapping relationship between the stability margin and the first and second shape factors, the first and second shape factors are determined by the following formulas:

[0077]

[0078] Where α represents the first shape factor, β represents the second shape factor, Γ represents the stability margin of the pneumatic ash conveying system, π represents pi, sin represents the sine function, and cos represents the cosine function.

[0079] Specifically, the above formula uses the stability margin Γ as a common adjustment variable for the two shape factors. Within the preset value range of the stability margin Γ, as the stability margin Γ increases, the first shape factor α gradually decreases, and the second shape factor β gradually increases. Conversely, as the stability margin Γ decreases, the first shape factor α gradually increases, and the second shape factor β gradually decreases. Through this complementary relationship, the first and second shape factors can respectively adjust the changing characteristics of the ash conveying urgency curves on both sides of the adaptive threshold, thereby dynamically adjusting the ash conveying urgency according to the system's stable state.

[0080] For example, when the stability margin Γ = 0.6, the first shape factor is α = 1 + cos(0.3π) ≈ 1.588, and the second shape factor is β = 1 + sin(0.3π) ≈ 1.809. Therefore, the calculated first and second shape factors can be substituted into the piecewise calculation relationship of the subsequent ash conveying urgency to adjust the response characteristics of ash conveying urgency with real-time material level changes within different material level ranges.

[0081] It should be noted that by using sine and cosine functions driven by the same stability margin to determine the two shape factors, the piecewise nonlinear mapping of the urgency of subsequent ash conveying can be dynamically adjusted according to the overall stable state of the pneumatic ash conveying system, thereby improving the matching degree between the conveying demand assessment and the current system operating state.

[0082] S305: Compare the real-time material level of each ash hopper with the adaptive threshold to obtain the material level comparison result.

[0083] The material level comparison result is a judgment result formed by comparing the corresponding real-time material level with the adaptive threshold for each ash hopper. It includes a first comparison result where the real-time material level is less than or equal to the adaptive threshold and a second comparison result where the real-time material level is greater than the adaptive threshold. This result is used to select the corresponding ash conveying urgency calculation interval.

[0084] S306: Based on the material level comparison results, combined with the first shape factor, the second shape factor, and the preset upper limit of the safe material level in the ash hopper, calculate the ash conveying urgency, which reflects the urgency of conveying in each ash hopper.

[0085] Among them, the upper limit of the safe material level of the ash hopper is a material level upper limit parameter that is preset according to the safe material storage range allowed by the ash hopper. The urgency of ash conveying is determined by the calculation relationship corresponding to the first shape factor for the first comparison result, and the urgency of ash conveying is determined by the calculation relationship corresponding to the second shape factor and the upper limit of the safe material level of the ash hopper for the second comparison result, so that each ash hopper obtains the urgency of ash conveying corresponding to its real-time material level.

[0086] It should be noted that those skilled in the art can set the size of the preset upper limit of the safety level of the ash hopper according to actual needs, and this invention does not limit it.

[0087] In one possible implementation, S306 specifically includes: Based on the material level comparison results, determine whether the real-time material level of each ash hopper is less than or equal to the adaptive threshold. If so, calculate the ash conveying urgency of the corresponding ash hopper based on the real-time material level, the adaptive threshold, and the first shape factor. Otherwise, calculate the ash conveying urgency of the corresponding ash hopper based on the real-time material level, the preset upper limit of the ash hopper's safe material level, and the second shape factor.

[0088] Specifically, to ensure that the urgency of ash conveying reflects both the real-time level of the ash hopper relative to the current group level benchmark and enhances the representation of conveying demand when the ash hopper level approaches the upper limit of the safe level, this embodiment of the invention establishes a piecewise nonlinear mapping relationship for the urgency of ash conveying using an adaptive threshold θ as the segment boundary. When the real-time level of the h-th ash hopper is not greater than the adaptive threshold, the ratio of the real-time level to the adaptive threshold is used as the normalization input, and the change characteristics of the urgency within this interval are adjusted using a first shape factor. When the real-time level is greater than the adaptive threshold, another nonlinear mapping relationship is constructed by combining the upper limit of the safe level of the ash hopper and a second shape factor, so that the urgency of ash conveying changes accordingly as the level approaches the upper limit of the safe level. Based on the above piecewise mapping principle, the urgency of ash conveying in the h-th ash hopper can be determined according to the following formula:

[0089] Among them, D h L represents the urgency of ash conveying in the h-th ash hopper. h L represents the real-time material level in the h-th ash hopper. max This indicates the preset upper limit of the safe material level in the ash hopper.

[0090] Specifically, to ensure that the urgency of ash conveying can reflect both the real-time position of the ash hopper level relative to the current group level benchmark and enhance the representation of conveying demand when the ash hopper level approaches or reaches the upper limit of the safe level, this embodiment of the invention uses an adaptive threshold θ and a preset upper limit of the safe level in the ash hopper L. max As the segment boundary, a piecewise nonlinear mapping relationship for the urgency of ash conveying is established.

[0091] When the real-time material level L in the h-th ash hopper h When the real-time material level L is less than or equal to the adaptive threshold θ, h The ratio of the adaptive threshold θ is used as a normalization input, and the first shape factor α is used to adjust the change characteristics of the ash conveying urgency in this range, so that the ash conveying urgency gradually increases from the low urgency range to the preset intermediate urgency as the real-time material level rises.

[0092] When the real-time material level L in the h-th ash hopper h The level is greater than the adaptive threshold θ and less than the preset upper limit of the ash hopper safety level L. max At that time, the normalized input is constructed based on the positional relationship between the real-time material level and the adaptive threshold and the upper limit of the safe material level in the ash hopper. The change characteristics of the ash conveying urgency within this range are adjusted by the second shape factor β, so that the ash conveying urgency increases continuously from the intermediate urgency and approaches 1 as the real-time material level approaches the upper limit of the safe material level in the ash hopper.

[0093] When the real-time material level of the h-th ash hopper The upper limit of the preset safe material level in the ash hopper is greater than or equal to L.max When the urgency of ash conveying in the corresponding ash hopper is set to 1, it indicates that the ash hopper is at the highest urgency level.

[0094] By using the above segmented settings, the urgency of ash conveying remains continuous at the adaptive threshold θ, and is limited to a preset range. Simultaneously, saturation processing is applied when the real-time material level reaches or exceeds the upper limit of the ash hopper's safe level, preventing the base of non-integer exponentiation from becoming negative after exceeding the safe level limit, thus avoiding the calculation result from losing its real-valued meaning.

[0095] For example, when the system has a high stability margin, α is small, making the curve convex. Even if the material level does not reach the threshold, a certain conveying priority can be maintained, promoting the emptying of the ash hopper. When the system is unstable, α increases (e.g., 2.0), and the curve becomes concave, significantly reducing the urgency of low-level ash hoppers and avoiding ineffective conveying that occupies system resources. In the high-level zone ( The second shape factor, β, dominates the degree of urgency as the system approaches the safety upper bound. When the system is unstable, an increase in β slows down the increase in urgency with rising material levels, preventing system overload and collapse due to multiple high-level hoppers simultaneously vying for conveying rights. Conversely, when the system is stable, a decrease in β causes the urgency to saturate rapidly, ensuring that high-level hoppers are prioritized for handling.

[0096] Through this nonlinear mapping that is modulated in real time by a stable margin, the urgency of ash conveying not only reflects the material storage status of the ash hopper itself, but also embeds adaptive protection logic for the overall health of the system, fundamentally solving the technical problem that traditional fixed threshold methods cannot balance efficiency and safety under complex working conditions.

[0097] It should be noted that this optional implementation adopts a piecewise nonlinear mapping mechanism, applying a shape factor modulated by stability margin in the low material level zone and the high material level zone respectively, thereby realizing differentiated sensitive response of conveying urgency under different working conditions.

[0098] In this embodiment of the invention, the median and standard deviation of the material level are determined by the real-time material level of each ash hopper. An adaptive threshold is dynamically determined by combining the effective resolution of the material level sensor, the number of ash hoppers, and the material level distribution status. At the same time, the first shape factor and the second shape factor are determined by using the stability margin. The urgency of ash conveying in each ash hopper is nonlinearly calculated using the adaptive threshold as the segment boundary. This allows the urgency of ash conveying to reflect the material storage status of a single ash hopper, the material level distribution of the ash hopper group, and the overall stability of the system, thereby improving the adaptability of the conveying demand evaluation to different operating conditions.

[0099] S4: Based on real-time monitoring data, determine the rated pressure deviation of each ash conveying branch, and determine the global control mode based on stability margin, urgency of each ash conveying branch, and rated pressure deviation.

[0100] The rated pressure deviation is used to characterize the degree of deviation of the real-time pressure of each ash conveying branch from the rated working pressure of the corresponding branch. The global control mode is determined jointly based on the overall operating status characterized by the stability margin, the ash hopper conveying demand characterized by the ash conveying urgency, and the branch pressure status characterized by the rated pressure deviation, and serves as the basis for selecting the corresponding fuzzy rule base when calculating the initial valve opening increment.

[0101] By comprehensively considering stability margin, ash conveying urgency, and rated pressure deviation, a global control mode corresponding to the current operating state is determined from the preset control strategy to take into account the overall stability of the system, conveying demand, and local pressure anomaly risks.

[0102] Optionally, the global control modes include emergency blockage relief mode, energy-saving optimization mode, and normal adjustment mode.

[0103] The emergency blockage relief mode is used when the risk of blockage propagation or the urgency of ash conveying reaches the corresponding trigger conditions. The energy-saving optimization mode is used when the risk of blockage propagation is in the low-risk range and the urgency of ash conveying meets the corresponding conditions. In all other cases, the conventional adjustment mode is used. Each of the three control modes is configured with a corresponding fuzzy rule base. The emergency blockage relief mode focuses on quickly eliminating blockage risk, the energy-saving optimization mode focuses on reducing gas consumption and equipment wear, and the conventional adjustment mode is used to maintain standard conveying capacity.

[0104] In one possible implementation, S4 specifically includes sub-steps S401 to S406: S401: Determine the rated pressure deviation of each ash conveying branch based on the real-time pressure of each ash conveying branch in the real-time monitoring data and the preset rated working pressure of the branch.

[0105] The rated working pressure of the branch is a pressure benchmark set in advance based on the design operating pressure of the corresponding ash conveying branch. The rated pressure deviation is calculated by the difference between the real-time pressure of the corresponding ash conveying branch and the rated working pressure of the branch, and a one-to-one correspondence is established with the corresponding ash conveying branch.

[0106] It should be noted that those skilled in the art can set the preset rated working pressure of the branch according to actual needs, and this invention does not limit this.

[0107] S402: Identify the branches with an absolute value of rated pressure deviation that is greater than the effective resolution of the pressure sensor, and count the number of branches with excessive tolerance.

[0108] Among them, the ash conveying branches whose absolute value of rated pressure deviation is greater than the effective resolution of the pressure sensor are identified as out-of-tolerance branches. The number of out-of-tolerance branches is counted by traversing each ash conveying branch to characterize the scale of ash conveying branches that reach the pressure out-of-tolerance condition in the current control cycle.

[0109] S403: Sort the absolute values ​​of rated pressure deviations in the number of branches with out-of-tolerance ratings in descending order to obtain a descending sequence of absolute values ​​of rated pressure deviations.

[0110] Specifically, the absolute value of the rated pressure deviation for each out-of-tolerance branch is obtained and arranged in descending order to obtain a descending sequence of absolute values ​​of rated pressure deviation. Each element in the sequence corresponds to an out-of-tolerance branch and serves as the input of the pressure deviation amplitude for the blockage propagation risk operator. The first element of the descending sequence is the maximum absolute value of the rated pressure deviation, and subsequent elements decrease sequentially. When there are identical absolute values ​​of rated pressure deviation, they are arranged according to the preset numbering order of the corresponding ash conveying branches.

[0111] By sorting in the above way, a definite correspondence is established between the absolute values ​​of different rated pressure deviations and the corresponding summation terms in the blockage propagation risk operator, thus avoiding inconsistencies in calculation results due to different sorting methods.

[0112] S404: Based on the number of out-of-tolerance branches, the total number of ash conveying branches, the absolute value sequence of rated pressure deviation, and the preset system design conveying pressure, the blockage propagation risk operator is determined through combination number calculation and permutation number calculation.

[0113] The system design conveying pressure is a pre-set conveying pressure scale, which can be set according to actual needs; this invention does not limit this setting. The blockage propagation risk operator integrates the number of out-of-tolerance branches, the total number of ash conveying branches, and the rated pressure deviation amplitude of each out-of-tolerance branch, and normalizes them using the system design conveying pressure to characterize the comprehensive blockage propagation risk when multiple branches experience pressure anomalies simultaneously.

[0114] Specifically, a weighted term related to the number of out-of-tolerance branches and the system branch scale is formed by the ratio of the number of combinations to the number of permutations. Different orders of abnormal combinations are accumulated using alternating signs to reduce the accumulation of risk caused by repeated inclusion of abnormal combinations. Furthermore, the absolute value of the rated pressure deviation is normalized using the system design delivery pressure. Therefore, the blockage propagation risk operator can be determined according to the following formula:

[0115] Where Ψ represents the congestion propagation risk operator, m represents the number of out-of-tolerance branches, C(m,k) represents the number of combinations of selecting k out-of-tolerance branches from m out-of-tolerance branches, and P(n,k) represents the number of permutations of selecting k ash conveying branches from n ash conveying branches. p represents the k-th absolute value of the rated pressure deviation in the sequence of absolute values ​​of rated pressure deviation. ref This indicates the preset system design delivery pressure.

[0116] Specifically, the construction of this formula has clear physical and statistical significance: the ratio of the combination number C(m,k) to the permutation number P(n,k) represents the probability weight of simultaneously causing out-of-range errors in k randomly selected branches from n branches, reflecting the possibility of concurrent faults in a multi-branch coupled system. (Interleaved summation term) This introduces the concept of inclusion-exclusion principle, avoiding the overestimation of risk caused by simple superposition. By normalizing the pressure deviation, the risk operator becomes a dimensionless relative indicator. Therefore, the blockage propagation risk operator not only considers the number of abnormal branches but also the distribution structure of the degree of abnormality, which can more accurately characterize the overall blockage propagation trend of the system than a single maximum pressure value.

[0117] It should be noted that this optional implementation achieves adaptive switching from local pressure anomaly to global control mode by constructing a composite risk operator that includes the number of out-of-tolerance branches and the magnitude of the deviation, and combining it with the risk threshold boundary dynamically generated by the stability margin.

[0118] S405: Based on the preset maximum allowable continuous pressure deviation and stability margin of the system, determine the lower and upper bounds of the risk threshold using a sine function.

[0119] The maximum allowable continuous pressure deviation of the system is a pressure parameter preset according to the allowable continuous operating pressure deviation range of the pneumatic ash conveying system; this invention does not limit this parameter. The lower and upper bounds of the risk threshold are determined using this pressure parameter as a scale and stability margin as a dynamic adjustment input, through a preset sine function relationship, jointly dividing the mode determination interval of the blockage propagation risk operator.

[0120] The lower and upper bounds of the risk threshold change dynamically with the stability margin: when the stability margin is high, the system has a stronger ability to resist disturbances, and the risk assessment range is correspondingly widened. When the stability margin decreases, the risk assessment range is correspondingly narrowed, making the system more likely to enter a higher-risk control mode, thus adapting the risk assessment criteria to the current stable state of the system.

[0121] S406: Determine the global control mode of the pneumatic ash conveying system based on the maximum value among the blockage propagation risk operator, the lower bound of the risk threshold, the upper bound of the risk threshold, and the urgency of ash conveying in each ash hopper.

[0122] The maximum ash conveying urgency is obtained by comparing the ash conveying urgency of all ash hoppers in the current control cycle. The blockage propagation risk operator is compared with the lower and upper bounds of the risk threshold, respectively. Then, combined with the preset judgment conditions corresponding to the maximum ash conveying urgency, the global control mode of the current control cycle is determined from the emergency blockage relief mode, energy-saving optimization mode, and normal adjustment mode.

[0123] Specifically, when the blockage propagation risk operator exceeds the upper limit of the risk threshold, or the maximum ash conveying urgency reaches a preset extremely high limit, the system is designated as emergency blockage removal mode. When the blockage propagation risk operator is below the lower limit of the risk threshold, and the maximum ash conveying urgency is at a low level, the system is designated as energy-saving optimization mode. All other situations are designated as normal adjustment mode. The preset extremely high limit can be set according to actual needs; this invention does not limit it.

[0124] The aforementioned joint determination allows both the risk of congestion and the actual transportation demand to participate in the mode selection, so as to take into account both the risk of abnormal local pressure and the overall transportation demand.

[0125] Reference manual attached Figure 3 The diagram illustrates the relationship between a blockage propagation risk operator, a lower risk threshold, and an upper risk threshold as a function of time, and the determination of these parameters, provided by an embodiment of the present invention.

[0126] Specifically, Figure 3 The horizontal axis represents time per minute, and the vertical axis represents dimensionless values. The blue solid line and data points represent the congestion propagation risk operator, the orange dashed line represents the lower limit of the risk threshold, the green dotted line represents the upper limit of the risk threshold, and the vertical dashed line represents the mode switching determination time. The graph also marks the entry into the emergency congestion clearing determination zone. Regarding the inclusion relationships... Figure 3 This includes a congestion propagation risk operator, a lower risk threshold, an upper risk threshold, a mode switching determination time, and an emergency congestion resolution determination zone. In terms of connectivity, the congestion propagation risk operator changes over time and is compared with both the lower and upper risk thresholds. The upper and lower risk thresholds together define the risk determination range. When the congestion propagation risk operator exceeds the upper risk threshold, it enters the emergency congestion resolution determination zone, thus connecting the calculation result of the risk operator with the global control mode determination.

[0127] It should be noted that, Figure 3 By comparing the dynamic risk threshold with the congestion propagation risk operator, an intuitive representation of the local pressure anomaly risk to the global control mode can be achieved, which is conducive to improving the matching degree between the control mode and the current system operating state.

[0128] In this embodiment of the invention, the rated pressure deviation is determined by comparing the real-time pressure of each ash conveying branch with the rated working pressure, and the branches with excessive deviation are screened using the effective resolution of the pressure sensor. Based on the number of branches with excessive deviation, the total number of ash conveying branches, the pressure deviation amplitude, and the system design conveying pressure, a blockage propagation risk operator is determined. At the same time, the risk threshold boundary is dynamically determined according to the stability margin, and the emergency blockage removal mode, energy-saving optimization mode, or conventional adjustment mode is selected in combination with the maximum ash conveying urgency. This allows local pressure anomalies, overall stable state, and actual conveying needs to participate in the control strategy decision-making, improving the pertinence and reliability of global control mode switching under different operating conditions.

[0129] S5: Calculate the initial valve opening increment for each ash conveying branch according to the global control mode.

[0130] Among them, the initial valve opening increment is the valve opening adjustment amount obtained by fuzzy reasoning for each ash conveying branch based on the current global control mode and the control pressure status of the corresponding branch. Each initial valve opening increment is associated with the corresponding ash conveying branch and added to the current valve opening of the corresponding ash conveying branch to form the candidate opening for subsequent safety projection.

[0131] Specifically, this is the initial step in the lower-level execution chain. Different global control modes correspond to different control objectives and response characteristics. Therefore, in this step, the corresponding control algorithm or rule base is invoked according to the determined mode to calculate the valve adjustment amount. For example, in emergency unblocking mode, the calculated initial valve opening increment may focus more on rapid pressure relief or unblocking, allowing for a larger adjustment range. In energy-saving optimization mode, the incremental calculation focuses more on stability and minimizing energy consumption. This allows the valve's basic adjustment command to accurately adapt to the current macroscopic operating conditions.

[0132] In one possible implementation, S5 specifically includes sub-steps S501 to S506: S501: Determine the control pressure deviation of each ash conveying branch based on the real-time pressure of each ash conveying branch and the preset expected branch pressure.

[0133] Among them, the desired branch pressure is a pressure target value preset according to the target conveying condition of the corresponding ash conveying branch. The control pressure deviation is calculated by the difference between the real-time pressure of the corresponding ash conveying branch and the desired branch pressure. It is used to characterize the degree of deviation of the current pressure state of the corresponding ash conveying branch from the target pressure state and serves as one of the input variables of fuzzy control.

[0134] Specifically, the control pressure deviation is the difference between the real-time pressure of each ash conveying branch and the desired branch pressure, reflecting the gap between the current conveying state and the target state. The rate of change of the control pressure deviation is obtained through differential calculation, characterizing the dynamic evolution trend of the deviation. These two variables together constitute the input space of fuzzy control, which can simultaneously capture the static error and dynamic inertia of the system.

[0135] S502: Based on the control pressure deviations of each branch, determine the rate of change of control pressure deviation for the corresponding ash conveying branch through differential calculation.

[0136] Among them, the control pressure deviation change rate is the change amount obtained by differential calculation based on the control pressure deviation between the current control cycle and the previous control cycle of the corresponding ash conveying branch and the control cycle time interval. It is used to characterize the change trend of control pressure deviation with the control cycle and serves as another input variable for fuzzy control.

[0137] It should be noted that when the current control cycle is the first control cycle after the pneumatic ash conveying system is started, since there is no control pressure deviation from the previous control cycle, the control pressure deviation of the corresponding ash conveying branch in the previous control cycle is initialized to the control pressure deviation of the current control cycle, so that the control pressure deviation change rate of the first control cycle is initialized to 0. From the second control cycle onwards, the control pressure deviation change rate of the corresponding ash conveying branch is determined by differential calculation based on the control pressure deviation between the current control cycle and the previous control cycle, as well as the control cycle time interval.

[0138] S503: Based on the global control mode, call the corresponding fuzzy rule library.

[0139] Each fuzzy rule base includes a first input fuzzy set for control pressure deviation, a second input fuzzy set for the rate of change of control pressure deviation, and multiple fuzzy rules consisting of the mapping relationships between the first and second input fuzzy sets and their corresponding rule consequent outputs. Each first input fuzzy set is configured with a corresponding control pressure deviation membership function, each second input fuzzy set is configured with a corresponding control pressure deviation rate of change membership function, and each fuzzy rule is configured with a corresponding rule consequent output center value.

[0140] The fuzzy sets corresponding to the control pressure deviation are divided according to the range of change of the control pressure deviation from negative to positive. The fuzzy sets corresponding to the control pressure deviation change rate are divided according to the range of change of the control pressure deviation from a downward trend to an upward trend. The membership functions corresponding to adjacent fuzzy sets partially overlap within a preset range so that the fuzzy inference results between adjacent working conditions change continuously.

[0141] Emergency unblocking mode, energy-saving optimization mode, and normal adjustment mode are each configured with corresponding fuzzy rule bases. The three fuzzy rule bases use the same type of input variables, but through different membership function parameters, mapping relationships between rule antecedents and rule consequents, and / or rule consequent output center values, different global control modes have different valve regulation characteristics.

[0142] S504: Based on the control pressure deviation and the preset effective resolution of the control pressure deviation, determine the dynamic weight through double factorial operation.

[0143] Among them, the effective resolution of control pressure deviation is a pre-set minimum pressure scale used to distinguish changes in effective control pressure deviation. The dynamic weight is a weight parameter obtained by discretizing the degree of change of the control pressure deviation of the corresponding ash conveying branch relative to the effective resolution of control pressure deviation and performing a double factorial operation. It is used to adjust the influence of the control pressure deviation of the corresponding ash conveying branch in the calculation of fuzzy rule activation degree.

[0144] Specifically, for the i-th ash conveying branch, the absolute value of the control pressure deviation of this branch in the current control cycle is compared with the preset effective resolution of the control pressure deviation to determine the significance of the current control pressure deviation relative to the minimum credible control pressure change scale. The scale comparison result is discretized to obtain the corresponding non-negative integer order.

[0145] Furthermore, based on the obtained non-negative integer orders, the odd-numbered terms corresponding to the non-negative integer orders are subjected to a double factorial operation to obtain an odd double factorial result, and the even-numbered terms corresponding to the non-negative integer orders are subjected to a double factorial operation to obtain an even double factorial result. The odd double factorial result is used as the numerator, and the even double factorial result is used as the denominator; the ratio of the two is determined as the dynamic weight corresponding to the i-th ash conveying branch. Different ash conveying branches determine their corresponding dynamic weights based on their own control pressure deviations, establishing a one-to-one correspondence between the dynamic weights of each ash conveying branch and their respective current pressure deviation degrees.

[0146] When the control pressure deviation approaches the effective resolution of the control pressure deviation, the corresponding dynamic weights reduce the impact of this control pressure deviation on the activation degree of fuzzy rules according to a double factorial relationship. When the control pressure deviation reaches a level relative to the effective resolution that can effectively represent the actual pressure state change, the corresponding dynamic weights allow the control pressure deviation to participate normally in the subsequent rule activation degree calculation. Thus, the dynamic weights become an adjustment parameter between the control pressure deviation and the subsequent fuzzy rule activation degree.

[0147] S505: Combine dynamic weights, membership degrees corresponding to control pressure deviations, and membership degrees corresponding to control pressure deviation change rates to determine the rule activation degree of each fuzzy rule in the fuzzy rule base.

[0148] Specifically, after calling the fuzzy rule base corresponding to the current global control mode, in order to determine the actual contribution of each fuzzy rule to the valve adjustment result within the current control cycle, this embodiment of the invention calculates the membership degree of the control pressure deviation and the rate of change of control pressure deviation to the corresponding fuzzy set, and uses both as the matching degree of the rule antecedent. Simultaneously, to reduce the interference of small pressure deviations close to the effective resolution on the fuzzy inference results, a dynamic weight related to the significance of the current control pressure deviation is introduced to correct the comprehensive matching degree of the rule antecedent. Therefore, only when the control pressure deviation, the trend of control pressure deviation change, and the dynamic weight all satisfy the corresponding conditions can the corresponding fuzzy rule obtain a high activation degree. Therefore, the rule activation degree of the r-th fuzzy rule can be determined according to the following formula:

[0149] Where, ε r ω represents the rule activation of the r-th fuzzy rule.i This represents the dynamic weight corresponding to the i-th ash conveying branch. The control pressure deviation e of the i-th ash conveying branch is represented by... i For the fuzzy set A corresponding to the r-th fuzzy rule r membership degree This represents the rate of change of control pressure deviation for the i-th ash conveying branch. For the fuzzy set B corresponding to the r-th fuzzy rule r membership degree, e i This represents the control pressure deviation of the i-th ash conveying branch. Let μ represent the rate of change of control pressure deviation for the i-th ash conveying branch, and let μ() represent the membership function.

[0150] Specifically, the formula employs a product-type fuzzy conjunctive approach, combining static deviation membership, dynamic rate of change membership, and dynamic weights as the rule's reliability. The product operation causes a simultaneous decrease in activation when any input does not match the corresponding rule, while ω... i This can further reduce low confidence bias close to the effective resolution, thereby reducing rule mis-triggering caused by measurement noise while maintaining the continuity of fuzzy inference.

[0151] Furthermore, based on the activation degree of each rule, the weighted average of the output center values ​​of the rule consequents of the fuzzy rules is calculated to obtain the preliminary valve opening increment for each ash conveying branch. By introducing dynamic weights to correct traditional fuzzy inference, this embodiment makes the valve adjustment amount depend not only on the magnitude of the deviation but also on the reliability of the deviation, thereby improving the robustness of control under critical conditions.

[0152] It should be noted that this optional implementation introduces dynamic weights related to the deviation resolution to correct the fuzzy inference process, enabling the valve adjustment amount to be finely adapted to the operating conditions under different global control modes.

[0153] S506: Based on the activation degree of each rule, the output center value of the rule consequent of the fuzzy rule is weighted and averaged to obtain the preliminary valve opening increment of each ash conveying branch.

[0154] Among them, the output center value of the rule consequent is the valve opening increment representative value pre-configured in each fuzzy rule. Each rule consequent output center value is associated with the corresponding rule activation degree. The rule activation degree is used as the weight to perform a weighted average of the output center values ​​of the rule consequent of all activated rules. The resulting weighted average value is used as the initial valve opening increment of the corresponding ash conveying branch in the current control cycle.

[0155] Specifically, the activation degree of each fuzzy rule in the currently invoked fuzzy rule library is multiplied by the output center value of the corresponding rule consequent, and the products are accumulated. Simultaneously, the activation degrees of all fuzzy rules in the currently invoked fuzzy rule library are accumulated. When the accumulated activation degrees of all fuzzy rules are greater than 0, the accumulated product is divided by the accumulated activation degrees of each rule; the resulting ratio is the initial valve opening increment for the corresponding ash conveying branch.

[0156] When the cumulative result of the rule activation degree of each fuzzy rule in the currently invoked fuzzy rule base is 0, it indicates that the current control pressure deviation and the rate of change of control pressure deviation have not effectively activated any fuzzy rule in the current fuzzy rule base. At this time, the above weighted average calculation is not performed, and the initial valve opening increment of the corresponding ash conveying branch in the current control cycle is determined to be 0, so as to keep the current valve opening of the corresponding ash conveying branch unchanged and avoid the initial valve opening increment being undeterminable due to the denominator of the weighted average calculation being 0.

[0157] Furthermore, the above calculation process uses the rule activation degree as the weighting basis for the output center value of the corresponding rule consequent, enabling multiple fuzzy rules with different activation degrees to be fused according to their respective contributions, thereby obtaining the initial valve opening increment for subsequent safety projection. In the case where no effective activation rule exists, the initial valve opening increment is set to 0, maintaining the current valve state in the control process, and waiting for the next control cycle to re-perform fuzzy inference based on the updated real-time monitoring data.

[0158] It should be noted that the weighted averaging process described above can reduce the abrupt changes in valve opening increment caused by switching between different fuzzy rules, thereby improving the continuity of the initial control quantity. Simultaneously, by setting a rollback mechanism when the cumulative rule activation result is 0, the situation where the denominator is 0 in the weighted averaging calculation can be avoided, improving the stability of the fuzzy control process under boundary or abnormal input conditions.

[0159] In this embodiment of the invention, the control pressure deviation and its rate of change are determined by the real-time pressure and the desired branch pressure. The corresponding fuzzy rule library is called according to the current global control mode. At the same time, the dynamic weight is determined by the relationship between the control pressure deviation and the effective resolution. The activation degree of the rules is corrected by the dynamic weight. Then, the output of the rule follow-up is fused according to the activation degree of each rule to obtain the preliminary valve opening increment. This enables the basic valve adjustment to respond to the global operating mode, the degree of pressure deviation and the pressure change trend at the same time, and reduces the impact of small measurement disturbances on the fuzzy control results.

[0160] S6: Based on the current valve opening and the initial valve opening increment of each ash conveying branch, perform safety projection to obtain multiple safety valve openings.

[0161] Specifically, candidate valve openings are generated based on the current valve opening and the initial valve opening increment. These candidate openings are then restricted to a dynamic limit range determined according to the current operating conditions to obtain a safe valve opening that meets the dynamic limit conditions. This safe opening serves as the current input signal for the subsequent trigonometric function smoother. Through safe projection, the valve regulation result calculated theoretically is limited to the safe and feasible region corresponding to the current operating conditions.

[0162] In one possible implementation, S6 specifically includes sub-steps S601 to S604: S601: Add the current valve opening of each ash conveying branch to the corresponding initial valve opening increment to obtain the candidate opening.

[0163] Among them, the candidate opening degree is the target valve opening degree of the corresponding ash conveying branch before dynamic limit processing. It is obtained by adding the current valve opening degree collected in the current control cycle and the corresponding preliminary valve opening degree increment calculated by S5, and is used as the object to be projected for safety projection.

[0164] S602: Construct constraint factors based on the number of ash conveying branches whose absolute value of rated pressure deviation is greater than the effective resolution of the pressure sensor.

[0165] Among them, the constraint factor is constructed based on the quantitative relationship between the number of out-of-tolerance branches and the total number of ash conveying branches in the current control cycle. It is used to characterize the safety constraint degree of the current valve opening and serves as the basis for determining the lower limit and upper limit of the dynamic limit.

[0166] Specifically, the absolute value of the rated pressure deviation of each ash conveying branch is compared with the effective resolution of the pressure sensor. Ash conveying branches with an absolute value of rated pressure deviation greater than the effective resolution of the pressure sensor are identified as out-of-tolerance branches, and the number of out-of-tolerance branches is counted. The ratio of the number of out-of-tolerance branches to the total number of ash conveying branches is determined as the constraint factor. The constraint factor ranges from 0 to 1, where the constraint factor is 0 when there are no out-of-tolerance branches, and 1 when all ash conveying branches are out-of-tolerance branches. The constraint factor increases with the increase in the number of out-of-tolerance branches.

[0167] The criteria for determining the out-of-tolerance branches mentioned above are the same as those used for determining the pressure anomaly branches during the global control mode determination process, ensuring that both control stages employ consistent pressure anomaly states. A larger constraint factor indicates a higher degree of system pressure anomaly, resulting in stronger constraints on subsequent valve regulation.

[0168] S603: Determine the dynamic limit lower boundary and dynamic limit upper boundary based on the constraint factor, the urgency of ash conveying in each ash hopper, the preset lower physical limit of the valve, the preset upper physical limit of the valve, and the current valve opening of each ash conveying branch.

[0169] The lower and upper physical limits of the valve are the minimum and maximum opening degrees preset according to the valve's allowable working stroke, respectively. The lower and upper dynamic limit limits are the safe opening boundaries for the current control cycle, determined within the physical limits based on constraint factors, the urgency of ash conveying in the corresponding ash hopper, and the current valve opening of the corresponding ash conveying branch. A correspondence is established between the ash hopper and the ash conveying branch according to their actual conveying connection.

[0170] Specifically, the allowable valve opening adjustment range for the corresponding ash conveying branch in the current control cycle is obtained by multiplying the difference obtained by subtracting the constraint factor, the urgency of ash conveying in the corresponding ash hopper, and the preset basic maximum allowable valve opening adjustment range. The basic maximum allowable valve opening adjustment range is the maximum allowable valve opening range within a single control cycle, which can be preset based on the response performance of the valve actuator, the allowable working stroke, and the control cycle length.

[0171] Furthermore, the initial dynamic lower limit is obtained by subtracting the allowable valve opening adjustment range from the current valve opening, and the initial dynamic upper limit is obtained by adding the allowable valve opening adjustment range to the current valve opening. The larger value between the initial dynamic lower limit and the valve's physical lower limit is determined as the dynamic lower limit, and the smaller value between the initial dynamic upper limit and the valve's physical upper limit is determined as the dynamic upper limit. This ensures that the dynamic lower limit is not lower than the valve's physical lower limit, the dynamic upper limit is not higher than the valve's physical upper limit, and the dynamic lower limit is not greater than the dynamic upper limit.

[0172] When the urgency of ash conveying is constant, increasing the constraint factor reduces the allowable valve opening adjustment range, and the dynamic limit range shrinks towards the current valve opening. Conversely, when the constraint factor is constant, increasing the urgency of ash conveying increases the allowable valve opening adjustment range, and the dynamic limit range expands accordingly. This allows for the dynamic determination of the safe opening range based on the degree of system pressure anomaly and conveying requirements.

[0173] S604: Based on the lower limit and upper limit of the dynamic limit, perform a safety projection on the candidate opening to obtain the safety valve opening of each ash conveying branch.

[0174] Specifically, when the candidate opening degree is between the lower and upper limits of the dynamic limit, it is used as the safety valve opening degree. When the candidate opening degree is below the lower limit, the lower limit is used as the safety valve opening degree. When the candidate opening degree is above the upper limit, the upper limit is used as the safety valve opening degree. Thus, the candidate opening degree is projected onto the current dynamic safe feasible region, ensuring that the output valve opening degree meets the safety constraints corresponding to the current operating condition.

[0175] In this embodiment of the invention, a candidate opening is formed by the current valve opening and the initial valve opening increment. A constraint factor is constructed based on the number of out-of-tolerance branches. The dynamic limit range is determined by combining the urgency of ash conveying, the physical boundary of the valve, and the current valve opening. The candidate opening is then projected into this range to reduce the risk of equipment over-limit, pressure fluctuation, or system instability caused by excessive valve adjustment.

[0176] S7: Using a trigonometric smoother, the opening degree of each safety valve is weighted and fused with the final control signal of the previous control cycle to obtain the final control signal of the current control cycle for each ash conveying branch, and then output to the valve positioner of the corresponding ash conveying branch.

[0177] The trigonometric smoother determines a smoothing factor based on the initial valve opening increment and the dynamic maximum step size, and then uses this smoothing factor to weight and fuse the current safety valve opening with the final control signal from the previous control cycle. The final control signal from the previous control cycle is the control signal sent to the valve positioner by the corresponding ash conveying branch after smoothing and fusion in the previous control cycle. This process reduces abrupt changes in control signals between adjacent control cycles.

[0178] In one possible implementation, S7 specifically includes sub-steps S701 to S704: S701: Determine the dynamic maximum step length of each ash conveying branch based on the urgency of ash conveying in each ash hopper and the preset basic single-step maximum allowable opening change.

[0179] The maximum allowable change in valve opening per step is a parameter preset based on the allowable range of basic valve opening changes within a control cycle. The dynamic maximum step size is adjusted based on this base value according to the urgency of ash conveying in the corresponding ash hopper, and serves as the benchmark for scaling the initial valve opening increment during smoothing factor calculation. The dynamic maximum step size increases when the ash conveying urgency is high, and decreases when the ash conveying urgency is low.

[0180] S702: Based on the initial valve opening increment and the corresponding dynamic maximum step size of each ash conveying branch, the corresponding smoothing factor is determined by the sine square function of the trigonometric function smoother.

[0181] The smoothing factor is a weighted coefficient obtained by mapping the initial valve opening increment to the dynamic maximum step size using a sine square function. It is used to determine the weight of the current safety valve opening and the final control signal of the previous control cycle in the final control signal of the current control cycle.

[0182] Specifically, for the i-th ash conveying branch, the ratio of the absolute value of the initial valve opening increment to the dynamic maximum step size is determined, and the ratio is limited to the range of 0 to 1 to obtain the normalized adjustment ratio. When the absolute value of the initial valve opening increment is less than the dynamic maximum step size, the ratio of the two is used as the normalized adjustment ratio; when the absolute value of the initial valve opening increment is greater than or equal to the dynamic maximum step size, the normalized adjustment ratio is determined to be 1.

[0183] Furthermore, the normalized adjustment ratio is multiplied by π / 2 and used as the input of the sine function. The resulting sine function value is squared to obtain the smoothing factor corresponding to the i-th ash conveying branch, so that the smoothing factor ranges from 0 to 1.

[0184] When the initial valve opening increment is small relative to the dynamic maximum step size, the smoothing factor is small, allowing the final control signal to retain more of the final control signal from the previous control cycle. As the initial valve opening increment approaches the dynamic maximum step size, the smoothing factor gradually increases, improving the final control signal's ability to follow the current safety valve opening. When the initial valve opening increment reaches or exceeds the dynamic maximum step size, the smoothing factor is 1.

[0185] S703: Using a smoothing factor as the weight, the opening degree of the safety valve of each ash conveying branch is weighted and fused with the final control signal of the previous control cycle to obtain the final control signal of the current control cycle.

[0186] Specifically, a smoothing factor is used as the weight for the current safety valve opening, and the difference between the smoothing factor and the current smoothing factor is used as the weight for the final control signal of the previous control cycle. These two factors are then weighted and fused. The final control signal of the i-th ash conveying branch in the current control cycle can be determined according to the following formula:

[0187] Among them, u cmd,i (c) represents the final control signal of the i-th ash conveying branch in the current control cycle c, η i u represents the smoothing factor corresponding to the i-th ash conveying branch. safe,i u represents the opening degree of the safety valve in the i-th ash conveying branch. cmd,i (c-1) represents the final control signal of the i-th ash conveying branch in the previous control cycle c-1.

[0188] When the current control cycle is the first control cycle after system startup, the current actual valve opening of the i-th ash conveying branch when the system enters closed-loop control is used as the initial value of the final control signal of the previous control cycle. From the second control cycle onwards, the final control signal calculated and output in the previous control cycle is used as the final control signal of the previous control cycle in the current control cycle.

[0189] By using the weighted fusion method described above, more historical control states are retained during small adjustments, while the degree of following the current safety valve opening is improved when there is a greater adjustment demand, thus balancing the smoothness of the control signal and the speed of response.

[0190] S704: Sends the final control signal of the current control cycle to the valve positioner of the corresponding ash conveying branch.

[0191] In this process, the final control signal of the current control cycle is associated with the ash conveying branch according to the branch number. Each final control signal is sent to the corresponding valve positioner through the controller output interface. The valve positioner adjusts the valve opening of the corresponding ash conveying branch according to the received final control signal, thereby completing the control output of the current control cycle.

[0192] For example, if the current valve opening of a certain ash conveying branch is 40%, and the initial valve opening increment obtained through S5 is 3%, then the candidate opening is 43%. If the current lower and upper dynamic limit boundaries are 35% and 45% respectively, then the candidate opening is within the dynamic limit range, and the safety valve opening is 43%. If the corresponding maximum dynamic step size is 5%, then the ratio of the absolute value of the initial valve opening increment to the maximum dynamic step size is 0.6, and the corresponding smoothing factor is determined according to the sine square mapping in S702. Then, based on this smoothing factor, the safety valve opening of 43% is weighted and fused with the final control signal of 41% from the previous control cycle to obtain the final control signal for the current control cycle.

[0193] In this embodiment of the invention, the basic single-step maximum allowable opening change is dynamically adjusted based on the urgency of ash conveying. A smoothing factor is determined using a sine square function based on the relationship between the initial valve opening increment and the dynamic maximum step size. The safety valve opening is then weighted and fused with the final control signal of the previous control cycle. This allows the final control signal to adaptively coordinate the new control target and the historical control state according to the current adjustment requirements. While maintaining the necessary response speed, this suppresses signal steps between adjacent control cycles, reducing frequent valve positioner actions and mechanical shocks.

[0194] S8: Return to step S1 and enter the next control cycle. This cycle is repeated to achieve adaptive closed-loop regulation of the pneumatic ash conveying process.

[0195] In this process, after completing the acquisition of real-time monitoring data, calculation of stability margin, calculation of ash conveying urgency, determination of global control mode, calculation of preliminary valve opening increment, safety projection, and output of final control signal in the current control cycle, the updated real-time monitoring data is acquired again to enter the next control cycle, and the final control signal of the current control cycle is used as the final control signal of the previous control cycle required for smooth fusion in the next control cycle.

[0196] Therefore, steps S1 to S7 are executed cyclically according to the control cycle, so that real-time data acquisition, status evaluation, conveying demand evaluation, control mode decision, valve adjustment, safety constraints and smooth output form a continuous closed loop, and the control strategy and valve control quantity are continuously updated as the operating status of the pneumatic ash conveying system changes.

[0197] In this embodiment of the invention, by reacquiring real-time monitoring data after completing valve control in the current control cycle, and using the final control signal of the current control cycle as the historical control state required for smooth fusion in the next control cycle, a continuous closed-loop control relationship is formed, which integrates real-time data acquisition, stable state evaluation, conveying demand evaluation, control mode decision-making, valve adjustment, safety constraints, and smooth output. This allows for continuous updates to the control strategy and valve control quantities as the operating state of the pneumatic ash conveying system changes, thereby improving the adaptability, continuity, and safety of ash conveying control under complex dynamic conditions.

[0198] Reference manual attached Figure 4 The diagram shows a structural schematic of an adaptive control system for pneumatic ash conveying based on real-time monitoring data provided by the present invention.

[0199] The present invention also provides a pneumatic ash conveying adaptive control system 20 based on real-time monitoring data, applied to the above-mentioned pneumatic ash conveying adaptive control method based on real-time monitoring data, comprising: Processor 201.

[0200] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, they implement the pneumatic ash conveying adaptive control method based on real-time monitoring data as described in the method embodiment.

[0201] The pneumatic ash conveying adaptive control system 20 based on real-time monitoring data provided by the present invention can execute the above-mentioned pneumatic ash conveying adaptive control method based on real-time monitoring data and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate further.

[0202] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0203] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0204] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0205] The following points need to be explained: (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0206] (2) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0207] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An adaptive control method for pneumatic ash conveying based on real-time monitoring data, characterized in that, include: S1: Obtain real-time monitoring data of the pneumatic ash conveying system; S2: Calculate the stability margin of the pneumatic ash conveying system based on the real-time monitoring data; S3: Calculate the urgency of ash conveying, which reflects the urgency of conveying each ash hopper; S4: Based on the real-time monitoring data, determine the rated pressure deviation of each ash conveying branch, and determine the global control mode based on the stability margin, the urgency of each ash conveying branch, and the rated pressure deviation of each branch. S5: Calculate the initial valve opening increment for each of the ash conveying branches according to the global control mode; S6: Based on the current valve opening degree of each of the ash conveying branches and the initial valve opening degree increment, perform safety projection to obtain multiple safety valve opening degrees; S7: Using a trigonometric smoother, the opening degree of each safety valve is weighted and fused with the final control signal of the previous control cycle to obtain the final control signal of the current control cycle of each ash conveying branch, and output to the valve positioner of the corresponding ash conveying branch. S8: Return to step S1 and enter the next control cycle. This cycle is repeated to achieve adaptive closed-loop regulation of the pneumatic ash conveying process.

2. The pneumatic ash conveying adaptive control method based on real-time monitoring data according to claim 1, characterized in that, S2 specifically includes: S201: Based on the pressure difference between the real-time pressures of any two different ash conveying branches in the real-time monitoring data and the preset effective resolution of the pressure sensor, determine the discretization order between the corresponding two ash conveying branches. S202: Based on the discretization order, construct a symmetric weight matrix through double factorial operation; S203: Construct a diagonal matrix based on the sum of the off-diagonal elements in each row of the symmetric weight matrix; S204: Construct a weighted Laplace matrix based on the difference between the diagonal matrix and the symmetric weight matrix; S205: Add the weighted Laplace matrix to a square matrix of the same order with all elements equal to 1 to obtain the sum matrix; S206: Calculate the stability margin of the pneumatic ash conveying system based on the determinant value of the summed matrix and the trace of the weighted Laplace matrix.

3. The pneumatic ash conveying adaptive control method based on real-time monitoring data according to claim 1, characterized in that, S3 specifically includes: S301: Determine the median and standard deviation of the material level based on the real-time material level of each of the ash hoppers in the real-time monitoring data; S302: Based on the standard deviation of the material level and the preset effective resolution of the material level sensor, determine the weighting coefficients through double factorial operation; S303: Combining the median of the material level, the standard deviation of the material level, and the weighting coefficient, determine the adaptive threshold through permutation calculation; S304: Based on the stability margin, determine the first shape factor and the second shape factor using sine and cosine functions; S305: Compare the real-time material level of each of the ash hoppers with the adaptive threshold to obtain the material level comparison result; S306: Based on the material level comparison results, and in combination with the first shape factor, the second shape factor, and the preset upper limit of the safe material level in the ash hopper, calculate the ash conveying urgency, which reflects the urgency of conveying in each of the ash hoppers.

4. The pneumatic ash conveying adaptive control method based on real-time monitoring data according to claim 3, characterized in that, Specifically, S306 is: Based on the material level comparison results, determine whether the real-time material level of each ash hopper is less than or equal to the adaptive threshold; if so, calculate the ash conveying urgency of the corresponding ash hopper based on the real-time material level of the corresponding ash hopper, the adaptive threshold, and the first shape factor. Otherwise, the urgency of ash conveying for the corresponding ash hopper is calculated based on the real-time material level of the corresponding ash hopper, the preset upper limit of the safe material level of the ash hopper, and the second shape factor.

5. The pneumatic ash conveying adaptive control method based on real-time monitoring data according to claim 1, characterized in that, The global control modes include emergency blockage relief mode, energy-saving optimization mode, and normal adjustment mode.

6. The pneumatic ash conveying adaptive control method based on real-time monitoring data according to claim 1, characterized in that, S4 specifically includes: S401: Based on the real-time pressure of each of the ash conveying branches in the real-time monitoring data and the preset rated working pressure of the branches, determine the rated pressure deviation of each of the ash conveying branches. S402: Identify the branches with an absolute value of the rated pressure deviation that is greater than the effective resolution of the pressure sensor, and count the number of branches with excessive tolerance. S403: Sort the absolute values ​​of the rated pressure deviations among the number of out-of-tolerance branches in descending order to obtain a descending sequence of the absolute values ​​of the rated pressure deviations; S404: Based on the number of out-of-tolerance branches, the total number of ash conveying branches, the absolute value sequence of rated pressure deviation, and the preset system design conveying pressure, determine the blockage propagation risk operator through combination number operation and permutation number operation; S405: Based on the preset maximum allowable continuous pressure deviation of the system and the stability margin, determine the lower bound and upper bound of the risk threshold using a sine function; S406: Determine the global control mode of the pneumatic ash conveying system based on the maximum value among the blockage propagation risk operator, the lower bound of the risk threshold, the upper bound of the risk threshold, and the ash conveying urgency of each of the ash hoppers.

7. The pneumatic ash conveying adaptive control method based on real-time monitoring data according to claim 1, characterized in that, S5 specifically includes: S501: Determine the control pressure deviation of each of the ash conveying branches based on the real-time pressure of each branch and the preset expected branch pressure. S502: Based on the control pressure deviations of each of the above, determine the rate of change of the control pressure deviation of the corresponding ash conveying branch through differential calculation; S503: Based on the global control mode, call the corresponding fuzzy rule library; S504: Based on the control pressure deviation and the preset effective resolution of the control pressure deviation, determine the dynamic weight through double factorial operation; S505: Combine the dynamic weights, the membership degrees corresponding to the control pressure deviation, and the membership degrees corresponding to the control pressure deviation change rate to determine the rule activation degree of each fuzzy rule in the fuzzy rule base; S506: Based on the activation degree of each rule, the output center value of the rule consequent of the fuzzy rule is weighted and averaged to obtain the preliminary valve opening increment of each ash conveying branch.

8. The pneumatic ash conveying adaptive control method based on real-time monitoring data according to claim 1, characterized in that, S6 specifically includes: S601: Add the current valve opening of each of the ash conveying branches to the corresponding initial valve opening increment to obtain the candidate opening; S602: Construct a constraint factor based on the number of ash conveying branches whose rated pressure deviation absolute value is greater than the effective resolution of the pressure sensor; S603: Determine the dynamic limit lower bound and dynamic limit upper bound based on the constraint factor, the urgency of ash conveying of each ash hopper, the preset lower physical limit of the valve, the preset upper physical limit of the valve, and the current valve opening of each ash conveying branch. S604: Based on the lower limit of the dynamic limit and the upper limit of the dynamic limit, perform a safety projection on the candidate opening to obtain the safety valve opening of each of the ash conveying branches.

9. The pneumatic ash conveying adaptive control method based on real-time monitoring data according to claim 1, characterized in that, Specifically, S7 includes: S701: Determine the dynamic maximum step size of each ash conveying branch based on the ash conveying urgency of each ash hopper and the preset basic single-step maximum allowable opening change. S702: Based on the initial valve opening increment of each of the ash conveying branches and the corresponding dynamic maximum step size, the corresponding smoothing factor is determined through the sine square function of the trigonometric function smoother; S703: Using the smoothing factor as a weight, the opening degree of the safety valve of each of the ash conveying branches is weighted and fused with the final control signal of the previous control cycle to obtain the final control signal of the current control cycle. S704: Send the final control signal of the current control cycle to the valve positioner corresponding to the ash conveying branch.

10. A pneumatic ash conveying adaptive control system based on real-time monitoring data, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the pneumatic ash conveying adaptive control method based on real-time monitoring data as described in any one of claims 1 to 9.

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