Dynamically-adjusted gas-fired boiler waveform fin type heat exchanger optimization control system

Abstract

The invention, which relates to the technical field of heat exchanger control, discloses a dynamically-adjusted gas-fired boiler waveform fin heat exchanger optimization control system comprising an information acquisition module, a function construction module, a risk marking module, a map construction module, an influence evaluation module, an optimal solution module, a real-time adjustment module and a mode determination module. Collecting temperature and flow velocity data, extracting disturbance characteristics, mapping to a three-dimensional lattice to construct a potential energy function, and calculating node potential energy; evaluating a potential energy isomerism marked high-risk area, and calculating a migration rate to generate a migration graph and a trend graph; combining the control variables to construct a sensitivity graph and a target function, and solving an optimal parameter; and the actuator is driven to intervene and maintain the dynamic switching operation mode of the memory stack in real time. Disturbance distribution is quantized through three-dimensional lattice mapping and nonlinear potential energy modeling, a diffusion path is revealed in combination with an asymmetric migration graph, and heat exchange efficiency is improved and energy consumption is reduced based on a sensitivity graph and key parameter adjustment.

Description

Technical Field

[0001] The invention relates to the technical field of heat exchanger control, in particular to a dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system. Background Art

[0002] Gas boilers often use fin heat exchangers to increase the heat transfer area, and the corrugated fin structure has good turbulence promotion and heat transfer performance. However, existing heat exchanger control methods mostly rely on fixed parameters or rough operating condition classification, and are unable to respond in real time to complex phenomena such as local disturbances, boundary layer transitions, and flow unevenness generated during the heat exchange process, resulting in the following deficiencies: large energy efficiency fluctuations. Traditional control strategies based on steady-state models or single thermodynamic indicators are difficult to adapt to load transients and frequent operating condition switching, and heat exchange efficiency often fluctuates severely; there is no early warning of heat exchange boundary layer instability, and there is a lack of refined perception and analysis of local transitions and turbulent fluctuations in the fin boundary layer, making it impossible to actively enhance or suppress control based on microscopic disturbance characteristics; there is a lack of multi-parameter collaborative optimization, and there is complex coupling between multiple performance parameters such as gas flow, intake temperature, and fin vibration, but linear or discrete control methods are generally used, making it difficult to achieve overall optimization; the control strategy has a delayed response, and existing systems usually do not have a disturbance evolution memory and pattern judgment mechanism, and lack in-depth utilization of historical operating data, resulting in slow control response when disturbances suddenly change.

[0003] In view of this, there is an urgent need for a method that can perceive and quantify the internal disturbance distribution of the corrugated fin heat exchanger in real time, build a control model based on multi-dimensional thermal disturbance characteristics and spatial mapping, and achieve precise control of the dynamic operating conditions of the heat exchanger through multi-parameter collaborative optimization and adaptive strategy switching, so as to improve heat transfer efficiency, reduce energy consumption and ensure system stability. Summary of the Invention

[0004] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system, comprising:

[0006] Information acquisition module: collects state information of the internal space of the corrugated fin heat exchanger of the gas boiler, extracts the temperature gradient, velocity shear rate and disturbance persistence factor to form disturbance characteristics;

[0007] Function construction module: divides the internal space of the heat exchanger into lattice nodes, constructs a lattice mapping structure, maps the disturbance characteristics to the lattice nodes, constructs a disturbance local potential energy function based on the disturbance characteristics, and calculates the disturbance potential energy value of the lattice nodes;

[0008] Risk marking module: performs spatial heterogeneity aggregation analysis on lattice nodes, calculates the spatial gradient of disturbance energy, identifies abnormal aggregation areas, and marks them as high-risk nodes for disturbance;

[0009] Graph construction module: Calculates the disturbance migration rate of node pairs based on high-risk nodes, constructs disturbance asymmetric migration graph and disturbance spatial propagation trend graph;

[0010] Impact Assessment Module: This module constructs a control sensitivity map based on the current values ​​of the control variable group to assess the nonlinear impact of different control variables on the disturbance migration field.

[0011] Optimal solution module: Based on the control sensitivity map, it constructs the disturbance control objective function, performs the solution of the optimal control parameter set, and obtains the optimal combination of gas flow, intake temperature and fin vibration frequency;

[0012] Real-time adjustment module: drives the actuator according to the optimized combination parameters, adjusts the gas input and fin vibration response in real time, and realizes active intervention against the disturbing dynamic potential;

[0013] Mode determination module: Builds a disturbance state memory stack, records historical control responses and disturbance trend information, and dynamically determines the operating mode based on the disturbance change trend and switches.

[0014] The present invention is further configured such that the state information includes: temperature information and flow rate information;

[0015] Based on the temperature information, the temperature gradient in the three-dimensional space direction is calculated by local difference to obtain the temperature gradient;

[0016] Calculate the first-order derivative of velocity in the coordinate direction based on the flow velocity information to obtain the velocity shear rate;

[0017] A fixed-length sliding time window is set to perform time series analysis on the temperature information at each spatial location, count the number of times the temperature change rate sign changes, and construct a disturbance persistence factor.

[0018] The temperature gradient, velocity shear rate and disturbance persistence factor at the spatial position are set as disturbance features.

[0019] The present invention is further configured such that the function building module includes:

[0020] The internal space of the heat exchanger is divided into lattice nodes, a lattice mapping structure is constructed, and the disturbance eigenvector is mapped to the lattice node at the corresponding spatial position;

[0021] Perform nonlinear enhancement processing on the perturbation features corresponding to the lattice nodes, and construct a local perturbation potential energy function based on the perturbation directionality and spatial correlation;

[0022] Calculate the perturbation potential energy value of the lattice node based on the local perturbation potential energy function.

[0023] The present invention is further configured such that the risk marking module includes:

[0024] Get the perturbation potential energy values ​​of lattice nodes and neighboring nodes;

[0025] The spatial heterogeneity of lattice nodes is evaluated based on the difference in perturbation potential energy values, and the maximum energy jump between a lattice node and its neighboring nodes is calculated;

[0026] Compare the spatial heterogeneity measurement value and the maximum energy jump amplitude of the node with the preset threshold to screen out the abnormal disturbance candidate nodes;

[0027] Perform connectivity analysis on candidate nodes and divide adjacent candidate nodes into multiple clusters;

[0028] Each cluster is judged based on the number of nodes in the cluster and the average value of spatial heterogeneity within the cluster, and all nodes in the cluster that meet the preset conditions are marked as high-risk nodes for disturbance.

[0029] The present invention is further configured such that the graph construction module includes:

[0030] Obtain the marked high-risk disturbance nodes and their three-dimensional spatial locations, and obtain pairs of high-risk nodes that are neighbors of each other;

[0031] Calculate the perturbation migration rate of each high-risk node pair based on the node potential energy value difference and spatial distance, and set the perturbation migration rate as the weight of the directed edge;

[0032] A disturbance asymmetric migration graph is constructed based on a set of directed weighted edges. The path weight distribution and node centrality of the disturbance asymmetric migration graph are analyzed to generate a disturbance spatial propagation trend map.

[0033] The present invention is further configured such that the impact assessment module includes:

[0034] Get the values ​​of the current control variable group, including gas flow, intake air temperature and fin vibration frequency;

[0035] Apply small control variable disturbances to each high-risk node along the main migration path in the perturbation asymmetric migration diagram, and record the response differences of the perturbation migration rate and node potential energy value corresponding to the changes in each control variable;

[0036] Normalize the response differences respectively to obtain the sensitivity coefficient of each high-risk node corresponding to each control variable;

[0037] The sensitivity coefficient of each high-risk node is mapped to the spatial position to form a control sensitivity map.

[0038] The present invention is further configured such that the optimal solution module includes:

[0039] Based on the sensitivity coefficients in the control sensitivity map, a disturbance control objective function is constructed to comprehensively measure the nonlinear relationship between the degree of disturbance migration rate suppression and the improvement of heat transfer efficiency.

[0040] Under the constraints of the objective function, a recursive grid search or evolutionary algorithm is used to perform global optimization on the control variable group to find the optimal parameter set of gas flow, intake temperature and fin vibration frequency.

[0041] A fine local search correction is performed on the optimal parameter set to eliminate the error caused by the discretization search and obtain the optimal combination of gas flow, intake temperature and fin vibration frequency for real-time control.

[0042] The present invention is further configured such that the real-time adjustment module includes:

[0043] Receive the optimized combination parameters of gas flow, intake temperature and fin vibration frequency output by the optimal solution module;

[0044] Generate actuator control instructions based on optimized combination parameters and send adjustment signals to the gas regulating valve and fin vibration actuator;

[0045] Real-time monitoring of gas flow, intake temperature, and fin vibration response feedback data, and comparison with optimized combination parameters;

[0046] If the feedback data deviates from the optimized combination parameters, the control instructions are fine-tuned based on the preset compensation strategy.

[0047] The present invention is further configured such that the mode determination module includes:

[0048] During operation, a disturbance state memory stack is continuously built to record the control variable setting values, actuator response data, and corresponding disturbance potential energy distribution characteristics at each time point;

[0049] Extract time series change characteristics based on the evolution trend of historical disturbance potential energy in the memory stack;

[0050] Input the time series change characteristics into the preset mode determination model to determine the optimal operating mode category corresponding to the current disturbance dynamics, including enhanced mode, energy-saving mode or steady-state mode;

[0051] According to the judgment results, mode switching instructions are sent to the real-time adjustment module and the optimal solution module to dynamically adjust the control strategy to achieve adaptive operation under different disturbance potentials.

[0052] The present invention is further configured such that the time series variation characteristics include potential energy peak frequency, trend slope, and duration of continuous deviation;

[0053] Identify the consecutive peak positions in the sequence of the perturbation potential energy memory stack, calculate the interval time between adjacent peaks, and set it as the potential energy peak frequency;

[0054] Perform least squares straight line fitting based on the time series of disturbance potential energy changes, obtain the slope of the fitted line, and set it as the trend slope;

[0055] During the period of time when the disturbance potential energy continuously deviates from the preset baseline and remains above the preset threshold, the duration of the continuous deviation is cumulatively calculated, and the deviation stability is quantified based on the duration of the continuous deviation.

[0056] The present invention provides a dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system, comprising: an information acquisition module: collecting state information of the internal space of the gas boiler corrugated fin heat exchanger, extracting temperature gradient, velocity shear rate and disturbance persistence factor to form disturbance characteristics; a function construction module: dividing the internal space of the heat exchanger into lattice nodes, constructing a lattice mapping structure, mapping the disturbance characteristics to the lattice nodes, constructing a disturbance local potential energy function based on the disturbance characteristics, and calculating the disturbance potential energy value of the lattice node; a risk marking module: performing spatial heterogeneity aggregation analysis on the lattice nodes, calculating the disturbance energy spatial gradient, identifying abnormal aggregation areas, and marking them as disturbance high-risk nodes; a map construction module: calculating the disturbance mobility of node pairs based on high-risk nodes, and constructing a disturbance Asymmetric migration diagram and disturbance space propagation trend map; Impact assessment module: Constructs a control sensitivity map based on the current value of the control variable group to evaluate the nonlinear impact of different control variables on the disturbance migration field; Optimal solution module: Constructs a disturbance control objective function based on the control sensitivity map, performs the solution of the optimal control parameter set, and obtains the optimized combination of gas flow, intake temperature and fin vibration frequency; Real-time adjustment module: Drives the actuator according to the optimized combination parameters, adjusts the gas input and fin vibration response in real time, and realizes active intervention in the disturbance dynamics; Mode determination module: Constructs a disturbance state memory stack, records historical control response and disturbance trend information, and dynamically determines the operating mode based on the disturbance change trend and switches. The beneficial effects include:

[0057] 1. Multidimensional spatial mapping and potential energy modeling: Using a three-dimensional lattice mapping structure, disturbance characteristics are mapped to discrete nodes and a local disturbance potential energy function is constructed. This can quantify disturbance intensity and spatial heterogeneity in a nonlinear manner, achieve a visual representation of disturbance energy distribution, and provide a physical basis for accurate risk identification.

[0058] 2. Asymmetric migration map and propagation trend prediction: Constructing asymmetric migration map of disturbance and spatial propagation trend map can accurately reveal the main migration path and diffusion range of disturbance in the heat exchange channel, providing a decision basis for the evolution direction and priority intervention points for dynamic control;

[0059] 3. Global optimization and adaptive collaboration: Construct a disturbance control objective function based on the sensitivity map, and use a recursive grid or evolutionary algorithm to solve the optimal control parameter combination to achieve coordinated optimization of gas flow, intake temperature and fin vibration, significantly improving heat exchange efficiency and reducing energy consumption.

[0060] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the 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 inventive efforts. In the drawings:

[0062] Figure 1 It is a structural schematic diagram of a dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0064] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0065] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0066] Dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system, such as Figure 1 As shown, including:

[0067] Information acquisition module: collects state information of the internal space of the gas boiler corrugated fin heat exchanger, extracts temperature gradient, velocity shear rate and disturbance persistence factor to form disturbance characteristics;

[0068] Function construction module: divides the internal space of the heat exchanger into lattice nodes, constructs a lattice mapping structure, maps the disturbance characteristics to the lattice nodes, constructs a disturbance local potential energy function based on the disturbance characteristics, and calculates the disturbance potential energy value of the lattice nodes;

[0069] Risk marking module: performs spatial heterogeneity aggregation analysis on lattice nodes, calculates the spatial gradient of disturbance energy, identifies abnormal aggregation areas, and marks them as high-risk nodes for disturbance;

[0070] Graph construction module: Calculates the disturbance migration rate of node pairs based on high-risk nodes, constructs disturbance asymmetric migration graph and disturbance spatial propagation trend graph;

[0071] Impact Assessment Module: This module constructs a control sensitivity map based on the current values ​​of the control variable group to assess the nonlinear impact of different control variables on the disturbance migration field.

[0072] Optimal solution module: Based on the control sensitivity map, it constructs the disturbance control objective function, performs the solution of the optimal control parameter set, and obtains the optimal combination of gas flow, intake temperature and fin vibration frequency;

[0073] Real-time adjustment module: drives the actuator according to the optimized combination parameters, adjusts the gas input and fin vibration response in real time, and realizes active intervention against the disturbing dynamic potential;

[0074] Mode determination module: Builds a disturbance state memory stack, records historical control responses and disturbance trend information, and dynamically determines the operating mode based on the disturbance change trend and switches.

[0075] The present invention is further configured such that the state information includes temperature information and flow rate information; specifically, a plurality of temperature sensors T1-T2 are arranged in the internal space and surface of the gas boiler corrugated fin heat exchanger. n With speed sensor V1–V m ,Each temperature sensor collects the instantaneous temperature value at different positions inside the heat exchanger to form a three-dimensional temperature field data set; each velocity sensor collects the gas velocity vector at the corresponding position to form a three-dimensional velocity field data set;

[0076] Based on the temperature information, the temperature gradient in the three-dimensional space direction is calculated by local difference to obtain the temperature gradient; specifically, for each temperature sensor T iAt a point P (x, y, z) in space, the three-dimensional temperature gradient at point P is calculated using the central difference method based on the temperature values ​​of its six-way neighboring points.

[0077] Based on the velocity information, the first-order derivative of the velocity in the coordinate direction is calculated to obtain the velocity shear rate; specifically, for each velocity sensor V j The three-component velocity of the point Q(x,y,z) [v x ,v y ,v z ], perform first-order difference operations in adjacent coordinate directions to obtain the velocity shear rate at point Q;

[0078] Set a fixed-length sliding time window, perform time series analysis on the temperature information at each spatial position, count the number of times the temperature change rate sign changes, and construct the disturbance persistence factor; specifically, at each temperature sensor T i At , a fixed-length sliding time window is used, for example, 100 consecutive sampling points within 1 second, to perform time series analysis on the temperature series. Count the number of times N the temperature change rate sign (positive or negative) switches within the time window. sign The number of times is used as the disturbance persistence factor, which reflects the frequency and persistence of temperature disturbance;

[0079] The temperature gradient, velocity shear rate and disturbance persistence factor at the spatial position are set as disturbance characteristics; specifically, the temperature gradient vector, velocity shear rate component and disturbance persistence factor obtained in the above steps are combined in any order at the same spatial coordinate to form a ternary disturbance characteristic vector.

[0080] The present invention is further configured such that the function building module includes:

[0081] The internal space of the heat exchanger is divided into lattice nodes, and a lattice mapping structure is constructed to map the disturbance feature vectors to the lattice nodes at the corresponding spatial locations. Specifically, the three-dimensional space inside the corrugated fin heat exchanger is divided into small cells of equal volume, with the center of each cell defined as a lattice node. Based on the spatial coordinates of the disturbance feature vectors, each disturbance feature is assigned to the lattice node where it resides, forming a node-feature mapping relationship network.

[0082] Nonlinear enhancement processing is performed on the disturbance features corresponding to the lattice nodes, and a local disturbance potential energy function is constructed based on the disturbance directionality and spatial correlation. Specifically, the disturbance features grouped into the same lattice node are summarized within the node, and a nonlinear amplification strategy is applied to the temperature gradient component, velocity shear rate component, and disturbance duration factor component, respectively, to highlight the influence of local extreme values ​​and high-frequency fluctuations. The nonlinear amplification strategies used may include exponential amplification, piecewise linear enhancement, or threshold-triggered enhancement. Multiple strategies can be used in parallel or alternately to adapt to the interference intensity under different working conditions. The disturbance characteristics after nonlinear enhancement are combined with the characteristic direction consistency information of adjacent lattice nodes to construct a local potential energy function that can reflect the cumulative effect of the disturbance inside the node and the interaction with the neighborhood. It not only considers the characteristic intensity of a single node, but also pays attention to the continuity and consistency of the characteristic changes between adjacent nodes, which is used to quantify the local energy concentration of each node. Furthermore, in a feasible embodiment of the present invention, the internal space of the heat exchanger is simplified and divided into a 2×2×1 lattice structure, corresponding to four nodes A, B, C, and D, with spatial coordinates of (0,0,0), (1,0,0), (0,1,0), and (1,1,0), respectively. The information acquisition module obtains the disturbance characteristic data of each node respectively: the temperature gradient of node A is 10K / m, the velocity shear rate is 5s -1 , the disturbance duration factor is 3; the temperature gradient of node B is 12K / m, and the velocity shear rate is 4s -1 , the disturbance duration factor is 2; the temperature gradient of node C is 8K / m, and the velocity shear rate is 6s -1 , the disturbance duration factor is 4; the temperature gradient of node D is 15K / m, and the velocity shear rate is 3s -1 , the disturbance persistence factor is 1. In the nonlinear enhancement processing stage, after the function construction module summarizes the above-mentioned disturbance features in each node, it applies different strengths of amplification to the temperature gradient, velocity shear rate and disturbance persistence factor: the temperature gradient is multiplied by 1.2, the velocity shear rate is multiplied by 1.5, and the disturbance persistence factor is multiplied by 2. The obtained enhancement results (rounded off) are node A (12, 8, 6), node B (14, 6, 4), node C (9, 9, 8) and node D (18, 4, 2). When constructing the local disturbance potential energy function, the consistency of the characteristic direction between each node and its four-way neighboring nodes is considered: if the cosine of the angle between the enhanced characteristic vector and the neighborhood average vector exceeds 0.9, an additional value is added to the node summary result to reflect the neighborhood synergy effect. In this embodiment, node A is highly consistent with B and C, with a gain of 5; node B is relatively consistent with A and D, with a gain of 3; node C also has a gain of 3; node D has the lowest consistency with its neighbors, with a gain of only 1. The perturbation potential energy value of each node is the sum of the three enhanced features plus the neighborhood consistency gain, which is calculated to be 31 for node A, 27 for node B, 29 for node C, and 25 for node D.

[0083] Based on the local perturbation potential energy function, the perturbation potential energy values ​​of the lattice nodes are calculated. Specifically, the local perturbation potential energy function is called for each lattice node to calculate the node's potential energy value. Once the calculation is complete, the potential energy values ​​of all nodes are stored in a three-dimensional potential energy distribution map according to their spatial coordinates. This intuitively displays the spatial layout of the perturbation energy within the heat exchanger and provides input for the subsequent risk identification module.

[0084] The present invention is further configured such that the risk marking module includes:

[0085] Get the perturbation potential energy values ​​of lattice nodes and neighboring nodes;

[0086] The spatial heterogeneity of lattice nodes is evaluated based on the difference in perturbation potential energy values, and the maximum energy jump amplitude between the lattice node and its neighboring nodes is calculated. Specifically, for each node, the sum of the potential energy differences with its neighboring nodes is calculated to reflect the heterogeneity of the node in the local space. At the same time, the largest one-way difference is determined as the maximum energy jump amplitude of the node to capture the most intense local fluctuations.

[0087] Compare the spatial heterogeneity metric value and maximum energy jump amplitude of the node with the preset threshold value to screen out the candidate nodes with disturbance anomaly. Specifically, compare the spatial heterogeneity metric value and maximum jump amplitude of each node with the preset heterogeneity threshold and jump threshold respectively, and screen out the nodes that meet at least one of the threshold values ​​as candidate nodes with disturbance anomaly.

[0088] Performing connectivity analysis on candidate nodes and dividing mutually adjacent candidate nodes into multiple clusters; specifically, performing connectivity analysis on all candidate nodes based on lattice adjacency relationships and dividing the set of nodes that are directly connected to each other or connected through candidate node links into several clusters;

[0089] Each cluster is judged based on the number of nodes in the cluster and the average value of spatial heterogeneity within the cluster, and all nodes in the cluster that meet the preset conditions are marked as high-risk disturbance nodes; specifically, for each cluster, the number of nodes in the cluster and the average level of spatial heterogeneity within the cluster are judged according to the preset conditions. Only clusters that meet both the minimum cluster size and the minimum average heterogeneity requirements will have all their nodes marked as high-risk disturbance nodes. In the above-mentioned feasibility embodiment of the present invention, the obtained four-node lattice structure and the corresponding perturbation potential energy values ​​are: the potential energy value of node A is 31, node B is 27, node C is 29, and node D is 25. The risk marking module is executed according to the following steps: the potential energy value of each lattice node and the four-way neighboring nodes are read from the potential energy distribution map: the neighboring nodes of node A are B and C, and their potential energy values ​​are 27 and 29 respectively; the neighboring nodes of node B are A and D, and their potential energy values ​​are 31 and 25 respectively, and so on; for each node, the potential energy difference between it and each neighboring node is calculated, and the largest one is selected as the maximum energy jump amplitude of the node. For example, if the difference between node A and B is 4 and the difference between node A and C is 2, then its maximum jump amplitude is 4; if the difference between node D and B is 2 and the difference between node D and C is 4, then its maximum jump amplitude is also 4; if the difference between node B and A is 4 and the difference between node B and D is 2, the maximum jump amplitude is 4; if the difference between node C and A is 2 and the difference between node C and D is 4, the maximum jump amplitude is 4; calculate the spatial heterogeneity metric value for each node, that is, the sum of the potential energy differences between the node and all its neighbors. For example, the spatial heterogeneity of node A is 4+2=6; node B is 4+2=6; node C is 2+4=6; node D is 2+4=6; compare the spatial heterogeneity metric value and maximum jump amplitude of each node with the preset threshold. In this embodiment, the heterogeneity threshold is set to 5 and the jump amplitude threshold is set to 3, so the metric value (6) and maximum jump amplitude (4) of all nodes exceed their respective thresholds, so A, B, C, and D are all screened as disturbance anomaly candidate nodes; perform connectivity analysis on the candidate nodes. Since A, B, C, and D are adjacent to each other in the lattice, they are determined to be a cluster; the cluster is judged as follows: the number of nodes in the cluster is 4 (greater than the preset sub-cluster minimum size of 2), and the average spatial heterogeneity in the cluster is 6 (higher than the threshold of 5), so all nodes A, B, C, and D in the cluster are marked as high-risk nodes for disturbance.

[0090] The present invention is further configured such that the graph construction module includes:

[0091] Obtain the marked high-risk perturbation nodes and their 3D spatial locations, and obtain pairs of high-risk nodes that are neighbors of each other. Specifically, extract the 3D spatial coordinates and perturbation potential energy values ​​of each node from the set of high-risk perturbation nodes output by the risk marking module. Based on the neighborhood relationship of the lattice structure, select pairs of nodes that are neighbors of each other or can be directly connected.

[0092] The perturbation mobility rate for each high-risk node pair is calculated based on the difference in node potential energy and spatial distance, and the perturbation mobility rate is set as the weight of the directed edge. Specifically, for each high-risk node pair, the perturbation mobility rate is calculated based on the perturbation potential energy difference and spatial distance between the two nodes. The potential energy difference reflects the driving force for the perturbation to propagate to areas with lower potential energy, while the spatial distance represents the path impedance. The combination of the two yields the mobility rate, which is used to quantify the intensity of perturbation transmission between nodes.

[0093] Based on a set of directed weighted edges, a perturbation asymmetric migration graph is constructed. The path weight distribution and node centrality of the perturbation asymmetric migration graph are analyzed to generate a spatial propagation trend map of the perturbation. Specifically, the migration rate of each node pair is set as the weight of the directed edge, and a perturbation asymmetric migration graph is constructed with high-risk nodes as vertices and directed weighted edges as connections. This graph intuitively expresses the dominant flow direction and relative strength of the perturbation between different nodes. A topological analysis of the constructed directed weighted graph is performed, including path weight distribution and node centrality assessment. The most likely perturbation diffusion path is identified based on edge weights, and key convergence or divergence nodes are determined based on the node in-degree and out-degree weights. The analysis results are presented as trend graphs, indicating the diffusion direction and impact range of the disturbance along the main path, for reference by the dynamic control module. In the feasibility embodiment of the present invention, nodes A(0,0,0), B(1,0,0), C(0,1,0), and D(1,1,0) and their corresponding disturbance potential energy values ​​{31,27,29,25} are obtained from the high-risk node set output by the risk marking module. Based on the lattice topology, the node pairs that are neighbors are determined to be (A,B), (A,C), (B,D), and (C,D). For each node pair, the perturbation migration rate is estimated based on the potential energy difference between the two nodes and the spatial distance between them: for node pair (A, B), the potential energy difference is 4, the distance is 1, and the migration rate is set to 4.0; for node pair (A, C), the potential energy difference is 2, the distance is 1, and the migration rate is set to 2.0; for node pair (B, D), the potential energy difference is 2, the distance is 1, and the migration rate is set to 2.0; and for node pair (C, D), the potential energy difference is 4, the distance is √2≈1.414, and the migration rate is set to 2.8. Using these migration rates as directed edge weights, a directed weighted graph G is constructed: edge A→B has a weight of 4.0; edge A→C has a weight of 2.0; edge B→D has a weight of 2.0; and edge C→D has a weight of 2.8. This directed weighted graph G depicts the primary migration trend of perturbations from high-potential nodes to neighboring nodes. Analysis of the weight distribution of each path in the directed weighted graph G reveals that path A→B has the highest weight, followed by path C→D, A→C, and B→D. The node centrality is calculated based on the node's out-degree weight and in-degree weight: node A has the highest out-degree intensity and the highest centrality; node D has the highest in-degree intensity and is the main aggregation node; finally, a disturbance space propagation trend map is generated, which intuitively displays the main migration paths A→B and C→D with the length and thickness of the arrows, and reflects the centrality with the size of the node, guiding subsequent priority intervention in the A-B area or the D aggregation area.

[0094] The present invention is further configured such that the impact assessment module includes:

[0095] Obtain the values ​​of the current control variable group, including gas flow, intake air temperature, and fin vibration frequency; specifically, read the current values ​​of gas flow, intake air temperature, and fin vibration frequency from the optimal solution module or real-time monitoring system as benchmark conditions;

[0096] For each high-risk node, a small control variable perturbation is applied along the main migration path in the perturbation asymmetric migration diagram, and the response differences of the perturbation mobility and node potential energy value corresponding to the change of each control variable are recorded. Specifically, for each marked high-risk node, a small amplitude perturbation (for example, ±5%) is applied to the three control variables along its main migration path in the perturbation asymmetric migration diagram, and the changes in the corresponding perturbation mobility and node perturbation potential energy value are monitored and recorded in real time.

[0097] The response differences are normalized to obtain the sensitivity coefficient of each high-risk node corresponding to each control variable. Specifically, the ratio of the mobility and potential energy response differences caused by each perturbation to the applied amplitude is normalized to obtain the sensitivity coefficient of each high-risk node for each control variable, reflecting the relative influence of the variable on the propagation of the local perturbation.

[0098] The sensitivity coefficient of each high-risk node is mapped to the spatial position to form a control sensitivity map; specifically, the sensitivity coefficients of all high-risk nodes are mapped and displayed in a three-dimensional model according to their spatial positions to form a control sensitivity map, reflecting the response strength of different positions to the gas flow, intake temperature and fin vibration frequency; in the above-mentioned feasibility embodiment of the present invention, the initial setting values ​​of the current control variable group are: gas flow 100m– / h, intake temperature 300K and fin vibration frequency 50Hz. For the four marked high-risk nodes A, B, C, and D, a small perturbation of +5% is applied to the above three control variables along their main migration paths on the perturbation asymmetric migration map, and the change amplitude of the perturbation migration rate is recorded in real time. As shown in the following table:

[0099]

[0100]

[0101] By mapping the sensitivity coefficients of the three control variables corresponding to each of the above nodes and drawing a control sensitivity map according to the distribution positions of the nodes in three-dimensional space, the response strength of different nodes to gas flow, intake temperature and fin vibration frequency can be intuitively reflected.

[0102] The present invention is further configured such that the optimal solution module includes:

[0103] A disturbance control objective function is constructed based on the sensitivity coefficients in the control sensitivity map to comprehensively measure the nonlinear relationship between the degree of disturbance migration rate suppression and the extent of heat exchange efficiency improvement. Specifically, based on the sensitivity coefficients of each high-risk node in the control sensitivity map to gas flow, intake temperature, and fin vibration frequency, these are combined according to preset weights to form a nonlinear objective function that comprehensively measures the disturbance migration rate suppression effect and the extent of heat exchange efficiency improvement.

[0104] Under the constraints of the objective function, a recursive grid search or evolutionary algorithm is used to globally optimize the control variable set to determine the optimal parameter set for gas flow rate, intake temperature, and fin vibration frequency. Specifically, under the constraints of the objective function, including the allowable flow rate, temperature, and frequency ranges, a recursive grid search or evolutionary algorithm is used to perform a global search for the three control variables. Through multiple rounds of iteration or population evolution, the algorithm continuously updates the variable combination and selects a set of candidate parameters that maximize the objective function value, namely the optimal parameter set for gas flow rate, intake temperature, and fin vibration frequency.

[0105] A fine-grained local search and correction is performed on the optimal parameter set to eliminate errors caused by the discretization search and obtain the optimal combination of gas flow, intake temperature, and fin vibration frequency for real-time control. A small-scale local search is performed near the parameter set obtained by the global optimization to eliminate errors introduced by discretization or sampling intervals to obtain a more accurate real-time control combination. This step fine-tunes each variable based on the grid search to ensure that the final output parameters can achieve the optimal control effect in the actual actuator and feedback system. In the above-mentioned feasible embodiment of the present invention, the control sensitivity map has calculated the sensitivity coefficients of the four high-risk nodes to the three variables, and the comprehensive weights are determined to be 40% for gas flow, 35% for intake temperature, and 25% for fin vibration frequency. Within the allowable variable range - gas flow 90-110m / h, intake temperature 290-310K, and fin vibration frequency 45-55Hz - a recursive grid search with step sizes of 2m / h, 2K, and 1Hz is used to evaluate the objective function value of each combination.

[0106] The first round of global search revealed a parameter combination (102 m / h, 305 K, 52 Hz) that peaked the objective function. A subsequent localized search near this combination—flow rate 100 to 104 m / h, temperature 303 to 307 K, and frequency 51 to 53 Hz—with smaller step sizes (0.5 m / h, 0.5 K, 0.2 Hz) ultimately determined the optimal combination: gas flow rate 101.5 m / h, inlet temperature 304.0 K, and fin vibration frequency 51.6 Hz.

[0107] After the optimal combination is sent to the actuator through the real-time adjustment module, feedback monitoring verification shows that the disturbance migration rate can be reduced by about 15%, while the heat transfer efficiency can be improved by about 8%, fully demonstrating the collaborative optimization capability of the objective function in suppressing disturbances and improving efficiency.

[0108] The present invention is further configured such that the real-time adjustment module includes:

[0109] Receive the optimized combination parameters of gas flow, intake air temperature and fin vibration frequency output by the optimal solution module; specifically, obtain the optimized combination parameters of gas flow, intake air temperature and fin vibration frequency from the optimal solution module and store them in the control scheduling cache;

[0110] Generate actuator control instructions based on the optimized combination parameters and send adjustment signals to the gas regulating valve and fin vibration actuator. Specifically, generate standardized actuator control commands based on the above parameters and send corresponding opening, temperature control and vibration frequency setting signals to the gas regulating valve and fin vibration actuator respectively to realize equipment operation.

[0111] Real-time monitoring of feedback data on gas flow, intake temperature, and fin vibration response is performed, and compared with the optimized combination parameters. Specifically, the actual response values ​​of the gas valve opening, intake pipe temperature, and vibration sensor output are continuously sampled and compared with the cached optimized combination parameters in real time to evaluate execution errors.

[0112] If the feedback data deviates from the optimized combination parameters, the control instructions are fine-tuned based on the preset compensation strategy; specifically, when the monitored feedback value deviates from the target parameter beyond the preset tolerance, the correction amount is calculated according to the preset compensation strategy (including PID closed-loop adjustment and incremental correction table), and the execution instruction is updated until the actual response returns to near the optimization target; in the above-mentioned feasibility embodiment of the present invention, the optimized combination output by the optimal solution module is: gas flow rate 101.5m– / h, intake temperature 304.0K, fin vibration frequency 51.6Hz; the three sets of parameters are loaded into the control cache and prepared for issuance; the execution instruction is generated and issued: sending an "opening setting: 52%" signal to the gas valve; sending a "heating to 304.0K" command to the heat exchange inlet thermostat; sending a "vibration frequency: 51.6Hz" instruction to the vibration actuator. Real-time feedback monitoring: Sampling once per second: measured gas flow rate of 101.0 m / h; measured inlet temperature of 302.8 K; measured vibration frequency of 51.2 Hz. The flow rate, temperature, and frequency were found to be 0.5% lower than normal, 1.2 K lower than normal, and 0.4 Hz lower than normal, exceeding the ±1% tolerance. Based on the PID parameters, the gas valve opening was adjusted by +1%, the thermostat heating power by +3%, and the vibrator input voltage by +2%. After issuing the command again, the responses returned to 101.6 m / h, 304.1 K, and 51.6 Hz, reducing the error to ±0.1%.

[0113] The present invention is further configured such that the mode determination module includes:

[0114] During operation, a disturbance state memory stack is continuously constructed to record the control variable settings, actuator response data, and corresponding disturbance potential energy distribution characteristics at each time point. Specifically, during operation, the gas flow rate, intake temperature, and fin vibration frequency settings at each sampling moment, as well as the actual actuator response data and the corresponding three-dimensional distribution of disturbance potential energy are recorded in real time and pushed into the memory stack in chronological order to form continuous disturbance-control history data.

[0115] Based on the historical disturbance potential energy evolution trend in the memory stack, the time series change characteristics are extracted; the present invention is further configured such that the time series change characteristics include the potential energy peak frequency, the trend slope and the continuous deviation duration; the continuous peak positions are identified in the sequence of the disturbance potential energy memory stack, the interval time between adjacent peaks is calculated, and the interval time is set as the potential energy peak frequency; based on the time series of the disturbance potential energy change, the least squares straight line fitting is performed to obtain the slope of the fitting straight line, and the interval time is set as the trend slope; during the time period in which the disturbance potential energy continuously deviates from the preset baseline and remains above the preset threshold, the continuous deviation duration is cumulatively calculated, and the deviation stability is quantified according to the continuous deviation duration. Specifically, a time series analysis is performed on the evolution data of the perturbation potential energy distribution stored in the stack, and the frequency of the potential energy peak, the trend slope of the overall change, and the duration of the potential energy deviation from the operating baseline are identified in turn. The logic for extracting the potential energy peak frequency is as follows: in the sequence of the perturbation potential energy memory stack, the time points at which all local peaks appear are identified, the time interval between two adjacent peaks is calculated, and the peak frequency is expressed as its average reciprocal or reciprocal of the reciprocal, which is used to quantify the rhythm of repeated perturbations; the logic for extracting the trend slope is as follows: the discrete data of the entire perturbation potential energy over time is used as the independent variable The time and dependent variable (potential energy value) are input into the least squares straight line fitting algorithm, and the slope of the fitted line is extracted to represent the overall upward or downward trend of the potential energy over time. The logic for extracting the duration of continuous deviation is as follows: set a baseline potential energy value and deviation threshold, scan all time periods in the time series where the potential energy value is continuously higher than the baseline plus the threshold (or continuously lower than the baseline minus the threshold), and accumulate the duration of these continuous intervals as the duration of continuous deviation to quantify the degree of stable deviation of the disturbance from the normal baseline. The time series change characteristics jointly reflect the development speed, direction, and stability of the disturbance potential.

[0116] Input the time series change characteristics into a preset mode determination model to determine the optimal operating mode category corresponding to the current disturbance momentum, including enhanced mode, energy-saving mode, or steady-state mode. Specifically, the above time series change characteristics are input into a pre-trained mode determination model (which can be based on a decision tree, support vector machine, or simple rule engine). The model automatically determines one of the three operating modes suitable for the current disturbance momentum based on the combination of characteristics: enhanced mode, which is suitable for high-frequency and severe disturbances; energy-saving mode, which is suitable for low-frequency and small disturbances; and steady-state mode, which is suitable for flat or slightly fluctuating disturbances.

[0117] According to the determination result, a mode switching instruction is issued to the real-time adjustment module and the optimal solution module, and the control strategy is dynamically adjusted to achieve adaptive operation under different disturbance potentials. Specifically, according to the mode determination result, a mode switching instruction is issued to the real-time adjustment module and the optimal solution module respectively, the real-time adjustment module adjusts the response sensitivity of the feedback loop, and the optimal solution module replaces the optimization objective function weight of the corresponding mode to achieve adaptive operation of the system under different disturbance potentials. In the above-mentioned feasibility embodiment of the present invention, within 10 minutes of continuous operation, data is recorded once a minute in the memory stack, for a total of 10 items. The extracted disturbance potential energy peak time point and value are shown in the following table:

[0118]

[0119]

[0120] Peak frequency: A total of 10 peaks were identified, spanning 9 minutes (from the 1st to the 10th minute), with an average peak interval of approximately 1 minute and a peak frequency of approximately 1 peak / minute. Trend slope: A linear fit of the above data yielded a slope of approximately 0.92 units / minute, indicating that the potential energy increased at an overall rate of approximately 0.92 units / minute. Duration of sustained deviation: With a baseline potential energy value of 34 and a deviation threshold of 2, the following periods were detected for potential energy values ​​greater than 36: 36 in the 5th minute (reaching the threshold, a single point lasting 1 minute), and 37, 36, 38, and 37 in the 7th–10th minutes (4 consecutive minutes). The cumulative duration of sustained deviation is 1 + 4 = 5 minutes. The peak frequency, trend slope, and duration of sustained deviation are input into the pre-set rule engine. The rule is: if the peak frequency is ≥ 0.8, the trend slope is ≥ 0.5, and the duration of sustained deviation is ≥ 2, then an intensification pattern is identified. In this case, these conditions are met and the intensification pattern is identified. An "enhanced mode" instruction is issued to the real-time adjustment module to improve the response gain of the feedback loop PID parameters; the disturbance suppression weight in the objective function is adjusted from the original 25% to 40% in the optimal solution module, and parameter optimization is restarted; the above logic constructs a closed-loop adaptive channel from data collection to strategy adjustment through historical data integration - change feature extraction - operation mode determination - mode switching, which enables the heat exchanger control to move from "passive response" to "active prediction + intelligent switching", which not only improves the heat exchange efficiency, but also effectively suppresses the energy efficiency fluctuations caused by disturbances.

[0121] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0122] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0123] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or plural.

[0124] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0125] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0126] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0128] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0129] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0130] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0131] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system, characterized in that: include: Information acquisition module: collects state information of the internal space of the gas boiler corrugated fin heat exchanger, extracts temperature gradient, velocity shear rate and disturbance persistence factor to form disturbance characteristics; Function construction module: divides the internal space of the heat exchanger into lattice nodes, constructs a lattice mapping structure, maps the disturbance characteristics to the lattice nodes, constructs a disturbance local potential energy function based on the disturbance characteristics, and calculates the disturbance potential energy value of the lattice nodes; Risk marking module: performs spatial heterogeneity aggregation analysis on lattice nodes, calculates the spatial gradient of disturbance energy, identifies abnormal aggregation areas, and marks them as high-risk nodes for disturbance; Graph construction module: Calculates the disturbance migration rate of node pairs based on high-risk nodes, constructs disturbance asymmetric migration graph and disturbance spatial propagation trend graph; Impact Assessment Module: This module constructs a control sensitivity map based on the current values ​​of the control variable group to assess the nonlinear impact of different control variables on the disturbance migration field. Optimal solution module: Based on the control sensitivity map, it constructs the disturbance control objective function, performs the solution of the optimal control parameter set, and obtains the optimal combination of gas flow, intake temperature and fin vibration frequency; Real-time adjustment module: drives the actuator according to the optimized combination parameters, adjusts the gas input and fin vibration response in real time, and realizes active intervention against the disturbing dynamic potential; Mode determination module: Builds a disturbance state memory stack, records historical control responses and disturbance trend information, and dynamically determines the operating mode based on the disturbance change trend and switches.

2. The dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to claim 1 is characterized in that: Status information includes: temperature information and flow rate information; Based on the temperature information, the temperature gradient in the three-dimensional space direction is calculated by local difference to obtain the temperature gradient; Based on the flow velocity information, the first-order derivative of the velocity in the coordinate direction is calculated to obtain the velocity shear rate; A sliding time window of fixed length is set to perform time series analysis on the temperature information at each spatial location, count the number of times the temperature change rate sign changes, and construct the disturbance persistence factor; The temperature gradient, velocity shear rate and disturbance persistence factor at the spatial position are set as disturbance features.

3. The dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to claim 2 is characterized in that: Function building blocks include: The internal space of the heat exchanger is divided into lattice nodes, a lattice mapping structure is constructed, and the disturbance eigenvector is mapped to the lattice node at the corresponding spatial position; Perform nonlinear enhancement processing on the perturbation features corresponding to the lattice nodes, and construct a local perturbation potential energy function based on the perturbation directionality and spatial correlation; Calculate the perturbation potential energy value of the lattice node based on the local perturbation potential energy function.

4. The dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to claim 3 is characterized in that: The risk marking module includes: Get the perturbation potential energy values ​​of lattice nodes and neighboring nodes; The spatial heterogeneity of lattice nodes is evaluated based on the difference in perturbation potential energy values, and the maximum energy jump between a lattice node and its neighboring nodes is calculated; Compare the spatial heterogeneity measurement value and the maximum energy jump amplitude of the node with the preset threshold to screen out the abnormal disturbance candidate nodes; Perform connectivity analysis on candidate nodes and divide adjacent candidate nodes into multiple clusters; Each cluster is judged based on the number of nodes in the cluster and the average value of spatial heterogeneity within the cluster, and all nodes in the cluster that meet the preset conditions are marked as high-risk nodes for disturbance.

5. The dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to claim 2, characterized in that: Graph building blocks include: Obtain the marked high-risk disturbance nodes and their three-dimensional spatial locations, and obtain pairs of high-risk nodes that are neighbors of each other; Calculate the perturbation migration rate of each high-risk node pair based on the node potential energy value difference and spatial distance, and set the perturbation migration rate as the weight of the directed edge; A disturbance asymmetric migration graph is constructed based on a set of directed weighted edges. The path weight distribution and node centrality of the disturbance asymmetric migration graph are analyzed to generate a disturbance spatial propagation trend map.

6. The dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to claim 1, characterized in that: The impact assessment modules include: Get the values ​​of the current control variable group, including gas flow, intake air temperature and fin vibration frequency; Apply small control variable disturbances to each high-risk node along the main migration path in the perturbation asymmetric migration diagram, and record the response differences of the perturbation migration rate and node potential energy value corresponding to the changes in each control variable; Normalize the response differences respectively to obtain the sensitivity coefficient of each high-risk node corresponding to each control variable; The sensitivity coefficient of each high-risk node is mapped to the spatial position to form a control sensitivity map.

7. The dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to claim 1, characterized in that: The optimal solution module includes: Based on the sensitivity coefficients in the control sensitivity map, a disturbance control objective function is constructed to comprehensively measure the nonlinear relationship between the degree of disturbance migration rate suppression and the improvement of heat transfer efficiency. Under the constraints of the objective function, a recursive grid search or evolutionary algorithm is used to perform global optimization on the control variable group to find the optimal parameter set of gas flow, intake temperature and fin vibration frequency. A fine local search correction is performed on the optimal parameter set to eliminate the error caused by the discretization search and obtain the optimal combination of gas flow, intake temperature and fin vibration frequency for real-time control.

8. The dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to claim 1, characterized in that: The real-time adjustment module includes: Receive the optimized combination parameters of gas flow, intake temperature and fin vibration frequency output by the optimal solution module; Generate actuator control instructions based on optimized combination parameters and send adjustment signals to the gas regulating valve and fin vibration actuator; Real-time monitoring of gas flow, intake temperature, and fin vibration response feedback data, and comparison with optimized combination parameters; If the feedback data deviates from the optimized combination parameters, the control instructions are fine-tuned based on the preset compensation strategy.

9. The dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to claim 1, characterized in that: The mode determination module includes: During operation, a disturbance state memory stack is continuously built to record the control variable setting values, actuator response data, and corresponding disturbance potential energy distribution characteristics at each time point; Extract time series change characteristics based on the evolution trend of historical disturbance potential energy in the memory stack; Input the time series change characteristics into the preset mode determination model to determine the optimal operating mode category corresponding to the current disturbance dynamics, including enhanced mode, energy-saving mode or steady-state mode; According to the judgment results, mode switching instructions are sent to the real-time adjustment module and the optimal solution module to dynamically adjust the control strategy to achieve adaptive operation under different disturbance potentials.

10. The dynamically adjusted gas boiler corrugated fin heat exchanger optimization control system according to claim 9, characterized in that: The time series change characteristics include potential energy peak frequency, trend slope and duration of continuous deviation; Identify the consecutive peak positions in the sequence of the perturbation potential energy memory stack, calculate the interval time between adjacent peaks, and set it as the potential energy peak frequency; Perform least squares straight line fitting based on the time series of disturbance potential energy changes, obtain the slope of the fitted line, and set it as the trend slope; During the period of time when the disturbance potential energy continuously deviates from the preset baseline and remains above the preset threshold, the duration of the continuous deviation is cumulatively calculated, and the deviation stability is quantified based on the duration of the continuous deviation.

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